**Part 3**

**Material Science** 

310 Scanning Electron Microscopy

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van Asbeck, E.C.; Clemins, K.V. & Stevens, D.A. (2009) *Candida parapsilosis*: a review of its

Yang, Y.L.; Ho, Y.A.; Cheng, H.H.; Ho, M. & Lo, H.J. (2004). Susceptibilities of *Candida*

Zucchi, P.C.; Davis, T.R. & Kumamoto, C.A. (2010). A *Candida albicans* cell wall-linked

*Microbiology*, Vol.76, No.3 (May 2010), pp. 733-748, ISSN 1365-2958.

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283–309, ISSN 1040-841X.

2004), pp. 60-64, ISSN 0899-823X.

Multiple *Candida* strains in the course of a single systemic infection. *Journal of Clinical Microbiology*, Vol.26, No. 8 (August, 1988), pp. 1448-1459, ISSN 0095-1137. Trofa, D.; Gácser, A. & Nosanchuk, J.D. (2008). *Candida parapsilosis*: an emerging fungal

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epidemiology, pathogenesis, clinical aspects, typing and antimicrobial susceptibility. *Critical Review in Microbiology*, Vol.35, No.4 (November, 2009), pp.

species to amphotericin B and fluconazole: the emergence of fluconazole resistance in *Candida tropicalis*. *Infection Control Hospital Epidemiology*, Vol.25, No.1 (January,

protein promotes invasive filamentation into semi-solid medium. *Molecular* 

**16** 

*Brazil* 

**Multimodal Microscopy for Ore Characterization** 

The recent developments in electronics and computing have brought a radical change to the microstructural characterization of materials. The integration of digital image acquisition and digital image analysis with microscope automation methods is giving rise to a rich set

Modern microscopes of all kinds (optical, electron, scanning probe) are controlled by software and have digital image acquisition. This setup allows many integrated tasks to be run under the control of automation routines like, for instance, specimen scanning and automatic focusing. Additionally, some microscopes can be fully automated. Thus, it is possible to integrate specimen scanning, image acquisition and storage, processing, analysis

Besides the automation of routine tasks in the microscopes, Digital Microscopy really opens new possibilities for microstructural characterization. In this context, multimodal microscopy emerges as a promising trend. Multimodal microscopy aims at combining complementary types of information from a given sample in order to build a multidimensional data set. It generates multi-component images combining layers obtained from different microscopy modalities, or from the same microscope in diverse conditions. For instance, multimodal microscopy may consider different signals in scanning electron microscopy (SEM) and different contrast modes in optical microscopy. Sometimes, multimodal microscopy is also referred as co-site, correlative or collaborative microscopy. The key step of a multimodal microscopy methodology is the registration between images from a given field and/or set of fields. Image registration is the process of overlaying two or more images of the same scene taken at different conditions or by different sensors. Actually, registration is a crucial procedure in all image analysis tasks in which the final information is obtained from the combination of various data sources. Typically, registration is required to combine or compare images in remote sensing and medical imaging

Once the multimodal set of images is acquired and registered, image segmentation can be employed to discriminate phases, regions or objects of interest. Due to the nature of this problem, multidimensional pattern recognition techniques arise as potential methods for image segmentation. Then, after segmentation, one is able to measure size, shape, intensity,

of techniques in the new field of Digital Microscopy (Paciornik & Maurício, 2004).

**1. Introduction** 

applications.

and report generation in a single routine.

Otávio da Fonseca Martins Gomes and Sidnei Paciornik

*Dept. of Materials Engineering, Catholic University of Rio de Janeiro,* 

*Centre for Mineral Technology – CETEM,* 

## **Multimodal Microscopy for Ore Characterization**

Otávio da Fonseca Martins Gomes and Sidnei Paciornik

*Centre for Mineral Technology – CETEM, Dept. of Materials Engineering, Catholic University of Rio de Janeiro, Brazil* 

## **1. Introduction**

The recent developments in electronics and computing have brought a radical change to the microstructural characterization of materials. The integration of digital image acquisition and digital image analysis with microscope automation methods is giving rise to a rich set of techniques in the new field of Digital Microscopy (Paciornik & Maurício, 2004).

Modern microscopes of all kinds (optical, electron, scanning probe) are controlled by software and have digital image acquisition. This setup allows many integrated tasks to be run under the control of automation routines like, for instance, specimen scanning and automatic focusing. Additionally, some microscopes can be fully automated. Thus, it is possible to integrate specimen scanning, image acquisition and storage, processing, analysis and report generation in a single routine.

Besides the automation of routine tasks in the microscopes, Digital Microscopy really opens new possibilities for microstructural characterization. In this context, multimodal microscopy emerges as a promising trend. Multimodal microscopy aims at combining complementary types of information from a given sample in order to build a multidimensional data set. It generates multi-component images combining layers obtained from different microscopy modalities, or from the same microscope in diverse conditions. For instance, multimodal microscopy may consider different signals in scanning electron microscopy (SEM) and different contrast modes in optical microscopy. Sometimes, multimodal microscopy is also referred as co-site, correlative or collaborative microscopy.

The key step of a multimodal microscopy methodology is the registration between images from a given field and/or set of fields. Image registration is the process of overlaying two or more images of the same scene taken at different conditions or by different sensors. Actually, registration is a crucial procedure in all image analysis tasks in which the final information is obtained from the combination of various data sources. Typically, registration is required to combine or compare images in remote sensing and medical imaging applications.

Once the multimodal set of images is acquired and registered, image segmentation can be employed to discriminate phases, regions or objects of interest. Due to the nature of this problem, multidimensional pattern recognition techniques arise as potential methods for image segmentation. Then, after segmentation, one is able to measure size, shape, intensity,

Multimodal Microscopy for Ore Characterization 315

or grey levels that are proportional to their average atomic numbers. Figure 1 shows a BSE image of iron ore in which four phases can be recognized: the embedding resin (the black background), quartz (dark grey), goethite (the grey particle at centre), and hematite (white). Table 1 presents chemical formula, colour on RLM, and average atomic number of epoxy

epoxy

Fig. 1. BSE image of iron ore: the embedding resin (the black background), quartz (dark

Based on BSE and EDS techniques, some automated systems for ore characterization were developed and commercially launched (Petruk, 1988; Sutherland & Gottlieb, 1991; Gu, 2003). These systems are SEM's especially dedicated to quantitative mineral analysis. They can identify minerals using BSE and EDS signals, and perform quantification routines through integrated image analysis software. Their capabilities may include particle-by-particle analysis, mineral phase classification and quantification, and mineral liberation analysis. Therefore, they became dominant for ore characterization, both in academy and industry,

**200 µm**

Nevertheless, in recent years, there was a growing use of optical microscopy applied to ore characterization. Basically, three facts contributed to this trend: better optics, better digital image acquisition devices (Pirard et al., 1999), and the advent of Digital Microscopy. The progress in microscope optics, mainly due to infinity correct tubes and new advanced objective lenses, provided images with reduced spherical aberration and free of colour distortions (Davidson & Abramowitz, 1999), which are more suitable to image analysis and

The colour has always been one of the most important properties used for mineral identification under a microscope (Piller, 1966). Moreover, there are some relevant minerals that are not distinguishable in the SEM, but can be discriminated through their colours in the reflected light microscope, such as, for instance, hematite and magnetite, which are the major iron ore minerals. Hematite and magnetite have similar average atomic numbers, respectively 20.59 and 21.02, and consequently show similar grey levels in BSE images, preventing their discrimination. The segmentation of hematite and magnetite in such kind of images requires a strong image contrast. However, this contrast condition avoids the segmentation of other present phases. In practice, not even SEM-based systems for

grey), goethite (the grey particle at centre), and hematite (white).

due to their enormous analytical capacity and relative simplicity of use.

consequently to quantitative microscopy.

resin and some minerals.

quartz

hematite

goethite

and position parameters, leading to the possibility of automatic characterization of microstructural features.

The present chapter presents a multimodal methodology that combines images obtained by reflected light microscopy (RLM) and SEM. The so-called RLM-SEM co-site microscopy (Gomes & Paciornik, 2008a, 2008b) was developed to solve some ore microscopy problems that cannot be solved by either RLM or SEM.

## **2. Ore microscopy**

Ore microscopy is an essential tool for ore characterization. It was generally employed in its various modalities (stereoscopic, transmitted and reflected light, SEM, etc.) for mineral identification and quantification, and in the determination of mineral texture and liberation analysis. In certain conditions, ore microscopy is the single approach to access this kind of information. In the mining industry, it is extensively used to provide parameters to the Geometallurgy procedures for exploration, production planning, and processing plant design and optimization purposes.

Transmitted and reflected light microscopy, respectively for transparent and opaque minerals, are probably the most traditional techniques of mineralogical identification. During the last two centuries, diverse analytical methods based on various properties of minerals were developed and refined. Referring to reflected light microscopy, it is worth to mention properties such as reflectivity, colour, reflection pleochroism, internal reflections, hardness, preferential polishing, chemical reactivity, crystalline habit, and crystalline texture, among others. There are some classical text-books that cover both theoretical and practical aspects of ore microscopy such as Galopin & Henry (1972), Gribble & Hall (1992), Criddle & Stanley (1993), and Craig & Vaughan (1994).

However, these traditional methods generally require an expert mineralogist and only few of them can be applied in automated systems. Thus, optical microscopy was being left aside in favour of SEM in ore characterization methodologies. In fact, in the last decades, research and development of microscopy in Applied Mineralogy field were focused on SEM.

The SEM is a very versatile analytical instrument. It builds images through synchronization of the electron beam scanning and one of the many signals that come from the interaction between the electron beam and the specimen. Thus, the pixels present intensities proportional to the signal measured by one of the SEM detectors such as, for instance, backscattered electrons (BSE) or secondary electrons (SE) detectors. If the SEM has a coupled energy dispersive X-ray spectrometer (EDS), it becomes even more versatile, and can also perform elemental chemical analysis with a resolution down to approximately 1 µm on the surface. This is the great advantage of SEM – a large variety of electron-specimen interactions can be used to form images and to provide information with different physical meanings (Reimer, 1998; Goldstein et al., 2003).

The most used signal for ore characterization is BSE that can furnish topography information and atomic number contrast. Nevertheless, if the specimen is plane, each pixel is proportional to the average atomic number of its corresponding region on the specimen surface (Jones, 1987). Therefore, BSE images of polished samples are indirectly compositional images, in which mineral phases can be correlated to characteristic intensities

and position parameters, leading to the possibility of automatic characterization of

The present chapter presents a multimodal methodology that combines images obtained by reflected light microscopy (RLM) and SEM. The so-called RLM-SEM co-site microscopy (Gomes & Paciornik, 2008a, 2008b) was developed to solve some ore microscopy problems

Ore microscopy is an essential tool for ore characterization. It was generally employed in its various modalities (stereoscopic, transmitted and reflected light, SEM, etc.) for mineral identification and quantification, and in the determination of mineral texture and liberation analysis. In certain conditions, ore microscopy is the single approach to access this kind of information. In the mining industry, it is extensively used to provide parameters to the Geometallurgy procedures for exploration, production planning, and processing plant

Transmitted and reflected light microscopy, respectively for transparent and opaque minerals, are probably the most traditional techniques of mineralogical identification. During the last two centuries, diverse analytical methods based on various properties of minerals were developed and refined. Referring to reflected light microscopy, it is worth to mention properties such as reflectivity, colour, reflection pleochroism, internal reflections, hardness, preferential polishing, chemical reactivity, crystalline habit, and crystalline texture, among others. There are some classical text-books that cover both theoretical and practical aspects of ore microscopy such as Galopin & Henry (1972), Gribble & Hall (1992),

However, these traditional methods generally require an expert mineralogist and only few of them can be applied in automated systems. Thus, optical microscopy was being left aside in favour of SEM in ore characterization methodologies. In fact, in the last decades, research

The SEM is a very versatile analytical instrument. It builds images through synchronization of the electron beam scanning and one of the many signals that come from the interaction between the electron beam and the specimen. Thus, the pixels present intensities proportional to the signal measured by one of the SEM detectors such as, for instance, backscattered electrons (BSE) or secondary electrons (SE) detectors. If the SEM has a coupled energy dispersive X-ray spectrometer (EDS), it becomes even more versatile, and can also perform elemental chemical analysis with a resolution down to approximately 1 µm on the surface. This is the great advantage of SEM – a large variety of electron-specimen interactions can be used to form images and to provide information with different physical

The most used signal for ore characterization is BSE that can furnish topography information and atomic number contrast. Nevertheless, if the specimen is plane, each pixel is proportional to the average atomic number of its corresponding region on the specimen surface (Jones, 1987). Therefore, BSE images of polished samples are indirectly compositional images, in which mineral phases can be correlated to characteristic intensities

and development of microscopy in Applied Mineralogy field were focused on SEM.

microstructural features.

**2. Ore microscopy** 

design and optimization purposes.

Criddle & Stanley (1993), and Craig & Vaughan (1994).

meanings (Reimer, 1998; Goldstein et al., 2003).

that cannot be solved by either RLM or SEM.

or grey levels that are proportional to their average atomic numbers. Figure 1 shows a BSE image of iron ore in which four phases can be recognized: the embedding resin (the black background), quartz (dark grey), goethite (the grey particle at centre), and hematite (white). Table 1 presents chemical formula, colour on RLM, and average atomic number of epoxy resin and some minerals.

Fig. 1. BSE image of iron ore: the embedding resin (the black background), quartz (dark grey), goethite (the grey particle at centre), and hematite (white).

Based on BSE and EDS techniques, some automated systems for ore characterization were developed and commercially launched (Petruk, 1988; Sutherland & Gottlieb, 1991; Gu, 2003). These systems are SEM's especially dedicated to quantitative mineral analysis. They can identify minerals using BSE and EDS signals, and perform quantification routines through integrated image analysis software. Their capabilities may include particle-by-particle analysis, mineral phase classification and quantification, and mineral liberation analysis. Therefore, they became dominant for ore characterization, both in academy and industry, due to their enormous analytical capacity and relative simplicity of use.

Nevertheless, in recent years, there was a growing use of optical microscopy applied to ore characterization. Basically, three facts contributed to this trend: better optics, better digital image acquisition devices (Pirard et al., 1999), and the advent of Digital Microscopy. The progress in microscope optics, mainly due to infinity correct tubes and new advanced objective lenses, provided images with reduced spherical aberration and free of colour distortions (Davidson & Abramowitz, 1999), which are more suitable to image analysis and consequently to quantitative microscopy.

The colour has always been one of the most important properties used for mineral identification under a microscope (Piller, 1966). Moreover, there are some relevant minerals that are not distinguishable in the SEM, but can be discriminated through their colours in the reflected light microscope, such as, for instance, hematite and magnetite, which are the major iron ore minerals. Hematite and magnetite have similar average atomic numbers, respectively 20.59 and 21.02, and consequently show similar grey levels in BSE images, preventing their discrimination. The segmentation of hematite and magnetite in such kind of images requires a strong image contrast. However, this contrast condition avoids the segmentation of other present phases. In practice, not even SEM-based systems for

**200 µm** 

SEM.

**3. Image registration** 

properly composed and analyzed.

Multimodal Microscopy for Ore Characterization 317

Fig. 2. Images of an iron ore sample acquired on (a) reflected light microscope and (b) SEM.

hematite magnetite goethite epoxy quartz

(a) (b)

(a) (b)

pyrite chalcopyrite pentlandite

Fig. 3. Images of a copper ore sample acquired on (a) reflected light microscope and (b)

**200 µm**

in different conditions, and the subsequent process of overlaying them.

Image registration comprises the operation to determine the correspondence point to point between two or more images of the same area (or volume) obtained by different sensors or

Image registration is a fundamental procedure in all image analysis tasks in which the final information is gained from the combination of various data sources. Only after the registration, a multi-component image that represents a multimodal database can be

automated mineralogy can discriminate hematite and magnetite, because the discrimination of these minerals is not possible through EDS due to their similar chemical composition (Gomes & Paciornik, 2008b).

On the other hand, transparent minerals and the embedding resin generally cannot be distinguished by their specular reflectances. For instance, quartz and epoxy resin have practically the same reflectance through the visible light spectrum (Neumann & Stanley, 2008). Actually, this is a classical problem in ore microscopy that renders unfeasible this kind of analysis through reflected light microscopy.


Table 1. Chemical formula, colour on RLM, and average atomic number of epoxy resin and some minerals.

Figure 2 shows a pair of images of an iron ore sample acquired by reflected light microscopy and SEM. Comparing them, one can observe that the segmentation between quartz and epoxy resin in the BSE image is easy, but it is not viable in the optical image. On the other hand, hematite and magnetite present distinct colours, respectively light grey and pinkish grey, in the optical image, but have practically the same grey level in the BSE image.

Another example of minerals of difficult discrimination can be observed in Figure 3. It shows images of the same field of a copper ore sample acquired by reflected light microscopy and SEM. In the optical image, chalcopyrite can be easily identified by its characteristic brass yellow colour, but pyrite and pentlandite present a very similar colour (pale yellow). On the other hand, in the BSE image, chalcopyrite and pentlandite are practically indistinguishable, due to their similar average atomic numbers (23.54 and 23.36, respectively). Nevertheless, pyrite is slightly darker than pentlandite, because pyrite has a lower average atomic number (20.66).

The RLM-SEM co-site microscopy was developed to overcome these challenges. This methodology can improve the SEM analytical capacity adding specular reflectance (colour) information from RLM. The methodology was employed with some mineral samples, aiming at the discrimination of phases that are not distinguishable by either RLM or SEM, but can be discriminated through the combined use of both techniques.

automated mineralogy can discriminate hematite and magnetite, because the discrimination of these minerals is not possible through EDS due to their similar chemical composition

On the other hand, transparent minerals and the embedding resin generally cannot be distinguished by their specular reflectances. For instance, quartz and epoxy resin have practically the same reflectance through the visible light spectrum (Neumann & Stanley, 2008). Actually, this is a classical problem in ore microscopy that renders unfeasible this

Phase Chemical formula Colour on RLM Average atomic

Table 1. Chemical formula, colour on RLM, and average atomic number of epoxy resin and

Figure 2 shows a pair of images of an iron ore sample acquired by reflected light microscopy and SEM. Comparing them, one can observe that the segmentation between quartz and epoxy resin in the BSE image is easy, but it is not viable in the optical image. On the other hand, hematite and magnetite present distinct colours, respectively light grey and pinkish

Another example of minerals of difficult discrimination can be observed in Figure 3. It shows images of the same field of a copper ore sample acquired by reflected light microscopy and SEM. In the optical image, chalcopyrite can be easily identified by its characteristic brass yellow colour, but pyrite and pentlandite present a very similar colour (pale yellow). On the other hand, in the BSE image, chalcopyrite and pentlandite are practically indistinguishable, due to their similar average atomic numbers (23.54 and 23.36, respectively). Nevertheless, pyrite is slightly darker than pentlandite, because pyrite has a

The RLM-SEM co-site microscopy was developed to overcome these challenges. This methodology can improve the SEM analytical capacity adding specular reflectance (colour) information from RLM. The methodology was employed with some mineral samples, aiming at the discrimination of phases that are not distinguishable by either RLM or SEM,

but can be discriminated through the combined use of both techniques.

grey, in the optical image, but have practically the same grey level in the BSE image.

Epoxy resin C21H25ClO5 Dark grey 7.90 Quartz SiO2 Dark grey 10.80 Goethite FeO.OH Grey / Brown 19.23 Hematite Fe2O3 Light grey 20.59 Pyrite FeS2 Pale yellow 20.66 Magnetite Fe3O4 Pinkish grey 21.02 Pentlandite (Fe,Ni)9S8 Pale yellow 23.36 Chalcopyrite CuFeS2 Brass yellow 23.54 Covelline CuS Blue 24.64 Bornite Cu5FeS4 Purple 25.34 Sphalerite ZnS Grey 25.39 Chalcocite Cu2S Light grey 26.38 Native copper Cu Bright yellow 29.00

number

(Gomes & Paciornik, 2008b).

some minerals.

lower average atomic number (20.66).

kind of analysis through reflected light microscopy.

Fig. 2. Images of an iron ore sample acquired on (a) reflected light microscope and (b) SEM.

Fig. 3. Images of a copper ore sample acquired on (a) reflected light microscope and (b) SEM.

## **3. Image registration**

Image registration comprises the operation to determine the correspondence point to point between two or more images of the same area (or volume) obtained by different sensors or in different conditions, and the subsequent process of overlaying them.

Image registration is a fundamental procedure in all image analysis tasks in which the final information is gained from the combination of various data sources. Only after the registration, a multi-component image that represents a multimodal database can be properly composed and analyzed.

Multimodal Microscopy for Ore Characterization 319

In this context, multimodal microscopy procedures that are performed intrinsically in a unique microscope constitute probably the simplest cases, generally involving only rigid body transformations. Sometimes it is even possible to acquire images that are directly

Multimodal microscopy methodologies on reflected light microscope can be carried out with or without specimen removal from the stage. In the first case, specimen removal, for instance, for chemical etchings like in a classical metallographic approach, generally imply some displacement between images. Therefore, translation and occasionally rotation corrections are required. Soto et al. (2004) and Paciornik & Gomes (2009) present case studies of multimodal methods that involve specimen removal for chemical etchings.

On the other hand, optical methodologies without specimen removal can be sometimes performed without translation corrections (De-Deus et al., 2007). Nevertheless, there are some exceptions. For instance, Pirard (2004) proposed a multispectral imaging technique applied to ore characterization in which shifts of the order of several pixels occur between images obtained from different wavelengths; and Iglesias et al. (2011) developed a multimodal microscopy methodology based on the combination of cross-polarized and

The SEM forms an image through scanning its electron beam in a raster across the specimen and then it synchronizes the scanning with a signal from one of its detectors. Thus, in a given field, it can acquire several different images, which are ready to compose a multicomponent image without the need for a registration procedure. However, in practice, older equipments usually exhibit some translation between images from different detectors. In this case, a translation correction is not enough to properly register the images, because SEM's generally present complex and non-linear distortions (Goldstein et al., 2003) that

In the RLM-SEM co-site microscopy, the registration procedure involves rigid and non-rigid transformations. The specimen handling between the microscopes and the different stages imply that translation and rotation adjustments are necessary. Besides, non-rigid transformations are required due to the complex distortions that occur in images from SEM.

A registration procedure consists of a sequence of mathematical operations that determine the suitable spatial transformation and then defines and applies the geometric operation that properly performs the registration. The base of a registration procedure is the kind of information used by its algorithm. Therefore, as stated in the already classical review paper of Zitova & Flusser (2003), there are two main classes of algorithms according to their

Area-based algorithms, also called template matching, estimate the correspondence between images (or parts of them) in order to determine which transformations provide the best correspondence. The correlation between two signals (cross-correlation) is a standard approach to template matching algorithms that can be particularly efficient if it is computed in the frequency domain using the fast Fourier transform (Lewis, 1995). Area-based algorithms are in general simpler than feature-based ones. They are applied mostly in cases involving only rigid and scale transformations. Besides, they are more sensitive to noise in

bright field images in which there were small misalignments between them.

Even a fine calibration of the equipment is not capable of preventing them.

registered.

must be considered.

nature: area-based and feature-based.

Typically, image registration is employed for composition and comparison of multi-spectral images in Remote Sensing (Schowengerdt, 1983). It also has several applications in Medicine, such as diagnosis, preparation of surgeries, treatment evaluation, etc. It is used, for instance, for fusion of anatomical and functional information, which are usually obtained through different medical imaging techniques (van den Elsen et al., 1993; Maintz & Viergever, 1998).

There is in the literature a wide variety of image registration methods based on different principles and employed for diverse applications (Zitova & Flusser, 2003; Goshtasby, 2005). Anyway, registration consists in the determination and implementation of a geometric operation (spatial transformation) between images in order to correlate the spatial coordinates of both images. Therefore, the fundamental aspect of any registration method is the spatial transformation used to correctly overlay images. Although many types of variations may be present in images, a suitable transformation must remove only spatial distortions between them (Brown, 1992). Other differences, due to the diversity of information that each image represents, must be maintained, since these are the interesting characteristics that one aims to expose.

In fact, image registration is more complex than a simple image alignment. It is not limited to translation and rotation of images. It may be composed of a combination of six distinct basic transformations: translation, rotation, scale, shear, projection, and other non-linear and local distortions. Figure 4 presents the six basic transformations, showing their effects in a sample base image.

Fig. 4. Basic spatial transformations.

Spatial transformations can convert the coordinates of the sensed image to the coordinates of the base or reference image. Thus, they correlate these digital images pixel by pixel, allowing the assemblage of a multi-component image.

The transformations that involve only translation and rotation are generally called rigid body or Euclidean transformations, since the Euclidean distances within images are preserved (Szeliski, 2004). In contrast, the other ones are classified as non-rigid or elastic. Although this nomenclature is the most commonly found in the literature, including the present text, it is not a consensus. Some authors consider scale as a rigid body transformation too, and there are still others that also include shear and projection in the class of rigid transformations (Crum, et al., 2004).

Typically, image registration is employed for composition and comparison of multi-spectral images in Remote Sensing (Schowengerdt, 1983). It also has several applications in Medicine, such as diagnosis, preparation of surgeries, treatment evaluation, etc. It is used, for instance, for fusion of anatomical and functional information, which are usually obtained through different medical imaging techniques (van den Elsen et al., 1993; Maintz &

There is in the literature a wide variety of image registration methods based on different principles and employed for diverse applications (Zitova & Flusser, 2003; Goshtasby, 2005). Anyway, registration consists in the determination and implementation of a geometric operation (spatial transformation) between images in order to correlate the spatial coordinates of both images. Therefore, the fundamental aspect of any registration method is the spatial transformation used to correctly overlay images. Although many types of variations may be present in images, a suitable transformation must remove only spatial distortions between them (Brown, 1992). Other differences, due to the diversity of information that each image represents, must be maintained, since these are the interesting

In fact, image registration is more complex than a simple image alignment. It is not limited to translation and rotation of images. It may be composed of a combination of six distinct basic transformations: translation, rotation, scale, shear, projection, and other non-linear and local distortions. Figure 4 presents the six basic transformations, showing their effects in a

Spatial transformations can convert the coordinates of the sensed image to the coordinates of the base or reference image. Thus, they correlate these digital images pixel by pixel,

The transformations that involve only translation and rotation are generally called rigid body or Euclidean transformations, since the Euclidean distances within images are preserved (Szeliski, 2004). In contrast, the other ones are classified as non-rigid or elastic. Although this nomenclature is the most commonly found in the literature, including the present text, it is not a consensus. Some authors consider scale as a rigid body transformation too, and there are still others that also include shear and projection in the

Viergever, 1998).

sample base image.

characteristics that one aims to expose.

Fig. 4. Basic spatial transformations.

allowing the assemblage of a multi-component image.

class of rigid transformations (Crum, et al., 2004).

In this context, multimodal microscopy procedures that are performed intrinsically in a unique microscope constitute probably the simplest cases, generally involving only rigid body transformations. Sometimes it is even possible to acquire images that are directly registered.

Multimodal microscopy methodologies on reflected light microscope can be carried out with or without specimen removal from the stage. In the first case, specimen removal, for instance, for chemical etchings like in a classical metallographic approach, generally imply some displacement between images. Therefore, translation and occasionally rotation corrections are required. Soto et al. (2004) and Paciornik & Gomes (2009) present case studies of multimodal methods that involve specimen removal for chemical etchings.

On the other hand, optical methodologies without specimen removal can be sometimes performed without translation corrections (De-Deus et al., 2007). Nevertheless, there are some exceptions. For instance, Pirard (2004) proposed a multispectral imaging technique applied to ore characterization in which shifts of the order of several pixels occur between images obtained from different wavelengths; and Iglesias et al. (2011) developed a multimodal microscopy methodology based on the combination of cross-polarized and bright field images in which there were small misalignments between them.

The SEM forms an image through scanning its electron beam in a raster across the specimen and then it synchronizes the scanning with a signal from one of its detectors. Thus, in a given field, it can acquire several different images, which are ready to compose a multicomponent image without the need for a registration procedure. However, in practice, older equipments usually exhibit some translation between images from different detectors. In this case, a translation correction is not enough to properly register the images, because SEM's generally present complex and non-linear distortions (Goldstein et al., 2003) that must be considered.

In the RLM-SEM co-site microscopy, the registration procedure involves rigid and non-rigid transformations. The specimen handling between the microscopes and the different stages imply that translation and rotation adjustments are necessary. Besides, non-rigid transformations are required due to the complex distortions that occur in images from SEM. Even a fine calibration of the equipment is not capable of preventing them.

A registration procedure consists of a sequence of mathematical operations that determine the suitable spatial transformation and then defines and applies the geometric operation that properly performs the registration. The base of a registration procedure is the kind of information used by its algorithm. Therefore, as stated in the already classical review paper of Zitova & Flusser (2003), there are two main classes of algorithms according to their nature: area-based and feature-based.

Area-based algorithms, also called template matching, estimate the correspondence between images (or parts of them) in order to determine which transformations provide the best correspondence. The correlation between two signals (cross-correlation) is a standard approach to template matching algorithms that can be particularly efficient if it is computed in the frequency domain using the fast Fourier transform (Lewis, 1995). Area-based algorithms are in general simpler than feature-based ones. They are applied mostly in cases involving only rigid and scale transformations. Besides, they are more sensitive to noise in

Multimodal Microscopy for Ore Characterization 321

computationally a cognitive process that is inherent to the human vision. When we look at an image we use many different inputs to distinguish the objects: brightness, boundaries, specific shapes or textures. Our brains process this information in parallel at high speed, using previous experience. Computers, on the other hand, do not have the same associative power. The recognition of objects in an image is made through the classification of each

There are many segmentation methods based on different principles such as thresholding, edge detection, texture analysis, mathematical morphology, etc. Each one is generally more suitable for a specific application. Categorically, there is not an ideal generic method that is always the best one. Some classical references in this area are: Haralick et al. (1973), Otsu (1979), Haralick (1979), Beucher & Lantuéjoul (1979), Marr & Hildreth (1980), Pun (1981),

The most common segmentation method is thresholding. It is based on the assumption that pixels pertaining to a given class of objects (e.g. a specific mineral phase) have similar colour or greyscale intensity, and this colour is different from the background and from other classes of objects. In other words, there must be sufficient contrast between different phases in the material. If that is the case, then the segmentation is based on selecting

Noise, uneven illumination, edge effects contribute to degrade the discrimination between phases, and that is why a pre-processing step may be so relevant. Evidently, phase contrast maybe too low, depending on the microscope used, as described before, and that is where

In many situations the results of segmentation contain artefacts, such as spurious objects, touching or partially overlapping objects, etc. A very common artefact in mineralogical images is segmenting a phase together with the edges of a different phase that share the same intensity range. Some of these defects can be minimized with an appropriate preprocessing step, such as delineation (edge enhancement), but many must be corrected after

Post-processing makes intense use of morphological operators such as erosion, dilation, opening, closing, and more sophisticated functions like the watershed separation method for touching objects (Serra, 1982, 1988). Ideally, the final result is an image in which just the relevant objects are present and separated in groups that correspond to each phase present in the sample. However, as described below, this is rarely the case, and further analysis of

Given a segmented post-processed image containing a set of objects, several measurements are available. Field features such as number of objects and area fraction are some of the simplest ones. Object specific features are more sophisticated and include measurements of size, shape, position, intensity and texture of each object in the image (Friel, 2000). These

Common colour images, generated by either scientific or general-purpose digital cameras, are generally 24-bit RGB images. Actually, they consist of multi-component images

pixel of the image as pertaining or not to an object.

Canny (1986), and Adams & Bischof (1994).

colour/intensity thresholds that represent the various phases.

segmentation, in the so-called post-processing step.

features are critical for the classification step.

**4.1 Multi-component image analysis** 

combining information from different types of signals becomes critical.

the segmented objects must be done to complete the discrimination.

images. For instance, the multimodal methodologies presented by Soto et al. (2004), Paciornik & Gomes (2009), and Iglesias et al. (2011) employed cross-correlation in the frequency domain for the registration of their optical images.

Feature-based algorithms consist of four steps: feature detection, feature matching, mapping function design, and image transformation and resampling (Zitova & Flusser, 2003). Two sets of features, which are salient and distinctive objects such as corners, line intersections, edges, etc., are manually or automatically detected in both base and sensed images. These features are represented by the so-called control points (points themselves, centers of gravity, line endings, etc.). The aim is to find the pairwise correspondence between control points and then to map a suitable transformation from them. Therefore, the sensed image is transformed through the determined mapping function and an appropriate interpolation technique is employed in order to treat non-integer coordinates.

The detection of control points and the determination of their correspondence in base and sensed images are crucial and difficult tasks. The method named Scale Invariant Feature Transform (SIFT), proposed by Lowe (2004), has been shown computationally efficient and robust upon diverse distortions and multisensor cases.

In contrast to the area-based methods, the feature-based ones do not work directly with image intensity values. Control points constitute higher level information. This fact makes feature-based methods suitable for applications in which diverse sensors with different data structures and physical meanings are involved. Besides, it allows registering images with any nature of distortions, including non-linear and local ones (Zitova & Flusser, 2003).

Furthermore, in multimodal microscopy methodologies, an alternative approach can facilitate the determination of control points. By introducing indentation marks in the sample through a microdurimeter, the control points can be properly defined as their centers of gravity. In fact, this method can be useful for the registration of one or few fields in which specific microstructural features are of interest. However, it becomes impractical when the number of fields is large, as usually occurs in ore characterization procedures.

The RLM-SEM co-site microscopy methodology employs a feature-based method for registration that is described in the section 5.3.

### **4. Image processing and analysis**

A typical image processing and analysis sequence comprises the steps of image acquisition, digitization, pre-processing, segmentation, post-processing, feature extraction and classification (Gonzalez & Woods, 2007).

Pre-processing, or image enhancement, is the first step after image digitization and is used to correct basic image defects, normally created during the image acquisition step. Typical operations, at this step, are background correction, edge enhancement and noise reduction. Pre-processing is useful for qualitative reasons, as it improves the visibility of relevant features in the image, but it is even more important to prepare the image for the following step of segmentation.

Segmentation is the technical term used for the discrimination of objects in an image. Segmentation is probably the most complex step in the sequence because it tries to represent

images. For instance, the multimodal methodologies presented by Soto et al. (2004), Paciornik & Gomes (2009), and Iglesias et al. (2011) employed cross-correlation in the

Feature-based algorithms consist of four steps: feature detection, feature matching, mapping function design, and image transformation and resampling (Zitova & Flusser, 2003). Two sets of features, which are salient and distinctive objects such as corners, line intersections, edges, etc., are manually or automatically detected in both base and sensed images. These features are represented by the so-called control points (points themselves, centers of gravity, line endings, etc.). The aim is to find the pairwise correspondence between control points and then to map a suitable transformation from them. Therefore, the sensed image is transformed through the determined mapping function and an appropriate interpolation

The detection of control points and the determination of their correspondence in base and sensed images are crucial and difficult tasks. The method named Scale Invariant Feature Transform (SIFT), proposed by Lowe (2004), has been shown computationally efficient and

In contrast to the area-based methods, the feature-based ones do not work directly with image intensity values. Control points constitute higher level information. This fact makes feature-based methods suitable for applications in which diverse sensors with different data structures and physical meanings are involved. Besides, it allows registering images with any nature of distortions, including non-linear and local ones (Zitova & Flusser, 2003).

Furthermore, in multimodal microscopy methodologies, an alternative approach can facilitate the determination of control points. By introducing indentation marks in the sample through a microdurimeter, the control points can be properly defined as their centers of gravity. In fact, this method can be useful for the registration of one or few fields in which specific microstructural features are of interest. However, it becomes impractical when the number of fields is large, as usually occurs in ore characterization procedures.

The RLM-SEM co-site microscopy methodology employs a feature-based method for

A typical image processing and analysis sequence comprises the steps of image acquisition, digitization, pre-processing, segmentation, post-processing, feature extraction and

Pre-processing, or image enhancement, is the first step after image digitization and is used to correct basic image defects, normally created during the image acquisition step. Typical operations, at this step, are background correction, edge enhancement and noise reduction. Pre-processing is useful for qualitative reasons, as it improves the visibility of relevant features in the image, but it is even more important to prepare the image for the following

Segmentation is the technical term used for the discrimination of objects in an image. Segmentation is probably the most complex step in the sequence because it tries to represent

frequency domain for the registration of their optical images.

technique is employed in order to treat non-integer coordinates.

robust upon diverse distortions and multisensor cases.

registration that is described in the section 5.3.

**4. Image processing and analysis** 

classification (Gonzalez & Woods, 2007).

step of segmentation.

computationally a cognitive process that is inherent to the human vision. When we look at an image we use many different inputs to distinguish the objects: brightness, boundaries, specific shapes or textures. Our brains process this information in parallel at high speed, using previous experience. Computers, on the other hand, do not have the same associative power. The recognition of objects in an image is made through the classification of each pixel of the image as pertaining or not to an object.

There are many segmentation methods based on different principles such as thresholding, edge detection, texture analysis, mathematical morphology, etc. Each one is generally more suitable for a specific application. Categorically, there is not an ideal generic method that is always the best one. Some classical references in this area are: Haralick et al. (1973), Otsu (1979), Haralick (1979), Beucher & Lantuéjoul (1979), Marr & Hildreth (1980), Pun (1981), Canny (1986), and Adams & Bischof (1994).

The most common segmentation method is thresholding. It is based on the assumption that pixels pertaining to a given class of objects (e.g. a specific mineral phase) have similar colour or greyscale intensity, and this colour is different from the background and from other classes of objects. In other words, there must be sufficient contrast between different phases in the material. If that is the case, then the segmentation is based on selecting colour/intensity thresholds that represent the various phases.

Noise, uneven illumination, edge effects contribute to degrade the discrimination between phases, and that is why a pre-processing step may be so relevant. Evidently, phase contrast maybe too low, depending on the microscope used, as described before, and that is where combining information from different types of signals becomes critical.

In many situations the results of segmentation contain artefacts, such as spurious objects, touching or partially overlapping objects, etc. A very common artefact in mineralogical images is segmenting a phase together with the edges of a different phase that share the same intensity range. Some of these defects can be minimized with an appropriate preprocessing step, such as delineation (edge enhancement), but many must be corrected after segmentation, in the so-called post-processing step.

Post-processing makes intense use of morphological operators such as erosion, dilation, opening, closing, and more sophisticated functions like the watershed separation method for touching objects (Serra, 1982, 1988). Ideally, the final result is an image in which just the relevant objects are present and separated in groups that correspond to each phase present in the sample. However, as described below, this is rarely the case, and further analysis of the segmented objects must be done to complete the discrimination.

Given a segmented post-processed image containing a set of objects, several measurements are available. Field features such as number of objects and area fraction are some of the simplest ones. Object specific features are more sophisticated and include measurements of size, shape, position, intensity and texture of each object in the image (Friel, 2000). These features are critical for the classification step.

#### **4.1 Multi-component image analysis**

Common colour images, generated by either scientific or general-purpose digital cameras, are generally 24-bit RGB images. Actually, they consist of multi-component images

Multimodal Microscopy for Ore Characterization 323

components involve decision boundaries that are planes; and so on. Nevertheless, there are more sophisticated segmentation methods that offer more complex decision boundaries, such as curves, surfaces, etc. Besides, they can also be discontinuous. In this context, multidimensional pattern recognition techniques arise as potential methods for

Pattern recognition is the scientific discipline whose goal is the classification of objects (patterns) into a number of classes from the observation of their characteristics (Theodoridis & Koutroumbas, 2003). It aims to build a simpler representation of a data set through its more relevant features in order to perform its partition into classes (Duda et al., 2001).

%

Pattern recognition techniques can be used to classify objects (pixels, regions, etc.) within images. Any part of image that has at least one measurable property can be considered as an object and consequently it can be classified. In multi-component images, a pixel consists of a

The classification of pixels is actually an image segmentation procedure. Each class of pixels can properly represent a phase or mineral in an ore microscopy case. If the phases of interest are known, this problem becomes supervised (Duda et al., 2001). In a supervised classification procedure, this known information is exploited so that the classification

A supervised classification procedure involves three stages: training, validation and classification. The training stage comprises sampling of known pixels of each class in order to compose the so-called training set that is used as knowledge base. Therefore, the classifier is trained, i.e., it is designed based on the known information. Following, in the validation stage, another known set of pixels, the validation set, is classified aiming to estimate the performance of the classifier, considering its generalization capacity (Toussaint, 1974). If the validation stage indicates that the training was successful, the classification is then possible

It is worth to mention that although the RGB system is vastly predominant in image acquisition devices, it is not generally the most appropriate colour representation system for classification purposes because its three components (R, G and B) are very correlated, due to

segmentation of multi-component images.

Fig. 6. A RGB image and its bi-dimensional RG histogram.

vector in which the elements represent its values in the components.

system learns how the different classes can be recognized.

and consequently the segmentation procedure is implemented.

composed by three images that represent the primary colours (red, green and blue) with 8 bit quantization each (Orchard & Bouman, 1991). The RGB system is the most common colour representation system employed by cameras and displays. It was developed to be similar with the *tri-stimulus* response of human vision. Figure 5 shows a sample RGB image above its three components, respectively, R, G and B. In this figure, one can observe some samples of how the primary colours are mixed to compose other colours and grey levels.

Fig. 5. A RGB image and its components.

The components of a multi-component image can represent information of any source and physical meaning. It is not necessary that the data within components are correlated. They just must have spatial correspondence pixel-to-pixel, i.e., they must be registered.

Multi-component images with up to three components have the advantage that they can be viewed as RGB colour images in standard image viewer software. In this case, the so-called pseudo-colours denote the properties which are represented in the components. Pseudocolour images constitute a useful approach for data visualization.

Each component image can be singly processed and analyzed as a common intensity or grey level image. However, this processing should be carefully performed so that the spatial relations within images are not undesirably modified. Geometric operations must be especially avoided.

A multi-component image can also be conceived as a matrix in which each element is a vector, not a scalar value. Each vector represents a pixel, and each element of vector is the value of this pixel in one of the component images. In a RGB image, each pixel consists of a three-element vector that represents the intensity of the three primary colours. Therefore, in a multi-component image, the probability density function becomes multivariate and consequently its histogram of intensities becomes multidimensional. Figure 6 shows a RGB image of a copper ore sample in the reflected light microscope and its bi-dimensional RG histogram.

Image segmentation by thresholding can be generalized to an n-dimension problem. In this case, a threshold becomes a decision boundary, whose form depends on the number of components. One component leads to decision boundaries that are scalar values (thresholds); two components imply that decision boundaries are straight lines; three

composed by three images that represent the primary colours (red, green and blue) with 8 bit quantization each (Orchard & Bouman, 1991). The RGB system is the most common colour representation system employed by cameras and displays. It was developed to be similar with the *tri-stimulus* response of human vision. Figure 5 shows a sample RGB image above its three components, respectively, R, G and B. In this figure, one can observe some samples of how the primary colours are mixed to compose other colours and grey levels.

The components of a multi-component image can represent information of any source and physical meaning. It is not necessary that the data within components are correlated. They

Multi-component images with up to three components have the advantage that they can be viewed as RGB colour images in standard image viewer software. In this case, the so-called pseudo-colours denote the properties which are represented in the components. Pseudo-

Each component image can be singly processed and analyzed as a common intensity or grey level image. However, this processing should be carefully performed so that the spatial relations within images are not undesirably modified. Geometric operations must be

A multi-component image can also be conceived as a matrix in which each element is a vector, not a scalar value. Each vector represents a pixel, and each element of vector is the value of this pixel in one of the component images. In a RGB image, each pixel consists of a three-element vector that represents the intensity of the three primary colours. Therefore, in a multi-component image, the probability density function becomes multivariate and consequently its histogram of intensities becomes multidimensional. Figure 6 shows a RGB image of a copper ore sample in the reflected light microscope and its bi-dimensional RG

Image segmentation by thresholding can be generalized to an n-dimension problem. In this case, a threshold becomes a decision boundary, whose form depends on the number of components. One component leads to decision boundaries that are scalar values (thresholds); two components imply that decision boundaries are straight lines; three

just must have spatial correspondence pixel-to-pixel, i.e., they must be registered.

colour images constitute a useful approach for data visualization.

Fig. 5. A RGB image and its components.

especially avoided.

histogram.

components involve decision boundaries that are planes; and so on. Nevertheless, there are more sophisticated segmentation methods that offer more complex decision boundaries, such as curves, surfaces, etc. Besides, they can also be discontinuous. In this context, multidimensional pattern recognition techniques arise as potential methods for segmentation of multi-component images.

Fig. 6. A RGB image and its bi-dimensional RG histogram.

Pattern recognition is the scientific discipline whose goal is the classification of objects (patterns) into a number of classes from the observation of their characteristics (Theodoridis & Koutroumbas, 2003). It aims to build a simpler representation of a data set through its more relevant features in order to perform its partition into classes (Duda et al., 2001).

Pattern recognition techniques can be used to classify objects (pixels, regions, etc.) within images. Any part of image that has at least one measurable property can be considered as an object and consequently it can be classified. In multi-component images, a pixel consists of a vector in which the elements represent its values in the components.

The classification of pixels is actually an image segmentation procedure. Each class of pixels can properly represent a phase or mineral in an ore microscopy case. If the phases of interest are known, this problem becomes supervised (Duda et al., 2001). In a supervised classification procedure, this known information is exploited so that the classification system learns how the different classes can be recognized.

A supervised classification procedure involves three stages: training, validation and classification. The training stage comprises sampling of known pixels of each class in order to compose the so-called training set that is used as knowledge base. Therefore, the classifier is trained, i.e., it is designed based on the known information. Following, in the validation stage, another known set of pixels, the validation set, is classified aiming to estimate the performance of the classifier, considering its generalization capacity (Toussaint, 1974). If the validation stage indicates that the training was successful, the classification is then possible and consequently the segmentation procedure is implemented.

It is worth to mention that although the RGB system is vastly predominant in image acquisition devices, it is not generally the most appropriate colour representation system for classification purposes because its three components (R, G and B) are very correlated, due to

Multimodal Microscopy for Ore Characterization 325

A digital SEM was used to acquire a BSE image (1024 x 768 pixels) of each field imaged on RLM. In this procedure, the sample must be placed in the SEM stage at a similar arrangement as positioned in the RLM stage. It is unnecessary and impractical to place the sample in the exact same way, but a similar arrangement can make image registration easier and faster.

The magnification of the SEM was set to keep similar resolutions to optical images. Other SEM operational parameters were manually tuned for a representative field of the sample and then kept constant. After that, the field positions database was loaded with the acquisition routine developed in the SEM control software. It converts RLM stage coordinates to SEM stage coordinates and subsequently performs automatic specimen scanning and image acquisition. Thus, respectively for copper ore and iron ore samples, 121 (11 x 11) and 81 (9 x 9) fields per cross-section were imaged with the RLM and the SEM.

Figure 7 presents a field of the copper ore sample as imaged on RLM and SEM.

(a) (b)

Fig. 7. Images of a field of the copper ore sample obtained by (a) RLM and (b) SEM.

An automatic method for the registration of RLM and SEM images was developed. The distortions were considered according to their sources, and the registration procedure was accomplished through sequential steps. The first step comprises distortions from the SEM, such as astigmatism and local distortions. The second step adjusts rotation and the third one corrects translation. At the end, the registered images are cropped to represent exactly the

**100 µm**

The first step is carried out through a feature-based registration algorithm. It maps the SEM characteristic distortions based on several control points that are automatically detected. These distortions do not depend on samples. They are a function of SEM operational parameters, such as magnification and working distance. Therefore, this step was employed only one time for each SEM set-up, i.e., once for each magnification whose pixel size corresponds to the pixel size obtained through one of the objective lenses of the RLM.

**5.2 Image acquisition in SEM** 

**5.3 Image registration** 

same field.

their strong dependence from the light intensity (Littmann & Ritter, 1997). Besides, it doesn't represent colours in a uniform scale, preventing measurements of similarity between colours through their distance in RGB space (Cheng et al., 2001). There are many colour systems described in the literature (Sharma & Trussell, 1997). Actually, one can define any colour system from linear or non-linear transformations of RGB (Vandenbroucke et al., 2003). Systems like rgb, HSI, L\*a\*b\* and L\*u\*v\* present less correlated features and consequently tend to be more suitable for classification (Gomes & Paciornik, 2008a).

## **5. Combining reflected light microscopy and SEM**

This section describes the RLM-SEM co-site microscopy methodology by reviewing two case studies, in which it was applied for the characterization of a copper ore and an iron ore. These case studies were originally presented by Gomes & Paciornik (2008a and 2008c, respectively).

The RLM-SEM co-site microscopy comprises four sequential steps: image acquisition in RLM; image acquisition in SEM; registration; and image processing and analysis. The three first steps consist of generic routines of the methodology, but the image processing and analysis procedure depends on the case study. The segmentation of minerals in both case studies was performed through supervised classification of pixels, exploiting their multidimensional nature. However, used features and classification methods differ.

Ore microscopy procedures commonly involve acquisition and analysis of tens to hundreds of images per cross-section in order to provide a representative sampling. Therefore, in the development of the RLM-SEM co-site microscopy, automatic routines for field scanning and image acquisition were implemented for both used microscopes.

## **5.1 Image acquisition in reflected light microscopy**

A motorized and computer controlled microscope with a digital camera (1300 x 1030 pixels) was used. A routine was implemented for microscope and camera control, and for image acquisition. It integrates and automates many common procedures such as specimen x-y scanning, automatic focusing, background correction and imaging.

The following image acquisition procedures and conditions were employed:


## **5.2 Image acquisition in SEM**

324 Scanning Electron Microscopy

their strong dependence from the light intensity (Littmann & Ritter, 1997). Besides, it doesn't represent colours in a uniform scale, preventing measurements of similarity between colours through their distance in RGB space (Cheng et al., 2001). There are many colour systems described in the literature (Sharma & Trussell, 1997). Actually, one can define any colour system from linear or non-linear transformations of RGB (Vandenbroucke et al., 2003). Systems like rgb, HSI, L\*a\*b\* and L\*u\*v\* present less correlated features and

This section describes the RLM-SEM co-site microscopy methodology by reviewing two case studies, in which it was applied for the characterization of a copper ore and an iron ore. These case studies were originally presented by Gomes & Paciornik (2008a and 2008c,

The RLM-SEM co-site microscopy comprises four sequential steps: image acquisition in RLM; image acquisition in SEM; registration; and image processing and analysis. The three first steps consist of generic routines of the methodology, but the image processing and analysis procedure depends on the case study. The segmentation of minerals in both case studies was performed through supervised classification of pixels, exploiting their

Ore microscopy procedures commonly involve acquisition and analysis of tens to hundreds of images per cross-section in order to provide a representative sampling. Therefore, in the development of the RLM-SEM co-site microscopy, automatic routines for field scanning and

A motorized and computer controlled microscope with a digital camera (1300 x 1030 pixels) was used. A routine was implemented for microscope and camera control, and for image acquisition. It integrates and automates many common procedures such as specimen x-y

a. Before image acquisition, a SiC reflectivity standard was used to generate background images for each objective lens, which were subsequently employed for automatic

c. Camera sensitivity, exposure and white balance were optimized initially for a

d. Objective Lenses: 5X (NA 0.13); 10X (NA 0.20); 20X (NA 0.40), leading to resolutions of

e. Single fields regularly spaced on the sample were imaged through specimen scanning

f. Each field position was recorded in a database for subsequent image acquisition on in

multidimensional nature. However, used features and classification methods differ.

consequently tend to be more suitable for classification (Gomes & Paciornik, 2008a).

**5. Combining reflected light microscopy and SEM** 

image acquisition were implemented for both used microscopes.

scanning, automatic focusing, background correction and imaging.

The following image acquisition procedures and conditions were employed:

background correction (Pirard et al., 1999) of every acquired image. b. Illumination was kept constant by direct digital control of the lamp voltage.

representative field of the sample and kept constant there on.

**5.1 Image acquisition in reflected light microscopy** 

2.11, 1.05, and 0.53 µm/pixel, respectively.

with a motorised stage and automatic focusing.

g. All images were acquired at 24 bit RGB colour quantisation.

respectively).

SEM.

A digital SEM was used to acquire a BSE image (1024 x 768 pixels) of each field imaged on RLM. In this procedure, the sample must be placed in the SEM stage at a similar arrangement as positioned in the RLM stage. It is unnecessary and impractical to place the sample in the exact same way, but a similar arrangement can make image registration easier and faster.

The magnification of the SEM was set to keep similar resolutions to optical images. Other SEM operational parameters were manually tuned for a representative field of the sample and then kept constant. After that, the field positions database was loaded with the acquisition routine developed in the SEM control software. It converts RLM stage coordinates to SEM stage coordinates and subsequently performs automatic specimen scanning and image acquisition. Thus, respectively for copper ore and iron ore samples, 121 (11 x 11) and 81 (9 x 9) fields per cross-section were imaged with the RLM and the SEM. Figure 7 presents a field of the copper ore sample as imaged on RLM and SEM.

Fig. 7. Images of a field of the copper ore sample obtained by (a) RLM and (b) SEM.

## **5.3 Image registration**

An automatic method for the registration of RLM and SEM images was developed. The distortions were considered according to their sources, and the registration procedure was accomplished through sequential steps. The first step comprises distortions from the SEM, such as astigmatism and local distortions. The second step adjusts rotation and the third one corrects translation. At the end, the registered images are cropped to represent exactly the same field.

The first step is carried out through a feature-based registration algorithm. It maps the SEM characteristic distortions based on several control points that are automatically detected. These distortions do not depend on samples. They are a function of SEM operational parameters, such as magnification and working distance. Therefore, this step was employed only one time for each SEM set-up, i.e., once for each magnification whose pixel size corresponds to the pixel size obtained through one of the objective lenses of the RLM.

Multimodal Microscopy for Ore Characterization 327

in order to adjust the angle down to 0.01° of precision. This procedure is applied for one pair

After the second step, the SEM images are free of distortions and they are put in the same coordinate system of the RLM images. Therefore, only translation problems remain. Thus, in the third step, the RLM and the SEM images are finally registered through the maximization of normalized cross correlation. At the end, they are cropped to represent exactly the same

The registered SEM and RLM images went through a pre-processing step of delineation to reduce the well-known halo effect, making them more suitable for the subsequent segmentation procedures. Figure 10 shows a detail of an image obtained by RLM, before and after delineation operation. Comparing them, one can observe that delineation

improves phase transitions and consequently allows better segmentation results.

(a) (b)

Fig. 10. Delineation of a RLM image: (a) detail of the original image; (b) after delineation.

of images and the obtained angle is used to correct rotation in all SEM images.

field. Figure 9 shows the images present in Figure 7, after registration.

(a) (b)

Fig. 9. (a) RLM and (b) SEM images from Figure 7, after registration.

**5.4 Image processing and analysis** 

A standard specimen with regular distributed and easily extractable control points must be imaged on RLM and SEM. In the present case, two copper grids (200 mesh for 5X and 10X lenses, and 400 mesh for 20X lens) were used. These images were analyzed by an automatic routine in order to determine the centroid (center of gravity) of each grid hole, the control points. Then, a suitable spatial transformation was computed from the pairs of control points using the local weighted mean method proposed by Goshtasby (1988). Therefore, this spatial transformation was applied to just remove distortions in every SEM image of the ore sample.

Figure 8 shows the pairs of control points superimposed to the RLM image of the grid (10X). The white circles represent the control points extracted from the RLM image, and the white dots are the points obtained from the corresponding SEM image. As can be observed, circles and points are not aligned. Besides, the misalignment varies, evidencing the complexity of these distortions.

Fig. 8. Pairs of control points superimposed on the RLM image of the grid (10X). The white circles represent the control points extracted from the RLM image, and the white dots are the control points obtained from the corresponding SEM image.

The second step of the registration method aims at finding the rotation angle between images and subsequently to adjust rotation. This angle is due to sample manipulation and its different arrangement in the sample holders of the microscopes. Thus, this rotation is constant in all fields of a sample in a given experiment.

An iterative algorithm is used to determine the angle that maximizes the normalized cross correlation between a pair of images. The algorithm uses coarse-to-fine approach. It evolves

A standard specimen with regular distributed and easily extractable control points must be imaged on RLM and SEM. In the present case, two copper grids (200 mesh for 5X and 10X lenses, and 400 mesh for 20X lens) were used. These images were analyzed by an automatic routine in order to determine the centroid (center of gravity) of each grid hole, the control points. Then, a suitable spatial transformation was computed from the pairs of control points using the local weighted mean method proposed by Goshtasby (1988). Therefore, this spatial transformation was applied to just remove distortions in every SEM image of the ore sample. Figure 8 shows the pairs of control points superimposed to the RLM image of the grid (10X). The white circles represent the control points extracted from the RLM image, and the white dots are the points obtained from the corresponding SEM image. As can be observed, circles and points are not aligned. Besides, the misalignment varies, evidencing the complexity of

Fig. 8. Pairs of control points superimposed on the RLM image of the grid (10X). The white circles represent the control points extracted from the RLM image, and the white dots are

**200 µm** 

The second step of the registration method aims at finding the rotation angle between images and subsequently to adjust rotation. This angle is due to sample manipulation and its different arrangement in the sample holders of the microscopes. Thus, this rotation is

An iterative algorithm is used to determine the angle that maximizes the normalized cross correlation between a pair of images. The algorithm uses coarse-to-fine approach. It evolves

the control points obtained from the corresponding SEM image.

constant in all fields of a sample in a given experiment.

these distortions.

in order to adjust the angle down to 0.01° of precision. This procedure is applied for one pair of images and the obtained angle is used to correct rotation in all SEM images.

After the second step, the SEM images are free of distortions and they are put in the same coordinate system of the RLM images. Therefore, only translation problems remain. Thus, in the third step, the RLM and the SEM images are finally registered through the maximization of normalized cross correlation. At the end, they are cropped to represent exactly the same field. Figure 9 shows the images present in Figure 7, after registration.

Fig. 9. (a) RLM and (b) SEM images from Figure 7, after registration.

## **5.4 Image processing and analysis**

The registered SEM and RLM images went through a pre-processing step of delineation to reduce the well-known halo effect, making them more suitable for the subsequent segmentation procedures. Figure 10 shows a detail of an image obtained by RLM, before and after delineation operation. Comparing them, one can observe that delineation improves phase transitions and consequently allows better segmentation results.

Fig. 10. Delineation of a RLM image: (a) detail of the original image; (b) after delineation.

Multimodal Microscopy for Ore Characterization 329

The mineralogical assemblage of the iron ore sample was simple. It was mainly composed by hematite, magnetite, goethite and quartz. Therefore, in this case study, five classes were

The segmentation process was split in two supervised classification procedures. The first one recognised epoxy resin, quartz, goethite, and a hematite-magnetite composed phase through the classification of pixels in SEM images, using the BSE intensity as feature in a Bayes classifier. Then, the second classification procedure was able to discriminate hematite and magnetite. It was carried out through the classification of pixels in RLM-SEM composed images. In this case, their four components (R, G, B, and BSE intensity) were used as features and a Bayes classifier was employed. The training stage for both classification procedures comprised interactive sampling of 1000 pixels of each one of the five classes from a RLM-SEM composed image. Figure 12 presents the segmentation result for the images shown in

Fig. 12. The classification result for the images shown in the Figure 2: (a) segmented image;

The information from RLM and SEM presents different structures and physical meanings. RLM data consists of a vector of three 8-bit values, which denote specular reflectance in the RGB colour system. On the other hand, SEM data is represented as 8-bit values of BSE intensity that provides average atomic number contrast. Thus, it is very difficult to compute suitable measurements of similarity between patterns (pixels) that can be used to recognise the classes. In this kind of problem, Valev and Asaithambi (2001) pointed out that different classifiers can be used to complement one another. This is the approach employed for the

The increase of dimensionality carried out in the copper ore case study can make the classification task easier, since hidden information is discovered and consequently patterns are better described. However, the training data must grow exponentially with the dimensionality in order to prevent the so-called curse of dimensionality (Marques de Sá,

considered (epoxy resin, quartz, goethite, haematite, and magnetite).

(a) (b)

**5.4.2 Iron ore** 

and (b) look-up table.

segmentation in the iron ore case study.

**5.5 Discussion** 

Figure 2 and its applied look-up table.

## **5.4.1 Copper ore**

The copper ore sample had a complex mineralogy mainly composed of thirteen minerals, which were then taken as individual classes (quartz, three different silicates, apatite, magnetite, pentlandite, chalcopyrite, covelline, bornite, sphalerite, chalcocite, and native copper). Besides mineral phases, epoxy resin was taken as a class too. Thus, the training stage involved sampling of pixels from the fourteen classes. In practice, 6000 pixels of each phase were interactively selected from several images.

The delineated RLM images were converted from RGB to the rgb, HSI, L\*a\*b\* and L\*u\*v\* colour systems with the goal of revealing colour information hidden by the correlated RGB system. These conversion operations generate ten new components (r, g, b, H, S, I, a\*, b\*, u\*, v\*), increasing the system dimensionality from four to fourteen.

A Bayes classifier (Duda et al., 2001) was employed and the fourteen components (BSE intensity and the thirteen colour components) were used as features. The validation was carried out through holdout estimate (Toussaint, 1974), reaching a success rate larger than 99.5%.

The classification result was a grey level image per field in which each phase was represented by a distinct grey level. Thus, pixels classified as phase one have intensity one, pixels recognised as phase two have intensity two, and so on. Therefore, a suitable look-up table can be applied in order to attribute a different pseudo-colour to each phase and consequently to make their visualisation easier. Figure 11 presents the classification result for the images shown in Figure 9 and its look-up table.

Fig. 11. The classification result for the images shown in the Figure 9: (a) segmented image; and (b) look-up table.

## **5.4.2 Iron ore**

328 Scanning Electron Microscopy

The copper ore sample had a complex mineralogy mainly composed of thirteen minerals, which were then taken as individual classes (quartz, three different silicates, apatite, magnetite, pentlandite, chalcopyrite, covelline, bornite, sphalerite, chalcocite, and native copper). Besides mineral phases, epoxy resin was taken as a class too. Thus, the training stage involved sampling of pixels from the fourteen classes. In practice, 6000 pixels of each

The delineated RLM images were converted from RGB to the rgb, HSI, L\*a\*b\* and L\*u\*v\* colour systems with the goal of revealing colour information hidden by the correlated RGB system. These conversion operations generate ten new components (r, g, b, H, S, I, a\*, b\*, u\*,

A Bayes classifier (Duda et al., 2001) was employed and the fourteen components (BSE intensity and the thirteen colour components) were used as features. The validation was carried out through holdout estimate (Toussaint, 1974), reaching a success rate larger than

The classification result was a grey level image per field in which each phase was represented by a distinct grey level. Thus, pixels classified as phase one have intensity one, pixels recognised as phase two have intensity two, and so on. Therefore, a suitable look-up table can be applied in order to attribute a different pseudo-colour to each phase and consequently to make their visualisation easier. Figure 11 presents the classification result

(a) (b)

Fig. 11. The classification result for the images shown in the Figure 9: (a) segmented image;

**5.4.1 Copper ore** 

99.5%.

and (b) look-up table.

phase were interactively selected from several images.

for the images shown in Figure 9 and its look-up table.

v\*), increasing the system dimensionality from four to fourteen.

The mineralogical assemblage of the iron ore sample was simple. It was mainly composed by hematite, magnetite, goethite and quartz. Therefore, in this case study, five classes were considered (epoxy resin, quartz, goethite, haematite, and magnetite).

The segmentation process was split in two supervised classification procedures. The first one recognised epoxy resin, quartz, goethite, and a hematite-magnetite composed phase through the classification of pixels in SEM images, using the BSE intensity as feature in a Bayes classifier. Then, the second classification procedure was able to discriminate hematite and magnetite. It was carried out through the classification of pixels in RLM-SEM composed images. In this case, their four components (R, G, B, and BSE intensity) were used as features and a Bayes classifier was employed. The training stage for both classification procedures comprised interactive sampling of 1000 pixels of each one of the five classes from a RLM-SEM composed image. Figure 12 presents the segmentation result for the images shown in Figure 2 and its applied look-up table.

Fig. 12. The classification result for the images shown in the Figure 2: (a) segmented image; and (b) look-up table.

### **5.5 Discussion**

The information from RLM and SEM presents different structures and physical meanings. RLM data consists of a vector of three 8-bit values, which denote specular reflectance in the RGB colour system. On the other hand, SEM data is represented as 8-bit values of BSE intensity that provides average atomic number contrast. Thus, it is very difficult to compute suitable measurements of similarity between patterns (pixels) that can be used to recognise the classes. In this kind of problem, Valev and Asaithambi (2001) pointed out that different classifiers can be used to complement one another. This is the approach employed for the segmentation in the iron ore case study.

The increase of dimensionality carried out in the copper ore case study can make the classification task easier, since hidden information is discovered and consequently patterns are better described. However, the training data must grow exponentially with the dimensionality in order to prevent the so-called curse of dimensionality (Marques de Sá,

Multimodal Microscopy for Ore Characterization 331

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2001). In practice, this is not an issue for pixels classification as a typical image of 1024 x 768 pixels, for instance, has about 0.8 million of pixels, and it is easy to obtain several thousands of pixels of each class.

The resulting segmented images in both case studies reveal small amounts of misclassified pixels in borders between phases. It occurs mainly due to little cracks and relief, in spite of the good sample preparation and the delineation pre-processing. This misclassification is quite small and it can be negligible in mineralogical identification and quantification procedures. However, it becomes more significant for microstructural characterisation purposes, such as mineral liberation analysis. Therefore, post-processing routines should be developed.

## **6. Conclusion**

Multimodal microscopy extends the capabilities of traditional microscopy techniques, improving the discrimination of mineral phases in ores. By combining Optical Microscopy and Scanning Electron Microscopy it takes advantage of the complementary contrasts provided by these techniques.

This method relies on microscope automation, digital image acquisition, processing and analysis. Over the last years many of these techniques have become readily available in both commercial and free software environments.

The use of supervised classification methods relies on operator experience, during the training stage, but once the classifier is optimized and validated, the effective classification of unknown samples is fully automatic and fast.

The developed method is applicable to other materials for which individual microscopy techniques do not provide enough discrimination between the relevant phases.

## **7. Acknowledgment**

One of the authors (S. Paciornik) acknowledges the support of CNPq, the Brazilian Research Council.

## **8. References**


2001). In practice, this is not an issue for pixels classification as a typical image of 1024 x 768 pixels, for instance, has about 0.8 million of pixels, and it is easy to obtain several thousands

The resulting segmented images in both case studies reveal small amounts of misclassified pixels in borders between phases. It occurs mainly due to little cracks and relief, in spite of the good sample preparation and the delineation pre-processing. This misclassification is quite small and it can be negligible in mineralogical identification and quantification procedures. However, it becomes more significant for microstructural characterisation purposes, such as mineral liberation analysis. Therefore, post-processing routines should be

Multimodal microscopy extends the capabilities of traditional microscopy techniques, improving the discrimination of mineral phases in ores. By combining Optical Microscopy and Scanning Electron Microscopy it takes advantage of the complementary contrasts

This method relies on microscope automation, digital image acquisition, processing and analysis. Over the last years many of these techniques have become readily available in both

The use of supervised classification methods relies on operator experience, during the training stage, but once the classifier is optimized and validated, the effective classification

The developed method is applicable to other materials for which individual microscopy

One of the authors (S. Paciornik) acknowledges the support of CNPq, the Brazilian Research

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**17** 

Mexico

Maribel L. Saucedo-Muñoz,

*Instituto Politecnico Nacional (ESIQIE),* 

**SEM Analysis of Precipitation Process in Alloys** 

The microstructural characterization of the precipitation process in alloys is a very important aspect in order to understand the formation mechanism and growth kinetics of precipitated phases during its heating because of either the heat treating process or the operation-in-service conditions. Additionally, the microstructure control is a key point to know the degree of hardening after heat treating of the alloys and to assess their mechanical properties after a prolonged exposure at high temperature during the operation of an industrial component. There are different characterization techniques for microstructure; however, the use of the scanning electron microscopy, SEM, has been very popular for the microstructural observation and it has become a power tool for characterization of the phase transformations. Besides, the application of energydispersed-spectra, EDS-SEM system to the microstructural characterization has permitted to know not only the morphology of phases, sizes, distribution and then growth kinetics, but also their chemical composition and thus element distribution of the formed phases. Thus this chapter shows the application of SEM-EDS system to the characterization of microstructural of precipitation process in different alloy systems such as Fe-Ni-Al alloy,

Phase separation in alloys usually consists of the formation of a supersaturated solid solution by heating the alloy at temperatures higher than the equilibrium solvus line and subsequently quenched rapidly. This supersaturated solid solution can usually be separated in two or more phases as a result of the isothermal aging at temperatures lower than that of equilibrium. Phase separation can mainly take place by two mechanisms, nucleation and growth, and spinodal decomposition (Porter, 2009). The former mechanism consists of the formation of a stable nucleus with a nucleation barrier to overcome and it is characterized by an incubation period. In contrast, the latter one is initiated by the spontaneous formation and subsequent growth of coherent composition fluctuations. The formation of fine secondphase dispersion in a matrix promotes its hardening, known as precipitation hardening. If the aging of alloys continues, it is expected that larger precipitates will grow at the expense of smaller ones which dissolve again given rise to a change in the precipitate size

**1. Introduction** 

austenitic stainless steels and Mg-Zn-Al alloy.

**2. Precipitation in alloys** 

distribution (Kostorz, 2005).

Victor M. Lopez-Hirata and Hector J. Dorantes-Rosales

analysis. *Computer Vision and Image Understanding,* Vol. 90, No. 2, (May 2003), pp. 190-216, ISSN 1077-3142


## **SEM Analysis of Precipitation Process in Alloys**

Maribel L. Saucedo-Muñoz,

Victor M. Lopez-Hirata and Hector J. Dorantes-Rosales *Instituto Politecnico Nacional (ESIQIE),*  Mexico

## **1. Introduction**

334 Scanning Electron Microscopy

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The microstructural characterization of the precipitation process in alloys is a very important aspect in order to understand the formation mechanism and growth kinetics of precipitated phases during its heating because of either the heat treating process or the operation-in-service conditions. Additionally, the microstructure control is a key point to know the degree of hardening after heat treating of the alloys and to assess their mechanical properties after a prolonged exposure at high temperature during the operation of an industrial component. There are different characterization techniques for microstructure; however, the use of the scanning electron microscopy, SEM, has been very popular for the microstructural observation and it has become a power tool for characterization of the phase transformations. Besides, the application of energydispersed-spectra, EDS-SEM system to the microstructural characterization has permitted to know not only the morphology of phases, sizes, distribution and then growth kinetics, but also their chemical composition and thus element distribution of the formed phases. Thus this chapter shows the application of SEM-EDS system to the characterization of microstructural of precipitation process in different alloy systems such as Fe-Ni-Al alloy, austenitic stainless steels and Mg-Zn-Al alloy.

## **2. Precipitation in alloys**

Phase separation in alloys usually consists of the formation of a supersaturated solid solution by heating the alloy at temperatures higher than the equilibrium solvus line and subsequently quenched rapidly. This supersaturated solid solution can usually be separated in two or more phases as a result of the isothermal aging at temperatures lower than that of equilibrium. Phase separation can mainly take place by two mechanisms, nucleation and growth, and spinodal decomposition (Porter, 2009). The former mechanism consists of the formation of a stable nucleus with a nucleation barrier to overcome and it is characterized by an incubation period. In contrast, the latter one is initiated by the spontaneous formation and subsequent growth of coherent composition fluctuations. The formation of fine secondphase dispersion in a matrix promotes its hardening, known as precipitation hardening. If the aging of alloys continues, it is expected that larger precipitates will grow at the expense of smaller ones which dissolve again given rise to a change in the precipitate size distribution (Kostorz, 2005).

SEM Analysis of Precipitation Process in Alloys 337

shrinkage rate of an individual particle depends not only on its normalized radii but also on its local environment. That is, a particle surrounded by several larger particles will grow slower, or shrink faster, than a particle of the same size whose neighbors are smaller. Thus, as the volume fraction increased, the particle size distribution widened increasing the coarsening rate at the same time. It was also observed that the higher aging temperature, the faster coarsening kinetics of the β´ precipitates because of the increase in volume diffusion

Fig. 1. SEM micrographs for the Fe-10Ni-15Al alloy aged at 750 °C for (a) 75, (b) 250, and (c)

Fig. 2. Plot of r3-ro3 vs. aging time for the Fe-10Ni-15Al alloy aged at 750, 850 and 920 °C.

500 h, and at 920 °C for (d) 25, (e) 100 and (f) 200 h.

(Ratke & Vorhees, 2002).

#### **2.1 Coarsening process in Fe-Ni-Al alloys**

The precipitation of the β´phase is important for strengthening at high temperatures in different engineering ferritic alloys such as, PH stainless steels, nitralloy, Fe-Cr-Ni-Al based alloys, etc. These alloys are used in industrial components which require good mechanical strength and oxidation resistance at high temperatures. The β´phase is an ordered phase of the B2 type crystalline structure (Sauthoff, 2004). The coarsening resistance of precipitates is a key factor to keep the high strength at high temperatures in this type of alloys. An alternative to have a good coarsening resistance, it is to have a low value of lattice misfit which maintains a coherent interface between the precipitate and matrix (Kostorz, 2005). Thus, this section shows the effect of structural and morphological characteristics of the β´ precipitates on the coarsening behavior during the isothermal aging of an Fe-10Ni-15Al alloy.

## **2.1.1 Experimental details**

An Fe-10Ni-15Al alloy (wt. %) was melted using pure metallic elements in an electrical furnace under an argon atmosphere. The ingot of 30 x 10 x 10 mm was encapsulated in a quartz tube with argon gas and then homogenized at 1100 °C for one week. Specimens were solution treated at 1100 °C for 1 h and subsequently aged at temperatures of 750, 850 and 920 °C for times from 0.25 to 750 h. These samples were also observed with a SEM analysis with EDS detector at 20 kV. Vickers hardness was tested for the aged specimens using a load of 100 g.

## **2.1.2 Microstructural evolution of coarsening**

SEM micrographs of precipitates are shown for the sample aged at 750 and 920 °C for different times in Figs. 1 (a-c) and (d-f), respectively. The shape of the β´ precipitates was round without any preferential alignment for the aging at 750 °C up to 75 h and 920 °C up to 0.5 h, Figs 1 (a). A further aging changed the shape of the β´ precipitates to cuboids with a preferential alignment on the <100> directions of the ferritic α phase, Figs. 1 (c-e). A prolonged aging at 920 °C promoted the change of shape to rectangular plates also aligned in the <100> directions, Fig. 1 (f). The volume percentage of precipitation was determined to be about 30, 25 and 20 % for the samples aged at 750, 850 and 920 °C, respectively.

#### **2.1.3 Growth kinetics of coarsening**

The variation of the β´ precipitates size expressed as r3-ro3 with aging time for the sample aged at 750, 850 and 920 °C is shown in Fig. 2. It can be noticed that the experimental data fit to a straight line for each temperature. Thus the growth kinetics of coarsening followed the behavior predicted by the Lifshitz-Slyozov-Wagner (LSW) theory for coarsening controlled by volume diffusion. This fact shows a good agreement with the modified theory for the diffusion-controlled coarsening in ternary alloys (Kostorz, 2005) which predicts that growth kinetics is similar to that of LSW theory. The size distribution of precipitates is shown in Figs. 3 (a-c) for the sample aged at 750, 850 and 920 °C for 200 h, respectively. It can be seen that the size distribution is broader and lower than that predicted by the LSW theory because of the high volume fraction of precipitates, which has been reported in the coarsening process of several alloy systems. It has been observed that the growth or

The precipitation of the β´phase is important for strengthening at high temperatures in different engineering ferritic alloys such as, PH stainless steels, nitralloy, Fe-Cr-Ni-Al based alloys, etc. These alloys are used in industrial components which require good mechanical strength and oxidation resistance at high temperatures. The β´phase is an ordered phase of the B2 type crystalline structure (Sauthoff, 2004). The coarsening resistance of precipitates is a key factor to keep the high strength at high temperatures in this type of alloys. An alternative to have a good coarsening resistance, it is to have a low value of lattice misfit which maintains a coherent interface between the precipitate and matrix (Kostorz, 2005). Thus, this section shows the effect of structural and morphological characteristics of the β´ precipitates on the coarsening behavior during the isothermal aging of an Fe-10Ni-15Al

An Fe-10Ni-15Al alloy (wt. %) was melted using pure metallic elements in an electrical furnace under an argon atmosphere. The ingot of 30 x 10 x 10 mm was encapsulated in a quartz tube with argon gas and then homogenized at 1100 °C for one week. Specimens were solution treated at 1100 °C for 1 h and subsequently aged at temperatures of 750, 850 and 920 °C for times from 0.25 to 750 h. These samples were also observed with a SEM analysis with EDS detector at 20 kV. Vickers hardness was tested for the aged specimens using a load

SEM micrographs of precipitates are shown for the sample aged at 750 and 920 °C for different times in Figs. 1 (a-c) and (d-f), respectively. The shape of the β´ precipitates was round without any preferential alignment for the aging at 750 °C up to 75 h and 920 °C up to 0.5 h, Figs 1 (a). A further aging changed the shape of the β´ precipitates to cuboids with a preferential alignment on the <100> directions of the ferritic α phase, Figs. 1 (c-e). A prolonged aging at 920 °C promoted the change of shape to rectangular plates also aligned in the <100> directions, Fig. 1 (f). The volume percentage of precipitation was determined to

The variation of the β´ precipitates size expressed as r3-ro3 with aging time for the sample aged at 750, 850 and 920 °C is shown in Fig. 2. It can be noticed that the experimental data fit to a straight line for each temperature. Thus the growth kinetics of coarsening followed the behavior predicted by the Lifshitz-Slyozov-Wagner (LSW) theory for coarsening controlled by volume diffusion. This fact shows a good agreement with the modified theory for the diffusion-controlled coarsening in ternary alloys (Kostorz, 2005) which predicts that growth kinetics is similar to that of LSW theory. The size distribution of precipitates is shown in Figs. 3 (a-c) for the sample aged at 750, 850 and 920 °C for 200 h, respectively. It can be seen that the size distribution is broader and lower than that predicted by the LSW theory because of the high volume fraction of precipitates, which has been reported in the coarsening process of several alloy systems. It has been observed that the growth or

be about 30, 25 and 20 % for the samples aged at 750, 850 and 920 °C, respectively.

**2.1 Coarsening process in Fe-Ni-Al alloys** 

**2.1.2 Microstructural evolution of coarsening** 

**2.1.3 Growth kinetics of coarsening** 

alloy.

of 100 g.

**2.1.1 Experimental details** 

shrinkage rate of an individual particle depends not only on its normalized radii but also on its local environment. That is, a particle surrounded by several larger particles will grow slower, or shrink faster, than a particle of the same size whose neighbors are smaller. Thus, as the volume fraction increased, the particle size distribution widened increasing the coarsening rate at the same time. It was also observed that the higher aging temperature, the faster coarsening kinetics of the β´ precipitates because of the increase in volume diffusion (Ratke & Vorhees, 2002).

Fig. 1. SEM micrographs for the Fe-10Ni-15Al alloy aged at 750 °C for (a) 75, (b) 250, and (c) 500 h, and at 920 °C for (d) 25, (e) 100 and (f) 200 h.

Fig. 2. Plot of r3-ro3 vs. aging time for the Fe-10Ni-15Al alloy aged at 750, 850 and 920 °C.

SEM Analysis of Precipitation Process in Alloys 339

rectangular plates also aligned in this direction. The coarsening process followed the growth kinetics predicted by the LSW theory. Nevertheless, the hardness peak was higher and the overaging process occurred later in the sample aged at 920 °C than those of the sample aged at 750 °C. This behavior can be attributed to the fast formation of cuboid morphology and alignment in the <100> direction due to the higher lattice misfit between the ferritic matrix

and β´ precipitate at this aging temperature.

Fig. 4. Aging curves for the Fe-10Ni-15Al alloy aged at 750 and 920 °C.

The austenitic stainless steels are construction materials for key corrosion-resistant equipment in most of the major industries, particularly in the chemical, petroleum, and nuclear power industries (Marshal, 1984). These steels are iron alloys containing a minimum of approximately 12 % chromium. This content of chromium allows the formation of the passive film, which is self-healing in a wide variety of environments. Nitrogen as an alloying element in iron-based alloys is known since the beginning of the last century having been profoundly studied during the last three decades (Nakajima et al., 1996). Nevertheless, nitrogen steels are now not widely used. The reason for the comparatively narrow industrial application lies in the old customer skepticism in relation to nitrogen as an element causing brittleness in ferritic steels, some technical problems involved with nitrogen into steel, and the insufficient knowledge of the physical nature of nitrogen in iron and its alloys. In the case of austenitic stainless steels, the main driving force in the development of nitrogen-containing steels is due to the higher yield and tensile strengths achieved, compared with conventionally-processed austenitic stainless steels without sacrificing toughness. Nitrogen stainless steels have yield and tensile strengths as much as 200-350 % of the AISI 300 series steels. It is also important to notice that, in contrast to carbon, nitrogen-containing austenitic stainless steels retain high fracture toughness at low temperatures. Therefore, the higher mechanical properties of nitrogen-containing austenitic

**2.2 Precipitation in austenitic stainless steels** 

Fig. 3. Size distribution of precipitates for the Fe-10Ni-15Al alloy aged at (a) 750, (b) 850 and (c) 920 °C for 200 h.

#### **2.1.4 Hardening behavior**

Figure 4 shows the aging curves for the sample aged at 750 and 920 °C. A higher hardness can be noticed in the sample aged at 920 °C. This can be attributed to the morphology and alignment of β´ precipitates. That is, they are cuboids aligned in the <100> crystallographic directions of the ferritic matrix. A similar hardening behavior was observed in Fe-Ni-Al alloys aged at lower temperatures, 500 °C (Soriano-Vargas et al. 2010, Cayetano-Castro et al. 2008). In contrast, the precipitates are rounded particles without any preferential crystallographic alignment for aging at 750 °C up to 75 h. Besides, the size of β´ precipitates is much smaller than that of the sample aged at 920 °C. It can also be observed that the hardness peak is first reached in the aging at 750 °C than that at 920 °C. Additionally, the overaging started first for the aging at 750 °C. Besides, the hardness is almost the same value for prolonged aging at both temperatures. All the above facts suggest that even the coarsening process at 920 °C is the fastest one, the cuboid morphology and alignment of β´ precipitates causes a higher hardness peak and a slower overaging process than those corresponding at 750 °C.

In summary, the aging process of the Fe-10Ni-15Al alloy promoted the precipitation of the β´(Fe(NiAl)) precipitates with the B2 type crystalline structure. The morphology of β´ precipitates was rounded at the early stages of aging and then it changed to cuboids aligned in the <100> directions of the ferritic matrix. A prolonged aging caused the formation of

Fig. 3. Size distribution of precipitates for the Fe-10Ni-15Al alloy aged at (a) 750, (b) 850 and

Figure 4 shows the aging curves for the sample aged at 750 and 920 °C. A higher hardness can be noticed in the sample aged at 920 °C. This can be attributed to the morphology and alignment of β´ precipitates. That is, they are cuboids aligned in the <100> crystallographic directions of the ferritic matrix. A similar hardening behavior was observed in Fe-Ni-Al alloys aged at lower temperatures, 500 °C (Soriano-Vargas et al. 2010, Cayetano-Castro et al. 2008). In contrast, the precipitates are rounded particles without any preferential crystallographic alignment for aging at 750 °C up to 75 h. Besides, the size of β´ precipitates is much smaller than that of the sample aged at 920 °C. It can also be observed that the hardness peak is first reached in the aging at 750 °C than that at 920 °C. Additionally, the overaging started first for the aging at 750 °C. Besides, the hardness is almost the same value for prolonged aging at both temperatures. All the above facts suggest that even the coarsening process at 920 °C is the fastest one, the cuboid morphology and alignment of β´ precipitates causes a higher hardness peak and a slower overaging process than those

In summary, the aging process of the Fe-10Ni-15Al alloy promoted the precipitation of the β´(Fe(NiAl)) precipitates with the B2 type crystalline structure. The morphology of β´ precipitates was rounded at the early stages of aging and then it changed to cuboids aligned in the <100> directions of the ferritic matrix. A prolonged aging caused the formation of

(c) 920 °C for 200 h.

**2.1.4 Hardening behavior** 

corresponding at 750 °C.

rectangular plates also aligned in this direction. The coarsening process followed the growth kinetics predicted by the LSW theory. Nevertheless, the hardness peak was higher and the overaging process occurred later in the sample aged at 920 °C than those of the sample aged at 750 °C. This behavior can be attributed to the fast formation of cuboid morphology and alignment in the <100> direction due to the higher lattice misfit between the ferritic matrix and β´ precipitate at this aging temperature.

Fig. 4. Aging curves for the Fe-10Ni-15Al alloy aged at 750 and 920 °C.

#### **2.2 Precipitation in austenitic stainless steels**

The austenitic stainless steels are construction materials for key corrosion-resistant equipment in most of the major industries, particularly in the chemical, petroleum, and nuclear power industries (Marshal, 1984). These steels are iron alloys containing a minimum of approximately 12 % chromium. This content of chromium allows the formation of the passive film, which is self-healing in a wide variety of environments. Nitrogen as an alloying element in iron-based alloys is known since the beginning of the last century having been profoundly studied during the last three decades (Nakajima et al., 1996). Nevertheless, nitrogen steels are now not widely used. The reason for the comparatively narrow industrial application lies in the old customer skepticism in relation to nitrogen as an element causing brittleness in ferritic steels, some technical problems involved with nitrogen into steel, and the insufficient knowledge of the physical nature of nitrogen in iron and its alloys. In the case of austenitic stainless steels, the main driving force in the development of nitrogen-containing steels is due to the higher yield and tensile strengths achieved, compared with conventionally-processed austenitic stainless steels without sacrificing toughness. Nitrogen stainless steels have yield and tensile strengths as much as 200-350 % of the AISI 300 series steels. It is also important to notice that, in contrast to carbon, nitrogen-containing austenitic stainless steels retain high fracture toughness at low temperatures. Therefore, the higher mechanical properties of nitrogen-containing austenitic

SEM Analysis of Precipitation Process in Alloys 341

The volume fraction of the discontinuous precipitation increased with time and the maximum value was determined by the point-count grid method, to be about 0.04. This value seems to be reasonable, since a volume fraction of 0.1 was reported in an austenitic stainless steel containing 0.42 % N, after a long heat treatment (Kikuchi et al., 1991). Some small intragranular precipitates were present in the JN1 and JJ1 steels aged at 700 and 800 °C for 5 h, Figs. 5 (a-d). The volume fraction of intragranular precipitates for the aged JJ1 steel was slightly higher than that of the aged JN1 steel. This tendency became higher by increasing the aging temperature. Almost no intragranular precipitation was observed in the aged JK2 steel. The precipitation of particles was also observed to occur on twin boundaries for the aged JN1 steel. The X-ray diffraction patterns of residues extracted from the JN1, JJ1 and JK2 steels aged at 700 and 800 °C for 5 h are shown in Fig. 7. The extracted precipitates of the JN1 steel, aged at 700 and 800 °C for 5 h, were identified as Cr23C6 and Cr2N. The Cr2N and Cr23C6 phases were also detected in the aged JJ1 steel. Besides, the presence of the (Fe2Mo) η phase was also noted in the samples aged at 800 and 900 °C. The precipitated particles of JK2 steel were mainly composed of Cr23C6. According to the chemical composition, shown in Table 1, the JN1 steel has the highest and lowest contents of interstitial solutes (C and N), and Mn, respectively. This suggests that the highest volume fraction of precipitation for carbides and nitrides must have occurred in this steel. In contrast, the JK2 steel has an interstitial solute content lower than that of the JN1 steel, but it has the highest content of manganese, which maintains nitrogen in solid solution, avoiding its precipitation. That is, it is only expected the precipitation of carbides for this steel. This

All the above results are summarized in the Time-Temperature-Precipitation (TTP) diagrams of JN1, JJ1 and JK2 steels, as shown in Figs. 8 (a-c), respectively. In general, it can be noticed that the kinetics of precipitation for JN1 steel is faster than that of JJ1 steel, because of its higher interstitial solute content. The TTP diagrams show that the intergranular precipitation of Cr23C6 and Cr2N preceded to the intragranular precipitation of

In contrast, Figs. 9 (a), (b) and (c) show the plots of CVN fracture energy at -196 °C versus aging time for the JN1, JJ1 and JK2 steels aged at 700, 800 and 900 °C, respectively. All the steels showed a monotonotic decrease in the CVN fracture energy with aging time at the three temperatures. It is also evident that the drop of fracture toughness of JN1 steel is always faster than that of JJ1 steel. The fastest drop of fracture toughness occurred in the JN1 steel samples aged at 900 °C. This fact may be attributed to the higher content of C and N in JN1 steel, which can lead to faster kinetics in intergranular precipitation during the aging process, as discussed in a later section. The CVN fracture energy of solution treated JK2 steel was lower than that corresponding to the other two steels. The lowest decrease in the CVN fracture energy was for the aged JK2 steel. Furthermore, the JK2 steel, aged at 900 °C, showed almost no change in the fracture energy with time. All the JN1, JJ1 and JK2 steels fractured in a ductile manner in the solution treated condition. Intergranular facets were found in all the aged samples, although the area fraction of intergranular facets to ductile

fact showed a good agreement with the above results.

Cr2N, and Cr2N and η phase in JN1 and JJ1 steels, respectively.

**2.2.3 Precipitation kinetics** 

**2.2.4 Fracture toughness** 

stainless steels have made very attractive its application in the power-generation industry, shipbuilding, railways, cryogenic process, chemical equipment, pressure vessels and nuclear industries (Nakajima et al. 1989). These stainless steels are also susceptible to the precipitation of different phases because of the aging for long exposition at high temperatures or during continuous cooling after a welding process. Therefore, it is important to evaluate the degree of microstructural degradation due to the precipitation phenomenon which may affect the cryogenic toughness in this type of steels. In this section, three types of austenitic stainless steels, JJ1, JN1 and JK2 developed for applications to the superconducting magnets of fusion experimental reactor by JAERI, were selected to study the microstructure evolution during isothermal aging.

#### **2.2.1 Experimental details**

Materials used in this work were forged-steel plates of 200 mm thick and their chemical compositions are shown in Table 1. The solution treatment of JN1, and JJ1 and JK2 was carried out at 1075 and 1050 °C, respectively, for 1 hour under an argon atmosphere, and then water-quenched. The aging temperatures and times were 600, 700, 800 and 900 °C and from 10 to 1000 minutes, respectively. The aged samples were prepared metallographically and etched with Vilella´s reagent. The precipitates in the aged samples were extracted electrolitically by dissolution of the austenitic matrix in a solution of 10 vol. %HCl-CH3OH at 4 volts. The X-ray diffraction pattern of extracted precipitates was measured in a diffractometer using Kα Cu radiation. The SEM/EDX microanalysis of precipitates was also conducted using the extraction replica technique.


Table 1. Chemical composition (wt.%) of materials.

#### **2.2.2 Microstructural evolution**

An intergranular precipitation can be observed for all cases. The highest and lowest volume fraction of intergranular precipitates corresponded to the aged JN1 and JK2 steels, respectively, Figs. 5 (a-b) and (e-f). The presence of an intergranular cellular precipitation of Cr2N was observed to occur in the JN1 steel sample aged at 900 °C. No intergranular precipitation was practically detected for the JK2 steel aged at 700 °C. The intragranular precipitates can be classified into two types: cellular or discontinuous precipitation and plate-like precipitates, which have a preferred alignment with the austenitic matrix. The morphology of cellular precipitates is similar to that of pearlite in carbon-steels, Fig. 6. The formation of this lamellar microstructure initiated at grain boundaries and grew into the austenite γ matrix, according to the following reaction:

$$
\chi \to \chi \text{+Cr}\_2\text{N} \tag{1}
$$

stainless steels have made very attractive its application in the power-generation industry, shipbuilding, railways, cryogenic process, chemical equipment, pressure vessels and nuclear industries (Nakajima et al. 1989). These stainless steels are also susceptible to the precipitation of different phases because of the aging for long exposition at high temperatures or during continuous cooling after a welding process. Therefore, it is important to evaluate the degree of microstructural degradation due to the precipitation phenomenon which may affect the cryogenic toughness in this type of steels. In this section, three types of austenitic stainless steels, JJ1, JN1 and JK2 developed for applications to the superconducting magnets of fusion experimental reactor by JAERI, were selected to study

Materials used in this work were forged-steel plates of 200 mm thick and their chemical compositions are shown in Table 1. The solution treatment of JN1, and JJ1 and JK2 was carried out at 1075 and 1050 °C, respectively, for 1 hour under an argon atmosphere, and then water-quenched. The aging temperatures and times were 600, 700, 800 and 900 °C and from 10 to 1000 minutes, respectively. The aged samples were prepared metallographically and etched with Vilella´s reagent. The precipitates in the aged samples were extracted electrolitically by dissolution of the austenitic matrix in a solution of 10 vol. %HCl-CH3OH at 4 volts. The X-ray diffraction pattern of extracted precipitates was measured in a diffractometer using Kα Cu radiation. The SEM/EDX microanalysis of precipitates was

Material C Si Mn Ni Cr Al N Mo

JN1 0.040 0.97 3.88 15.07 24.32 0.023 0.32 ---

JJ1 0.025 0.48 10.13 11.79 12.01 --- 0.236 4.94

JK2 0.05 0.39 21.27 9.15 12.97 --- 0.247 0.97

An intergranular precipitation can be observed for all cases. The highest and lowest volume fraction of intergranular precipitates corresponded to the aged JN1 and JK2 steels, respectively, Figs. 5 (a-b) and (e-f). The presence of an intergranular cellular precipitation of Cr2N was observed to occur in the JN1 steel sample aged at 900 °C. No intergranular precipitation was practically detected for the JK2 steel aged at 700 °C. The intragranular precipitates can be classified into two types: cellular or discontinuous precipitation and plate-like precipitates, which have a preferred alignment with the austenitic matrix. The morphology of cellular precipitates is similar to that of pearlite in carbon-steels, Fig. 6. The formation of this lamellar microstructure initiated at grain boundaries and grew into the

γ γ → +Cr N2 (1)

the microstructure evolution during isothermal aging.

also conducted using the extraction replica technique.

Table 1. Chemical composition (wt.%) of materials.

austenite γ matrix, according to the following reaction:

**2.2.2 Microstructural evolution** 

**2.2.1 Experimental details** 

The volume fraction of the discontinuous precipitation increased with time and the maximum value was determined by the point-count grid method, to be about 0.04. This value seems to be reasonable, since a volume fraction of 0.1 was reported in an austenitic stainless steel containing 0.42 % N, after a long heat treatment (Kikuchi et al., 1991). Some small intragranular precipitates were present in the JN1 and JJ1 steels aged at 700 and 800 °C for 5 h, Figs. 5 (a-d). The volume fraction of intragranular precipitates for the aged JJ1 steel was slightly higher than that of the aged JN1 steel. This tendency became higher by increasing the aging temperature. Almost no intragranular precipitation was observed in the aged JK2 steel. The precipitation of particles was also observed to occur on twin boundaries for the aged JN1 steel. The X-ray diffraction patterns of residues extracted from the JN1, JJ1 and JK2 steels aged at 700 and 800 °C for 5 h are shown in Fig. 7. The extracted precipitates of the JN1 steel, aged at 700 and 800 °C for 5 h, were identified as Cr23C6 and Cr2N. The Cr2N and Cr23C6 phases were also detected in the aged JJ1 steel. Besides, the presence of the (Fe2Mo) η phase was also noted in the samples aged at 800 and 900 °C. The precipitated particles of JK2 steel were mainly composed of Cr23C6. According to the chemical composition, shown in Table 1, the JN1 steel has the highest and lowest contents of interstitial solutes (C and N), and Mn, respectively. This suggests that the highest volume fraction of precipitation for carbides and nitrides must have occurred in this steel. In contrast, the JK2 steel has an interstitial solute content lower than that of the JN1 steel, but it has the highest content of manganese, which maintains nitrogen in solid solution, avoiding its precipitation. That is, it is only expected the precipitation of carbides for this steel. This fact showed a good agreement with the above results.

### **2.2.3 Precipitation kinetics**

All the above results are summarized in the Time-Temperature-Precipitation (TTP) diagrams of JN1, JJ1 and JK2 steels, as shown in Figs. 8 (a-c), respectively. In general, it can be noticed that the kinetics of precipitation for JN1 steel is faster than that of JJ1 steel, because of its higher interstitial solute content. The TTP diagrams show that the intergranular precipitation of Cr23C6 and Cr2N preceded to the intragranular precipitation of Cr2N, and Cr2N and η phase in JN1 and JJ1 steels, respectively.

#### **2.2.4 Fracture toughness**

In contrast, Figs. 9 (a), (b) and (c) show the plots of CVN fracture energy at -196 °C versus aging time for the JN1, JJ1 and JK2 steels aged at 700, 800 and 900 °C, respectively. All the steels showed a monotonotic decrease in the CVN fracture energy with aging time at the three temperatures. It is also evident that the drop of fracture toughness of JN1 steel is always faster than that of JJ1 steel. The fastest drop of fracture toughness occurred in the JN1 steel samples aged at 900 °C. This fact may be attributed to the higher content of C and N in JN1 steel, which can lead to faster kinetics in intergranular precipitation during the aging process, as discussed in a later section. The CVN fracture energy of solution treated JK2 steel was lower than that corresponding to the other two steels. The lowest decrease in the CVN fracture energy was for the aged JK2 steel. Furthermore, the JK2 steel, aged at 900 °C, showed almost no change in the fracture energy with time. All the JN1, JJ1 and JK2 steels fractured in a ductile manner in the solution treated condition. Intergranular facets were found in all the aged samples, although the area fraction of intergranular facets to ductile

SEM Analysis of Precipitation Process in Alloys 343

Fig. 6. SEM micrograph of the cellular precipitation in the JN1 steel aged at 700 °C for

Fig. 7. X-ray diffraction patterns of extracted residues for JN1, JJ1 and JK2 steels agedat 700

1000 h.

and 800 °C for 5 h.

surface was strongly dependent on aging conditions. The fraction of intergranular brittle fracture increased with time and temperature, and it seemed consistent with the CVN fracture energy value. Nevertheless, the fracture surface of the JK2 steel, aged at 900 °C, showed almost a complete ductile- fracture mode. These results are in agreement with the fracture mode observed in the tested SP test specimens.

Fig. 5. SEM micrographs of JN1, JJ1 and JK2 steels aged at 700 and 800 °C for 5 h.

In summary, the highest and lowest degradation in toughness for JN1 and JK2 steel, respectively, is associated with the volume fraction of intergranular precipitation formed during the thermal aging. An abundant presence of intergranular precipitates was reported to causes the reduction of cohesive strength of grain boundaries (Saucedo et al., 2001). This is also confirmed by the increase in intergranular brittle fracture as the thermal aging progresses.

surface was strongly dependent on aging conditions. The fraction of intergranular brittle fracture increased with time and temperature, and it seemed consistent with the CVN fracture energy value. Nevertheless, the fracture surface of the JK2 steel, aged at 900 °C, showed almost a complete ductile- fracture mode. These results are in agreement with the

Fig. 5. SEM micrographs of JN1, JJ1 and JK2 steels aged at 700 and 800 °C for 5 h.

progresses.

In summary, the highest and lowest degradation in toughness for JN1 and JK2 steel, respectively, is associated with the volume fraction of intergranular precipitation formed during the thermal aging. An abundant presence of intergranular precipitates was reported to causes the reduction of cohesive strength of grain boundaries (Saucedo et al., 2001). This is also confirmed by the increase in intergranular brittle fracture as the thermal aging

fracture mode observed in the tested SP test specimens.

Fig. 6. SEM micrograph of the cellular precipitation in the JN1 steel aged at 700 °C for 1000 h.

Fig. 7. X-ray diffraction patterns of extracted residues for JN1, JJ1 and JK2 steels agedat 700 and 800 °C for 5 h.

SEM Analysis of Precipitation Process in Alloys 345

Fig. 9. Plot of CVN impact energy at – 196 °C vs. aging time for tested steels.

Fig. 8. TTP diagrams of the (a) JN1, (b) JJ1 and (c) JK2 steels.

Fig. 8. TTP diagrams of the (a) JN1, (b) JJ1 and (c) JK2 steels.

Fig. 9. Plot of CVN impact energy at – 196 °C vs. aging time for tested steels.

SEM Analysis of Precipitation Process in Alloys 347

observed in these micrographs, Fig. 11 (h). In general, there is a precipitate coarsening as the aging process progresses, Figs. 11 (e-f) and (h-i). The morphology of cellular precipitation at 100 and 200 °C mainly consisted of an S-shape and double-seam morphologies. In contrast, the shape corresponding to 300 °C was mainly a single-seam. It has been reported (Aaronson et al., 2010) that the first morphology occurs at a low temperature (T<Tm/2) and it is associated with the free-boundary mechanism and the second one takes place at lower

Fig. 10. XRD patterns of the specimens solution-treated and aged at 300 °C for 150 h.

Fig. 11. SEM micrographs of the alloy aged at 100°C for (a) 550, (b) 1500 and (c) 3000 h, At

200 °C for (d) 1, (e) 10 and (f) 250 h, and at 300°C for (g) 0.9, (h) 1 and (i) 25 h.

temperature and it is related to the precipitate-assisted mechanism.

## **2.3 Cellular precipitation in a Mg-8.5Al-0.5Zn-0.2Mn alloy**

Mg-Al-Zn alloys have become one of the most important light alloys with a wide range of applications in the automotive industry. This is attributed to the best combination of castability, mechanical strength and ductility (Kainer, 2003). The AZ series of magnesium alloys are mainly based on the Mg-Al binary alloys system. According to the equilibrium Mg-Al alloy phase diagram, the equilibrium phases are the hcp Mg-rich α phase and Mg17Al12-γ phase with a complex bcc structure. During the aging process of the Mg-Al based alloys, two types of precipitation are present. That is, discontinuous precipitation takes place on grain boundaries. One of these, intergranular precipitations occurs forming a lamellar structure and it is also known as cellular precipitation. Additionally, continuous precipitation takes place in an intragranular manner and it exhibits more complicated morphologies and orientation relationships than the cellular precipitation. It has been shown in several works (Lai et al., 2008) that these alloys have a poor response to precipitation hardening, compared with precipitation-hardenable Al alloys. Furthermore, the aging hardness is strongly influenced by the morphology, the size and the distribution density of Mg17Al12 precipitates. Besides, it has been reported that both discontinuous and continuous precipitations have an effect on the hardness of these alloys (Contreras-Piedras, et al., 2010). Thus, this section shows the mechanism and growth kinetics of cellular precipitation in a Mg-8.5Al-0.5Zn-0.2Mn (wt.%) alloy aged isothermally at 100, 200 and 300 °C for different time periods.

## **2.3.1 Experimental details**

A Mg-8.5Al-0.5Zn-0.2Mn (wt.%) alloy was melted using pure metallic elements under a protective argon atmosphere. Table 1 shows the chemical analysis corresponding to this alloy. Specimens of 10 mm x 10 mm x 10 mm were cut from the ingot and encapsulated in a Pyrex tube under an argon atmosphere. These were homogenized at 430 °C for 3 days and subsequently water-quenched. Homogenized and solution-treated specimens were aged at 100, 200 and 300 °C for different times. The heat-treated specimens were analyzed by X-ray diffraction with copper Kα radiation. These specimens were prepared metallographically and etched with an etchant composed of 19 ml distilled water, 60 ml ethylene glycol, 20 ml glacial acetic acid and 1 ml nitric acid. Etched specimens were observed at 25 kV with a scanning electron microscope equipped with EDS analysis. Vickers hardness was measured in all the heat-treated samples with a load of 100 g. The volume fraction of the discontinuous precipitation was determined from SEM images using a commercial image analyzer.

## **2.3.2 Microstructural characterization**

The X-ray diffraction patterns of the specimens in the conditions of solution-treated and aged at 300 °C for 150 h are shown in Fig. 10. A single-phase is confirmed in the solutiontreated specimen, while the appearance of XRD peaks corresponding to the Mg17Al12-γ phase are evident in the XRD pattern of the specimen aged at 300 °C for 150 h. No other phases were detected. The presence of these phases for each case is in agreement with the equilibrium Mg-Al phase diagram. Figures 11 (a-i) show the SEM micrographs for the specimens aged at 100, 200 and 300 °C for different time periods. There is a clear competition between the discontinuous and continuous precipitation from the early to the late stages of aging (see, for instance Fig. 11 (e)). Some intragranular precipitates are also

Mg-Al-Zn alloys have become one of the most important light alloys with a wide range of applications in the automotive industry. This is attributed to the best combination of castability, mechanical strength and ductility (Kainer, 2003). The AZ series of magnesium alloys are mainly based on the Mg-Al binary alloys system. According to the equilibrium Mg-Al alloy phase diagram, the equilibrium phases are the hcp Mg-rich α phase and Mg17Al12-γ phase with a complex bcc structure. During the aging process of the Mg-Al based alloys, two types of precipitation are present. That is, discontinuous precipitation takes place on grain boundaries. One of these, intergranular precipitations occurs forming a lamellar structure and it is also known as cellular precipitation. Additionally, continuous precipitation takes place in an intragranular manner and it exhibits more complicated morphologies and orientation relationships than the cellular precipitation. It has been shown in several works (Lai et al., 2008) that these alloys have a poor response to precipitation hardening, compared with precipitation-hardenable Al alloys. Furthermore, the aging hardness is strongly influenced by the morphology, the size and the distribution density of Mg17Al12 precipitates. Besides, it has been reported that both discontinuous and continuous precipitations have an effect on the hardness of these alloys (Contreras-Piedras, et al., 2010). Thus, this section shows the mechanism and growth kinetics of cellular precipitation in a Mg-8.5Al-0.5Zn-0.2Mn (wt.%) alloy aged isothermally at 100, 200 and 300

A Mg-8.5Al-0.5Zn-0.2Mn (wt.%) alloy was melted using pure metallic elements under a protective argon atmosphere. Table 1 shows the chemical analysis corresponding to this alloy. Specimens of 10 mm x 10 mm x 10 mm were cut from the ingot and encapsulated in a Pyrex tube under an argon atmosphere. These were homogenized at 430 °C for 3 days and subsequently water-quenched. Homogenized and solution-treated specimens were aged at 100, 200 and 300 °C for different times. The heat-treated specimens were analyzed by X-ray diffraction with copper Kα radiation. These specimens were prepared metallographically and etched with an etchant composed of 19 ml distilled water, 60 ml ethylene glycol, 20 ml glacial acetic acid and 1 ml nitric acid. Etched specimens were observed at 25 kV with a scanning electron microscope equipped with EDS analysis. Vickers hardness was measured in all the heat-treated samples with a load of 100 g. The volume fraction of the discontinuous

precipitation was determined from SEM images using a commercial image analyzer.

The X-ray diffraction patterns of the specimens in the conditions of solution-treated and aged at 300 °C for 150 h are shown in Fig. 10. A single-phase is confirmed in the solutiontreated specimen, while the appearance of XRD peaks corresponding to the Mg17Al12-γ phase are evident in the XRD pattern of the specimen aged at 300 °C for 150 h. No other phases were detected. The presence of these phases for each case is in agreement with the equilibrium Mg-Al phase diagram. Figures 11 (a-i) show the SEM micrographs for the specimens aged at 100, 200 and 300 °C for different time periods. There is a clear competition between the discontinuous and continuous precipitation from the early to the late stages of aging (see, for instance Fig. 11 (e)). Some intragranular precipitates are also

**2.3 Cellular precipitation in a Mg-8.5Al-0.5Zn-0.2Mn alloy** 

°C for different time periods.

**2.3.1 Experimental details** 

**2.3.2 Microstructural characterization** 

observed in these micrographs, Fig. 11 (h). In general, there is a precipitate coarsening as the aging process progresses, Figs. 11 (e-f) and (h-i). The morphology of cellular precipitation at 100 and 200 °C mainly consisted of an S-shape and double-seam morphologies. In contrast, the shape corresponding to 300 °C was mainly a single-seam. It has been reported (Aaronson et al., 2010) that the first morphology occurs at a low temperature (T<Tm/2) and it is associated with the free-boundary mechanism and the second one takes place at lower temperature and it is related to the precipitate-assisted mechanism.

Fig. 10. XRD patterns of the specimens solution-treated and aged at 300 °C for 150 h.

Fig. 11. SEM micrographs of the alloy aged at 100°C for (a) 550, (b) 1500 and (c) 3000 h, At 200 °C for (d) 1, (e) 10 and (f) 250 h, and at 300°C for (g) 0.9, (h) 1 and (i) 25 h.

SEM Analysis of Precipitation Process in Alloys 349

rapid coarsening of the Mg17Al12-γ precipitates either in the discontinuous or continuous precipitations. In contrast, the highest and slowest hardness peaks occurred in the alloy aged at 100 °C. This fact seems to be related to the fine continuous precipitation due to the

Fig. 12. Volume fraction of cellular precipitation vs. aging time of the alloy aged at 100, 200

Fig. 13. Interlamellar spacing as a function of aging temperature.

slow diffusion process at this temperature.

and 300 °C.

#### **2.3.3 Growth kinetics of cellular precipitation**

The plot of volume fraction of cellular precipitation vs. aging time is shown in Fig. 12. The highest volume fraction occurred for the lowest aging temperature, 100 °C. This fact suggests that continuous precipitation extends more rapidly within grains limiting the growth of cellular precipitation. The analysis of the plot of the volume fraction *Xf* vs. aging time *t*, Fig. 12, was carried out using the Johnson-Mehl-Avrami-Kolmogorov equation (Cahn, 1975):

$$\mathbf{X}\_{\text{f}} = \mathbf{1} - \exp\left(-\mathbf{k}\mathbf{t}^{\text{n}}\right) \tag{2}$$

The time exponent *n* was determined to be about 1.1, 0.85 and 0.87 for 100, 200 and 300 °C, respectively. These exponent values are close to 1 and it is associated with the dimensionality of the saturation site. That is, it corresponds to a boundary (Cahn, 1975). The lamellar structure always nucleates at grain boundaries and grows perpendicularly to them. The cellular growth stops only if the volume fraction of continuous precipitation is significant to impede its growth. In addition, the activation energy for the cellular precipitation was determined to be about 64.6 kJ mol-1. It was obtained by the slope of the straight line in the Arrhenius plot of the time for a volume fraction of 0.6 vs. the reciprocal value of the absolute temperature as shown in Fig. 5. This energy value seems to be reasonable because it is much lower than the self-diffusion of Mg, 135 kJ mol-1 (Mehrer, 1990). That is, it seems to correspond to a grain boundary diffusion process. Additionally, an energy value of 84 kJ mol-1 was reported for the cellular precipitation in the binary Al-Zn alloy system (Contreras et al., 2010), which is also a low energy value as that found in present work. Figure 6 shows the variation of interlamellar spacing, S, of discontinuous precipitation as a function of temperature. An increase in lamellar spacing is observed with the increase in temperature. A similar behavior was reported for the discontinuous precipitation in Al-Zn alloys (Contreas et al. 2010). According to the Turnbull theory for cell growth kinetics, the interlamellar spacing S is defined as follows (Aaronson et al. 2010):

$$\mathbf{S} = \mathbf{4}\mathbf{\hat{y}}\mathbf{V}/\Delta\mathbf{G} \tag{3}$$

Where γ is the interfacial energy, V the molar volume, and ΔG the free energy associated with the cellular reaction. ΔG has an inverse relation with undercooling, temperature. Thus, the lower temperature corresponds to the shorter interlamellar spacing. Moreover, the interlamellar spacing remains constant with the increase in aging time for all aging temperatures. These facts seem to be in agreement with the Turnbull theory, which predicts constant lamellar spacing and lamellae growth rate according to the following equation (Aaronson et al., 2010):

$$\mathbf{G} = \sim 4 \mathbf{\hat{S}} \mathbf{D}\_{\boldsymbol{\delta}} / \mathbf{S}^2 \tag{4}$$

Where G is the lamellae growth rate, Db is the solute diffusivity along the cell boundary and δ is the cell boundary thickness.

#### **2.3.4 Hardenin behavior**

The aging curves for 100, 200 and 300 °C are shown in Fig. 7. The lowest and fastest hardness peak was observed in the aging at 300 °C. This behavior can be attributed to the

The plot of volume fraction of cellular precipitation vs. aging time is shown in Fig. 12. The highest volume fraction occurred for the lowest aging temperature, 100 °C. This fact suggests that continuous precipitation extends more rapidly within grains limiting the growth of cellular precipitation. The analysis of the plot of the volume fraction *Xf* vs. aging time *t*, Fig. 12, was carried out using the Johnson-Mehl-Avrami-Kolmogorov equation (Cahn, 1975):

The time exponent *n* was determined to be about 1.1, 0.85 and 0.87 for 100, 200 and 300 °C, respectively. These exponent values are close to 1 and it is associated with the dimensionality of the saturation site. That is, it corresponds to a boundary (Cahn, 1975). The lamellar structure always nucleates at grain boundaries and grows perpendicularly to them. The cellular growth stops only if the volume fraction of continuous precipitation is significant to impede its growth. In addition, the activation energy for the cellular precipitation was determined to be about 64.6 kJ mol-1. It was obtained by the slope of the straight line in the Arrhenius plot of the time for a volume fraction of 0.6 vs. the reciprocal value of the absolute temperature as shown in Fig. 5. This energy value seems to be reasonable because it is much lower than the self-diffusion of Mg, 135 kJ mol-1 (Mehrer, 1990). That is, it seems to correspond to a grain boundary diffusion process. Additionally, an energy value of 84 kJ mol-1 was reported for the cellular precipitation in the binary Al-Zn alloy system (Contreras et al., 2010), which is also a low energy value as that found in present work. Figure 6 shows the variation of interlamellar spacing, S, of discontinuous precipitation as a function of temperature. An increase in lamellar spacing is observed with the increase in temperature. A similar behavior was reported for the discontinuous precipitation in Al-Zn alloys (Contreas et al. 2010). According to the Turnbull theory for cell growth kinetics, the interlamellar spacing S is defined as follows (Aaronson et al. 2010):

S=-4γV/ΔG (3)

Where γ is the interfacial energy, V the molar volume, and ΔG the free energy associated with the cellular reaction. ΔG has an inverse relation with undercooling, temperature. Thus, the lower temperature corresponds to the shorter interlamellar spacing. Moreover, the interlamellar spacing remains constant with the increase in aging time for all aging temperatures. These facts seem to be in agreement with the Turnbull theory, which predicts constant lamellar spacing and lamellae growth rate according to the following equation

Where G is the lamellae growth rate, Db is the solute diffusivity along the cell boundary and

The aging curves for 100, 200 and 300 °C are shown in Fig. 7. The lowest and fastest hardness peak was observed in the aging at 300 °C. This behavior can be attributed to the

( ) <sup>n</sup> X 1 exp kt <sup>f</sup> =− − (2)

<sup>2</sup> G ~4 ≈ δD /S <sup>δ</sup> 4)

**2.3.3 Growth kinetics of cellular precipitation** 

(Aaronson et al., 2010):

δ is the cell boundary thickness.

**2.3.4 Hardenin behavior** 

rapid coarsening of the Mg17Al12-γ precipitates either in the discontinuous or continuous precipitations. In contrast, the highest and slowest hardness peaks occurred in the alloy aged at 100 °C. This fact seems to be related to the fine continuous precipitation due to the slow diffusion process at this temperature.

Fig. 12. Volume fraction of cellular precipitation vs. aging time of the alloy aged at 100, 200 and 300 °C.

Fig. 13. Interlamellar spacing as a function of aging temperature.

SEM Analysis of Precipitation Process in Alloys 351

The authors wish to acknowledge the financial support from Instituto Politecnico Nacional

Aaronson, H.I, Enomoto M. & Lee, J.K. (2010). *Mechanism of Diffusional Phase Transformations* 

Cayetano-Castro, N.; Dorantes-Rosales H., Lopez-Hirata, V.M., Cruz-Rivera, J. & Gonzalez-

Christian J.W. (1975), *The Theory of Transformations in Metals and Alloys*, Pergamon Press,

Contreras-Piedra, E., Esquivel-Gonzalez, R., Lopez-Hirata, V.M., Saucedo-Muñoz, M.L.,

Kainer, K.U. (2003), Magnesium- Alloys and Technologies, Wiley-VCH, ISBN 3-527-30570-X,

Kikuchi M., Kajihara M. & Choi S. (1991). Cellular Precipitation Involving both

Lai W.J.; Lai, Y.Y. Lu, Y.F. Hsu, S. Trong, W.H. Wang. (2009). Aging behaviour and

Marshal, P. (1984). *Austenitic Stainless Steels Microstructure and Properties*, Elsevier Applied

Mehrer, H. (1990), *Numerical Data and Functional Relationship in Science and Technology*,

Nakajima H., Nunoya Y., Nozawa M., Ivano O., Takano K., Ando S. & Ohkita S. (1996).

Porter D.A.; Easterling, K.E. & Sherif, M.Y (2009). *Phase Transformations in Metals and Alloys*,

Ratke, L. & Vorhees, P.W. (2002). *Growth and Coarsening: Ripening in Materials*, Springer,

Sauthoff, G. (1995). *Intermetallics,* Wiley-VCH, ISBN 3-527-29320-5, Weinheim, Germany

*Materials Science Engineering A*, Vol. 146, pp. 131-150, ISSN 0921-5093 Kostorz, G. (2001). *Phase Transformations in Materials*, Wiley-VCH, ISBN 3-527-30256-5,

Velazquez, J.L. (2008). Cinética de Engrosamiento de Precipitados Coherentes en la Aleación Fe-10%Ni-15%Al. *Revista de Metalurgia de Madrid*, Vol. 44, No. X, (Month,

Paniagua-Mercado, A.M. & Dorantes-Rosales, H.J. (2010). Growth Kinetics of Cellular Precipitation in a Mg-8.5Al-0.5Zn-0.2Mn (wt.%) Alloy, *Materials Science* 

Substitutional and Interstitial Solutes: Cellular of Cr2N in Cr-Ni Austenitic Steels.

precipitate morphologies in Mg–7.7Al–0.5Zn–0.3Mn (wt.%) alloy, *Journal of Alloys* 

Landolt-Borstein New Series III/26, ISBN 0-387-50886-4, Springer-Verlag, Berlin,

Development of High Strength Austenitic Stainless Steel for Conduit of Nb3Al Conductor, *Advances in Cryogenic Engineering*, Vol. 42 A, pp. 323-330, ISSN 0065-

*in Metals and Alloys*, CRC Press, ISBN 978-1-4200-6299-1, NW, USA

*Engineering A*, Vol. 527, pp. 7775-7778, 2010. ISSN 0921-5093

*Compounds,* Vol. 476, pp.118-124, ISSN 0925-8388

Science Publisher, ISBN 0267-0836, NY, USA

CRC Press, ISBN 978-1-4200-6210-6, NW, USA.

Berlin, Germany, ISBN 3-540—42563-2

**4. Acknowledgment** 

Germany

Germany

2482

Weinheim, Germany

**5. References** 

(ESIQIE), SIP-IPN and CONACYT 100584.

2008) pp. 162-169, ISSN 1582-2214

ISBN 0-08-018031-0, Oxford, UK

In summary, the microstructural evolution and growth kinetics were studied in an isothermally-aged Mg-8.5Al-0.5Zn-0.2Mn (wt%) alloy and the growth kinetics of cellular precipitation was evaluated using the Johnson-Mehl-Avrami-Kolmogorov equation analysis (Cahn, 1975), which gives a time exponent close to 1. This value confirms that cellular precipitation takes place on the saturation sites corresponding to grain boundaries. Additionally, the activation energy for the cellular precipitation was determined to be about 64.6 kJ mol-1. This also indicates a grain boundary diffusion process. The variation of cellular spacing with temperature follows the behavior expected by Turnbull theory. The highest hardness peak corresponded to the lowest aging temperature and it is associated with a fine continuous precipitation, while the lowest hardness peak was detected at the highest aging temperature and it is attributed to the rapid coarsening process of both precipitations.

Fig. 14. Aging curves for 100, 200 and 300 °C.

#### **3. Conclusion**

This chapter showed three applications of SEM characterization for the analysis of different phase transformations in ferrous and nonferrous alloys, as well as its effect on their mechanical properties. The analysis of these phase transformations enables us to characterize the growth kinetics of these transformations which can be useful either to design heat tretments in order to obtain better mechanical properties or to analyze the microstructural evolution in order to asses the mechanical properties of a component-inservice. Besides, it was shown that the SEM characterization parameters can be used along with the phase transformation theories permitting a better understanding of the transformation behavior in materials after heating.

#### **4. Acknowledgment**

The authors wish to acknowledge the financial support from Instituto Politecnico Nacional (ESIQIE), SIP-IPN and CONACYT 100584.

#### **5. References**

350 Scanning Electron Microscopy

In summary, the microstructural evolution and growth kinetics were studied in an isothermally-aged Mg-8.5Al-0.5Zn-0.2Mn (wt%) alloy and the growth kinetics of cellular precipitation was evaluated using the Johnson-Mehl-Avrami-Kolmogorov equation analysis (Cahn, 1975), which gives a time exponent close to 1. This value confirms that cellular precipitation takes place on the saturation sites corresponding to grain boundaries. Additionally, the activation energy for the cellular precipitation was determined to be about 64.6 kJ mol-1. This also indicates a grain boundary diffusion process. The variation of cellular spacing with temperature follows the behavior expected by Turnbull theory. The highest hardness peak corresponded to the lowest aging temperature and it is associated with a fine continuous precipitation, while the lowest hardness peak was detected at the highest aging temperature and it is attributed to the rapid coarsening process of both precipitations.

This chapter showed three applications of SEM characterization for the analysis of different phase transformations in ferrous and nonferrous alloys, as well as its effect on their mechanical properties. The analysis of these phase transformations enables us to characterize the growth kinetics of these transformations which can be useful either to design heat tretments in order to obtain better mechanical properties or to analyze the microstructural evolution in order to asses the mechanical properties of a component-inservice. Besides, it was shown that the SEM characterization parameters can be used along with the phase transformation theories permitting a better understanding of the

Fig. 14. Aging curves for 100, 200 and 300 °C.

transformation behavior in materials after heating.

**3. Conclusion** 


**18** 

*Japan* 

**Cutting Mechanism of** 

Junsuke Fujiwara *Osaka University,* 

**Sulfurized Free-Machining Steel** 

In order to improve efficiency of cutting process in production industry, development of new steel which has good machinablity is desired. The work material which has good surface roughness, easy breakable chip and small tool wear as the good machinablity is expected. And the free-machining steel was developed owning to adding elements which could make the machinablity better. Of all others, leaded free-machining steel and sulfurized free-machining steel are famous. The leaded free-machining steel and sulfurized free-machining steel are well used in the production industry. However the use of the leaded free-machining steel is limited from an environmental problem. In order to develop new environmental friendly free-machining steel, it is necessary to find out the behavior of the inclusion in the work material for the improvement of the machining performance.

There are a lot of studies about the behavior of the inclusion in the free-machining steel (Narutaki et al., 1987), (Yaguchi, 1991), (Usui et al., 1980). There are some papers about the role of the lead and the manganese sulfide which are the representative inclusions. The Pb inclusion acts as lubricant and reduces cutting resistance (Akazawa, 1997). As the MnS is harder than steel, the MnS acts as an internal stress concentration source when the work material reforms into a chip at the cutting edge. And the MnS produced the micro-cracks at shear deformation zone. This is the cause that the shear area became small and reduces the cutting stress (Yamamoto, 1971). Although these results are almost reasonable, we must think over the role of the inclusion again in order to produce new free-machining steel. The experiment was carried out to find out the mechanism of the sulfurized inclusion on the machinablity, using some kinds of steels which have different size of the inclusion. The observation of the deformation behavior near the cutting edge was carried out to investigate

In this experiment, two kinds of the sulfurized free-machining steels (Steel A and Steel B) which have different size of the inclusion were used. Figure 1 show optical microphotographs of microstructure and size distribution of MnS in the Steel A and Steel B, respectively. The area fraction of equivalent circle diameter of the inclusions was also shown in these figures. The steel A contains larger inclusions than the steel B. These

**1. Introduction** 

the effect of the inclusion in detail.

**2. Experimental method** 


## **Cutting Mechanism of Sulfurized Free-Machining Steel**

Junsuke Fujiwara *Osaka University, Japan* 

## **1. Introduction**

352 Scanning Electron Microscopy

Soriano-Vargas, O.; Saucedo-Muñoz, M.L., Lopez-Hirata, V.M. & Paniagua Mercado, A.

*Mater. Trans. JIM*, Vol. 51, No. x, (Month, 2010), pp.442-446, ISSN 1345-9678 Saucedo-Muñoz, M.L.;, Watanabe Y., Shoji T. & Takahashi H. (2001), Effect of

pp. 693-700. ISSN 011-2275

(2010). Coarsening of β´ Precipitates in an Isothermally-Aged Fe75-Ni10-Al15 Alloy,

Microstructure Evolution on Fracture Toughness in Isothermally Aged Austenitic Stainless Steels for Cryogenic Applications*. Journal of Cryogenic Materials*, Vol. 40,

> In order to improve efficiency of cutting process in production industry, development of new steel which has good machinablity is desired. The work material which has good surface roughness, easy breakable chip and small tool wear as the good machinablity is expected. And the free-machining steel was developed owning to adding elements which could make the machinablity better. Of all others, leaded free-machining steel and sulfurized free-machining steel are famous. The leaded free-machining steel and sulfurized free-machining steel are well used in the production industry. However the use of the leaded free-machining steel is limited from an environmental problem. In order to develop new environmental friendly free-machining steel, it is necessary to find out the behavior of the inclusion in the work material for the improvement of the machining performance.

> There are a lot of studies about the behavior of the inclusion in the free-machining steel (Narutaki et al., 1987), (Yaguchi, 1991), (Usui et al., 1980). There are some papers about the role of the lead and the manganese sulfide which are the representative inclusions. The Pb inclusion acts as lubricant and reduces cutting resistance (Akazawa, 1997). As the MnS is harder than steel, the MnS acts as an internal stress concentration source when the work material reforms into a chip at the cutting edge. And the MnS produced the micro-cracks at shear deformation zone. This is the cause that the shear area became small and reduces the cutting stress (Yamamoto, 1971). Although these results are almost reasonable, we must think over the role of the inclusion again in order to produce new free-machining steel. The experiment was carried out to find out the mechanism of the sulfurized inclusion on the machinablity, using some kinds of steels which have different size of the inclusion. The observation of the deformation behavior near the cutting edge was carried out to investigate the effect of the inclusion in detail.

## **2. Experimental method**

In this experiment, two kinds of the sulfurized free-machining steels (Steel A and Steel B) which have different size of the inclusion were used. Figure 1 show optical microphotographs of microstructure and size distribution of MnS in the Steel A and Steel B, respectively. The area fraction of equivalent circle diameter of the inclusions was also shown in these figures. The steel A contains larger inclusions than the steel B. These

Cutting Mechanism of Sulfurized Free-Machining Steel 355

Cutting speed 0.016 m/min Depth of cut 0.1 mm Tool SHK4 Rake angle 10° Clearance angle 17°

The Cutting forces were measured in the orthogonal cutting. These results are shown in Fig. 3. On the whole, the cutting force in the Steel A was bigger than that in the Steel B. The cutting force in the Steel A was more stable than that in the Steel B. This fact led smooth

Fig. 2. Method of orthogonal cutting

Table 2. Cutting conditions in orthogonal cutting

**3. Experimental results and discussions** 

(a) Steel A (b) Steel B

Fig. 3. Cutting forces in orthogonal cutting

**3.1 Orthogonal cutting** 

surface roughness.

sulfurized free-machining steels contain 0.42% S, and chemical compositions of these materials are almost the same as shown in Table 1. The inclusions tend to be slender parallel to rolling direction.

In an orthogonal cutting at low speed, the cutting forces were measured. The cutting width of the work material was 2 mm. The surface of work materials was polished to observe deformation of the inclusions. An orthogonal cutting was performed using table feeding system of a horizontal milling machine as shown in Fig. 2. Table 2 shows cutting conditions in the orthogonal cutting. The cutting speed was 16mm/min and the depth of cut was 0.1mm. The tool material was high speed steel and its rake angle was 10 degree.

Fig. 1. Optical micrographs of microstructure and size distribution of MnS


Table 1. Chemical compositions of work materials

sulfurized free-machining steels contain 0.42% S, and chemical compositions of these materials are almost the same as shown in Table 1. The inclusions tend to be slender

In an orthogonal cutting at low speed, the cutting forces were measured. The cutting width of the work material was 2 mm. The surface of work materials was polished to observe deformation of the inclusions. An orthogonal cutting was performed using table feeding system of a horizontal milling machine as shown in Fig. 2. Table 2 shows cutting conditions in the orthogonal cutting. The cutting speed was 16mm/min and the depth of cut was

(a) Steel A

Equivalent circle diameter μm

Equivalent circle diameter μm

Area fraction

 %

Fig. 1. Optical micrographs of microstructure and size distribution of MnS

Table 1. Chemical compositions of work materials

(b) Steel B

Area fraction

 %

Mass % C Si Mn S Al O2 Steel A 0.03 0.01 1.44 0.42 0.001 0.0175 Steel B 0.03 0.01 1.7 0.43 0.001 0.0044

0.1mm. The tool material was high speed steel and its rake angle was 10 degree.

parallel to rolling direction.

Fig. 2. Method of orthogonal cutting


Table 2. Cutting conditions in orthogonal cutting

## **3. Experimental results and discussions**

#### **3.1 Orthogonal cutting**

The Cutting forces were measured in the orthogonal cutting. These results are shown in Fig. 3. On the whole, the cutting force in the Steel A was bigger than that in the Steel B. The cutting force in the Steel A was more stable than that in the Steel B. This fact led smooth surface roughness.

Fig. 3. Cutting forces in orthogonal cutting

Cutting Mechanism of Sulfurized Free-Machining Steel 357

Cutting speed 0.27 mm/s Depth of cut 20~50 μm Tool SKH4

Rake angle 6° Clearance angle 3°

Test piece

Cutting direction

10

1

10

Figure 7 shows the sequential photographs during cutting of the Steel A. As the Steel A has the spindle shaped inclusions, the inclusion MnS which was extended perpendicular to the cutting direction can be found. This inclusion turned in counterclockwise and broke to several pieces around the shear zone. These pieces create voids around them, and flowed to the chip in the direction parallel to the shear plane. As shown in this figure, the void was formed at the upper of the inclusion and the micro-crack was formed along the primary

Figure 8 shows the sequential photographs during cutting of the Steel B. The inclusion of higher aspect ratio than that in the Steel A can be found. As the Steel B had long slender inclusions, this inclusion broke into smaller pieces than that in the Steel A. These pieces create very small voids between them. The inclusions in the Steel B are well dispersed, so these very small voids are created at various places in the work material. It causes the

It is very important to know strain and stress distribution in shearing zone. The sequential images could be taken during micro-cutting in SEM. Using with image processing, the strain increase and stress distribution around MnS can be calculated. That is to say, as comparison with two sequential SEM images after micro movement of the tool, the displacement within observed zone could be measured by tracing a same point. Moreover the strain increase and

In order to measure the displacement from the sequential SEM images, PIV (Particle Image Velocimetry) method was used (Raffel etal., 2007). The moving distance was calculated from gray level pattern between SEM image A at t in time and SEM image B at t+△t in time as

Table 3. Cutting conditions in SEM

Inclusions

shear plane.

shown in Fig.9.

reduction of the cutting force.

**3.3 Image analysis for stress distribution** 

stress distribution could be calculated from the displacement.

Fig. 6. Work material for micro-cutting in SEM

In order to investigate the flow state of around the shear zone, a quick stop test was carried out during the orthogonal cutting. Figure 4 shows the enlarged photographs around the shear zone. In case of the Steel A, the large crack parallel to the shear plane was found. In case of Steel B, the chip is thin, and there are small cracks near the rake face.

Fig. 4. Microphotographs of partially formed chip

### **3.2 Micro-cutting in SEM**

A small orthogonal cutting equipment as shown in Fig. 5 was mounted into the Scanning Electrical Microscope (SEM) (Iwata 1977). The deformation behavior around the shear zone was observed in detail with the SEM. An example of the micro-cutting in SEM is also shown in this figure. The cutting speed was 0.27 mm/s and the depth of cut was 20 - 50 μm. Table 3 shows the cutting conditions. The work material was cut from the test piece as shown in Fig.6.

(a) Cutting equipment (b) Micro-cutting

Fig. 5. Cutting equipment in SEM and micro-cutting


#### Table 3. Cutting conditions in SEM

356 Scanning Electron Microscopy

In order to investigate the flow state of around the shear zone, a quick stop test was carried out during the orthogonal cutting. Figure 4 shows the enlarged photographs around the shear zone. In case of the Steel A, the large crack parallel to the shear plane was found. In

300mm 300mm

(a) Steel A (b) Steel B

A small orthogonal cutting equipment as shown in Fig. 5 was mounted into the Scanning Electrical Microscope (SEM) (Iwata 1977). The deformation behavior around the shear zone was observed in detail with the SEM. An example of the micro-cutting in SEM is also shown in this figure. The cutting speed was 0.27 mm/s and the depth of cut was 20 - 50 μm. Table 3 shows the cutting conditions. The work material was cut from the test piece as shown in

(a) Cutting equipment (b) Micro-cutting

case of Steel B, the chip is thin, and there are small cracks near the rake face.

Fig. 4. Microphotographs of partially formed chip

Fig. 5. Cutting equipment in SEM and micro-cutting

**3.2 Micro-cutting in SEM** 

Fig.6.

Fig. 6. Work material for micro-cutting in SEM

Figure 7 shows the sequential photographs during cutting of the Steel A. As the Steel A has the spindle shaped inclusions, the inclusion MnS which was extended perpendicular to the cutting direction can be found. This inclusion turned in counterclockwise and broke to several pieces around the shear zone. These pieces create voids around them, and flowed to the chip in the direction parallel to the shear plane. As shown in this figure, the void was formed at the upper of the inclusion and the micro-crack was formed along the primary shear plane.

Figure 8 shows the sequential photographs during cutting of the Steel B. The inclusion of higher aspect ratio than that in the Steel A can be found. As the Steel B had long slender inclusions, this inclusion broke into smaller pieces than that in the Steel A. These pieces create very small voids between them. The inclusions in the Steel B are well dispersed, so these very small voids are created at various places in the work material. It causes the reduction of the cutting force.

## **3.3 Image analysis for stress distribution**

It is very important to know strain and stress distribution in shearing zone. The sequential images could be taken during micro-cutting in SEM. Using with image processing, the strain increase and stress distribution around MnS can be calculated. That is to say, as comparison with two sequential SEM images after micro movement of the tool, the displacement within observed zone could be measured by tracing a same point. Moreover the strain increase and stress distribution could be calculated from the displacement.

In order to measure the displacement from the sequential SEM images, PIV (Particle Image Velocimetry) method was used (Raffel etal., 2007). The moving distance was calculated from gray level pattern between SEM image A at t in time and SEM image B at t+△t in time as shown in Fig.9.

Cutting Mechanism of Sulfurized Free-Machining Steel 359

Fig. 8. Deformation behavior of long slender MnS inclusion (Steel B)

Fig. 7. Deformation behavior of large spindle shaped MnS inclusion (Steel A)

Fig. 7. Deformation behavior of large spindle shaped MnS inclusion (Steel A)

Fig. 8. Deformation behavior of long slender MnS inclusion (Steel B)

Cutting Mechanism of Sulfurized Free-Machining Steel 361

Yield strength MPa 522 560 Tensile strength MPa 525 563

Elongation % 13.2 12.0

Reduction of area % 52.1 48.0

The strain increase distribution was calculated from the two sequential SEM Images as shown in Fig. 7. Figure 11 shows the strain increase distribution in micro-cutting of the Steel A. The moving distance of two images was 1.8 μm. As shown in Fig 11 (d), the shear strain increase was large in shear zone but another large strain increase was found around MnS.

Fig. 11. SEM image and strain increase distribution in micro-cutting of Steel A

upper of the MnS because of the stress concentration.

The stress distribution was calculated from the strain increase distribution. Figure 12 shows the stress distribution in micro-cutting of the Steel A. It shows that the stress was big at the

The strain increase distribution was calculated from the two sequential SEM Images as shown in Fig. 8. Figure 13 shows the strain increase distribution in micro-cutting of the Steel B. The moving distance of two images was 4.3 μm. As shown in Fig 13 (d), the strain increase along shear zone was large in and no large strain increase was found around MnS.

Table 4. Mechanical properties of work materials

Steel A Steel B

The strain and stress distribution was calculated from the displacement increase measured with the PIV method using with FEM as shown in Fig.10 (Usui et al., 1990). In the calculation, the mechanical properties as shown in Table 4 were used.

SEM image A at t in time

Fig. 9. Outline of correlation method in particle image velocimetry

Fig. 10. Model of triangulated zone for FEM calculation

The strain and stress distribution was calculated from the displacement increase measured with the PIV method using with FEM as shown in Fig.10 (Usui et al., 1990). In the

P

Mean moving distance

SEM image B at t+⊿t in time

1 2 7

5 6・ ・ ・ ・

> Gray value in the vicinity of point Q *gi*

Q

calculation, the mechanical properties as shown in Table 4 were used.

Reference zone

P

1 2 7

5 6・ ・ ・ ・

SEM image A at t in time

Searching zone

Fig. 9. Outline of correlation method in particle image velocimetry

Gray value in the vicinity of point P *fi*

Fig. 10. Model of triangulated zone for FEM calculation


Table 4. Mechanical properties of work materials

The strain increase distribution was calculated from the two sequential SEM Images as shown in Fig. 7. Figure 11 shows the strain increase distribution in micro-cutting of the Steel A. The moving distance of two images was 1.8 μm. As shown in Fig 11 (d), the shear strain increase was large in shear zone but another large strain increase was found around MnS.

Fig. 11. SEM image and strain increase distribution in micro-cutting of Steel A

The stress distribution was calculated from the strain increase distribution. Figure 12 shows the stress distribution in micro-cutting of the Steel A. It shows that the stress was big at the upper of the MnS because of the stress concentration.

The strain increase distribution was calculated from the two sequential SEM Images as shown in Fig. 8. Figure 13 shows the strain increase distribution in micro-cutting of the Steel B. The moving distance of two images was 4.3 μm. As shown in Fig 13 (d), the strain increase along shear zone was large in and no large strain increase was found around MnS.

Cutting Mechanism of Sulfurized Free-Machining Steel 363

The stress distribution was calculated from the strain increase distribution. Figure 14 shows the stress distribution in micro-cutting of the Steel B. As the Steel B had long slender inclusions, the inclusion MnS broke into small pieces and the stress distributed along the

Consequently, in the Steel A which has large spindle type MnS, the micro-crack is easily formed. As this micro-crack affects the breakage of a build-up edge (BUE) and chip, the BUE could not become big and the good finish surface roughness was gained in the Steel A. As shown in Fig. 15, large micro-crack affected the separation between the chip and the BUE.

shear plane. There is little stress concentration around the MnS.

Fig. 14. Stress distribution in micro-cutting of Steel A

Fig. 15. Effect of spindle-type MnS to suppress BUE growth in the vicinity of BUE

Built-up edge

Tool

Chip

Crack

MnS

MnS

Shear zone

Fig. 12. Stress distribution in micro-cutting of Steel A

Fig. 13. SEM image and strain increase distribution in micro-cutting of Steel A

(a) σx (b) σy

(c) τxy (d) σ

20µm

Fig. 12. Stress distribution in micro-cutting of Steel A

Fig. 13. SEM image and strain increase distribution in micro-cutting of Steel A

The stress distribution was calculated from the strain increase distribution. Figure 14 shows the stress distribution in micro-cutting of the Steel B. As the Steel B had long slender inclusions, the inclusion MnS broke into small pieces and the stress distributed along the shear plane. There is little stress concentration around the MnS.

Consequently, in the Steel A which has large spindle type MnS, the micro-crack is easily formed. As this micro-crack affects the breakage of a build-up edge (BUE) and chip, the BUE could not become big and the good finish surface roughness was gained in the Steel A. As shown in Fig. 15, large micro-crack affected the separation between the chip and the BUE.

Fig. 14. Stress distribution in micro-cutting of Steel A

Fig. 15. Effect of spindle-type MnS to suppress BUE growth in the vicinity of BUE

Cutting Mechanism of Sulfurized Free-Machining Steel 365

In quick stop test, the chips in the vicinity of the tool face and machined surface was observed as shown in Fig. 17. In case of the Steel A which contains large spindle shaped inclusions, the BUE could not be found. And machined surface had good surface roughness. In case of the Steel B which contains small slender inclusions, the BUE could be found on the rake face of the chip. There were many tears on the machined surface. The BUE partially separated and they leave on the machined surface. As a result, the surface roughness became bad. In this experiment, it was clear that the larger inclusions could reduce the

1. In machining of sulfurized free-machining, some inclusions creates voids around them, some break to several pieces depending on their conditions around the shear zone.

Fig. 17. Chip and finish surface in quick stop test

The main results obtained are as follows.

2. The larger inclusions can reduce the formation of the BUE.

formation of the BUE.

**4. Conclusions** 

#### **3.4 Quick stop test during turning.**

A quick stop experiment during turning was carried out with the quick stop system which was attached to a conventional lathe. Figure 16 shows the equipment of the quick stop test. The tool was rotated down at the moment when pulling a pin which fixed the tool and the cutting state was stopped quickly. The deformation behavior around the shear zone was observed from the workpiece with a chip. The cutting speed was 62 m/min and the depth of cut was 0.2 mm. Table 5 shows the cutting conditions.

Fig. 16. Equipment of quick stop test


Table 5. Cutting condition in quick stop test

A quick stop experiment during turning was carried out with the quick stop system which was attached to a conventional lathe. Figure 16 shows the equipment of the quick stop test. The tool was rotated down at the moment when pulling a pin which fixed the tool and the cutting state was stopped quickly. The deformation behavior around the shear zone was observed from the workpiece with a chip. The cutting speed was 62 m/min and the depth of

**3.4 Quick stop test during turning.** 

Fig. 16. Equipment of quick stop test

Table 5. Cutting condition in quick stop test

Cutting speed 62 m/min Depth of cut 0.2 mm

Rake angle 20° Clearance angle 6°

Tool Cemented carbide P10

cut was 0.2 mm. Table 5 shows the cutting conditions.

Fig. 17. Chip and finish surface in quick stop test

In quick stop test, the chips in the vicinity of the tool face and machined surface was observed as shown in Fig. 17. In case of the Steel A which contains large spindle shaped inclusions, the BUE could not be found. And machined surface had good surface roughness. In case of the Steel B which contains small slender inclusions, the BUE could be found on the rake face of the chip. There were many tears on the machined surface. The BUE partially separated and they leave on the machined surface. As a result, the surface roughness became bad. In this experiment, it was clear that the larger inclusions could reduce the formation of the BUE.

## **4. Conclusions**

The main results obtained are as follows.


**19** 

*Germany* 

**Catalyst Characterization with FESEM/EDX** 

Ag catalysts are of outstanding importance in the field of heterogeneous catalysis. Optimum distribution and morphology of the Ag particles must be ensured by controlled, tailored catalyst synthesis. Hence, there is a growing demand for the characterization of Agdispersed fine particle systems requiring high-resolution surface observation of particles down to a few tens of nanometers and elemental analysis by *field emission scanning electron microscopy and energy-dispersive X-ray spectrometry* (FESEM/EDX). It is beneficial to characterize the particle morphology by comparison of different imaging methods like secondary electron (SE)-, backscattered electron (BSE)- and transmitted electron (TE) detection. In scanning electron microscopy surface topography becomes visible due to the dependency of the SE yield on the angle of electron incidence. Together with the large depth of field informative images of irregularly shaped particle structures are obtained. The increased BSE yield of high atomic numbers (Z) such as Ag catalysts and promoters (e.g. Cs) compared to a low-density matrix and the high penetration depth of 20-30 keV electrons also allows imaging and analysis of inclusions that would be obscured at low beam energies. Both SE and BSE detectors, in particular at low beam voltages, can additionally reveal interesting surface features of fine Ag particles. A well-known example for a Ag catalyzed reaction is the α-Al2O3 supported Ag-catalyzed epoxidation of 1,3-butadiene to 3,4-epoxybutene. The electrophilic addition of oxygen across the carbon-carbon double bond of 1,3-butadiene, resulting in a three-member ring structure that can undergo further chemical transformations to oxygenated products, such as ketones, alcohols, and ethers. Supported silver catalysts have been shown to epoxidize olefins with nonallylic hydrogen when an alkali promotor is doped on the surface. Thus, the direct kinetically controlled oxidation to the corresponding epoxide is preferred. The guiding hypothesis for this partially oxidation is that surface oxametallacycles are key intermediates for epoxidation on promoted Ag catalysts. Therefore, the preparative application of Ag and promoters (Cs, Ba) on the catalyst support material is of great importance. Another important aspect is sintering of Ag particles which may reduce the catalytically active surface and decreases the overall reaction performance. For this research, catalysts are produced by sequential impregnation of two mineralogically differing support materials (SC13, SLA2) with an

**1. Introduction** 

**by the Example of Silver-Catalyzed** 

**Epoxidation of 1,3-Butadiene** 

Eckhard Dinjus and Michael Zimmermann

Thomas N. Otto, Wilhelm Habicht,

*Karlsruhe Institute of Technology, IKFT,* 

#### **5. References**


## **Catalyst Characterization with FESEM/EDX by the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene**

Thomas N. Otto, Wilhelm Habicht, Eckhard Dinjus and Michael Zimmermann *Karlsruhe Institute of Technology, IKFT, Germany* 

## **1. Introduction**

366 Scanning Electron Microscopy

Akazawa, T., Free cutting steels contributing to industry, *Journal of Special Steel,* Vol.46, No.5

Araki, T., Yamamoto, S., Machinability of Steel and Metallugical Factors, *Iron and Steel,* 

Iwata, K., Ueda, K., Shibasaka, T, Study on Micro-machining Mechanics Based on Direct-

Katayama, S., Toda, M., Hashimura, M., Growing Model of Build-up Edge in Relation to

Maekawa, K., Kubo, A., Kitagawa, T., MachinabilityAnalysis of Free-machining Steel, *Journal of Japan Society of Precision Engineering*, Vol. 57, No. 12 (1991) pp.2193-2198. Narutaki, N., Yamane, Y., Usuki, H., Yan, B., Kuwana, T., Machinability of Resulfurized

Raffel, M., Willert, C.E., Kompenhaus J., (2000) Particle Image Velocimetry, Springer, ISBN

Usui, E., (1990) *Modern Cutting Theory* (1st), Kyouritsu Shuppan, ISBN 4-320-08054-8, Tokyo. Usui, E., Obikawa, T., Shirakashi, T., Embrittle Action of Free-machining Additives, *Japan Society for Precision Engineering*, Vol.46, No.7 (July 1980), pp.849-855. Yaguchi, H., Effect of MnS Inclusion Size on Machinability of Low-Carbon Leaded

Yaguchi, H., Effect of soft assitives (Pb, Bi) on formation of Build-up edge, *Journal of Material* 

Yaguchi, H., The Role of Liquid Metal Embrittlement on the Chip Disposability of Steel, I*ron* 

SEM Observation, *Journal of Japan Society of Precision Engineering*, Vol. 43, No.3

Inhomogeneities of Steel Microstructure, *Journal of Japan Society of Precision* 

Steels under High Cutting Speed, J*ournal of Japan Society for Precision Engineering,*

Resulfurized Free-Maching Steel, J*ournal of Applied Metalworking* Vol. 4, No. 3 (1986)

**5. References** 

(May 1997), pp.6-10.

(1977) pp.311-317.

Vol.57, No.13 (Nov. 1971), pp.1912-1932.

*Engineering*, Vol. 62, No. 9 (1996) pp.1345-1349.

Vol.53, No.3 (March 1987), pp.455-466.

*Science Technology,* No. 4, (1988) pp.926-932.

*and Steel* Vol.77, No.5 (May 1991), pp.683-690.

3-540-63683-8 New York.

pp.214-220.

Ag catalysts are of outstanding importance in the field of heterogeneous catalysis. Optimum distribution and morphology of the Ag particles must be ensured by controlled, tailored catalyst synthesis. Hence, there is a growing demand for the characterization of Agdispersed fine particle systems requiring high-resolution surface observation of particles down to a few tens of nanometers and elemental analysis by *field emission scanning electron microscopy and energy-dispersive X-ray spectrometry* (FESEM/EDX). It is beneficial to characterize the particle morphology by comparison of different imaging methods like secondary electron (SE)-, backscattered electron (BSE)- and transmitted electron (TE) detection. In scanning electron microscopy surface topography becomes visible due to the dependency of the SE yield on the angle of electron incidence. Together with the large depth of field informative images of irregularly shaped particle structures are obtained. The increased BSE yield of high atomic numbers (Z) such as Ag catalysts and promoters (e.g. Cs) compared to a low-density matrix and the high penetration depth of 20-30 keV electrons also allows imaging and analysis of inclusions that would be obscured at low beam energies. Both SE and BSE detectors, in particular at low beam voltages, can additionally reveal interesting surface features of fine Ag particles. A well-known example for a Ag catalyzed reaction is the α-Al2O3 supported Ag-catalyzed epoxidation of 1,3-butadiene to 3,4-epoxybutene. The electrophilic addition of oxygen across the carbon-carbon double bond of 1,3-butadiene, resulting in a three-member ring structure that can undergo further chemical transformations to oxygenated products, such as ketones, alcohols, and ethers. Supported silver catalysts have been shown to epoxidize olefins with nonallylic hydrogen when an alkali promotor is doped on the surface. Thus, the direct kinetically controlled oxidation to the corresponding epoxide is preferred. The guiding hypothesis for this partially oxidation is that surface oxametallacycles are key intermediates for epoxidation on promoted Ag catalysts. Therefore, the preparative application of Ag and promoters (Cs, Ba) on the catalyst support material is of great importance. Another important aspect is sintering of Ag particles which may reduce the catalytically active surface and decreases the overall reaction performance. For this research, catalysts are produced by sequential impregnation of two mineralogically differing support materials (SC13, SLA2) with an

Catalyst Characterization with FESEM/EDX by

D2, SC13, 45/63 µm, 5 % Ag, 1500 ppm Cs

D4, SC13, 45/63 µm, 10 % Ag, 1500 ppm Cs

D6 SC13, 45/63 µm, 20 % Ag, 1500 ppm Cs

**2.3 Electron interactions with the specimen** 

interactions with the specimen, namely the generation of

with specimen and the according electron detectors.

D1, SC13, 45/63 µm, 5 % Ag

D3, SC13, 45/63 µm, 10 % Ag

D5, SC13, 45/63 µm, 20 % Ag

• secondary electrons (SE) • backscattered electrons (BSE) • transmitted electrons (TE) • characteristic X-rays

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 369

Scanning electron microscopy in combination with energy-dispersive X-ray spectrometry (SEM/EDX) is a well-established and versatile method for the characterization of heterogeneous catalysts, especially Ag catalysts. They are predominantly composed of small metal particles dispersed onto a supporting material, generally a chemically inert oxide. Information about particle size distribution, deposition on the substrate surface, and elemental compositions can be obtained easily. In FESEM, a high brightness Schottky-type field emission (FE) cathode with its small beam diameter (spot size) enables imaging of features with high resolution and high contrast down to the nanoscale even on bulk substrates. Coincidently, the element composition of the specimen is available by excitation of inner shell electrons to collect characteristic X-rays with sufficient intensity for analytical information. Due to the high depth of field (D ≈ d2 / λ, λ20 keV = 0.0086 nm), where D is the depth of field, d is the apparent resolution, and λ is the electron wavelength, impressive images of differently shaped catalyst particles can be generated [5]. In the SEM, the primary electron beam creates different types of electron interactions, while penetrating the specimen. For this research, however, we utilize the following 4 important electron

Secondary and backscattered electrons are essentially for the topographical imaging of the specimen surface and therefore are described in more detail in chapter 2.4. Transmitted electrons (TE) interact less with the specimen. The specimen appears more or less "transparent" for electrons, depending on the *thickness and density* of the specimen. In energy-dispersive X-ray Spectroscopy (EDX), the X-rays are produced by inelastic scattering of primary beam electrons with bound inner shell electrons during their penetration into the matter. Subsequent deexcitation by transition of outer shell electrons to the inner shell vacancy results in emission of an element specific X-ray quantum [6]. These characteristic Xrays are essential for the determination of the elemental composition of a specimen. The electron penetration depth depends mainly on the primary beam energy and the target composition. The X-ray production range also depends on the critical excitation energy of the specific X-ray line (e.g. Kα or Lα) and is always smaller than the electron range. The electron range is the travelling distance from the primary beam electron incident at the target surface to the point where the electrons lost their energy by multiple interaction processes within the material. Figure 1 illustrates the different kinds of electron interactions

MZ06, SLA 92, 45/63 µm, 5 % Ag, 1500 ppm Cs MZ09, SLA 92, 45/63 µm, 10 % Ag, 1500 ppm Cs

aqueous active component solution (AgNO3, CsNO3). The two selected Ag catalyst systems are examined using mainly FESEM/EDX. The determination of metal amount were carried out with EDX area analysis and, if necessary, supplemented by EDX-spot analysis. In some cases, characterization of Ag-distributions by EDX mappings were made. Furthermore, for the Monte Carlo (MC)- simulations x-ray line scans of two different SEM preparation techniques (bulk specimen, thin-film supported specimen) were performed to underpin the relationships impressively. Temperature-programmed O2 desorption (O2-TPD) as well as N2 sorption (BET) measurements are important analysis methods in catalysis chemistry, too. Both methods are used to support the FESEM/EDX investigation to provide complementary contributions with regard to the Ag distribution and the properties of the carrier surfaces.

## **2. Characterization and measurement methods**

#### **2.1 Ag catalyst systems**

Huge varieties of materials are used in the preparation of heterogeneous catalysts, especially industrial catalysts. Catalysts can be divided into three groups of constituents, namely active catalytic agents, promoters, and supports [1]. Catalysts are manufactured by various methods, such as wet impregnation, leaching, drying and calcination. The major components of the catalyst system are the catalyst support (bulk material, e.g. Al2O3, TiO2, SiO2), which might influence the catalytic activity of the active components (metal-support interactions, MSI) [2] and the active metal e.g. Ag, Pd, Pt is the active agent. Increasing the surface area of the active agent is one function of the support. Maintaining a high dispersion of the active components is the other function. α-Al2O3 with its small specific surface area has proved to be a wear-resistant carrier material for Ag and to be highly suited for the selective oxidation of 1,3-butadiene [3].

#### **2.2 Catalyst preparation**

The Ag catalysts for this research are produced by sequential incipient wetness impregnation of two mineralogically different carrier materials with an aqueous solution of AgNO3 and CsNO3 as active components. The following catalyst supports are applied:


Dry SC13 and SLA92 are pre-sieved and subjected to wet sieving (Retsch laboratory sieves manufactured according to DIN 3310, mesh width 0.045 mm – 0.063 mm). The sieve fractions are dried at 120°C in a circulating air oven for 5 h. The desired active component solutions AgNO3(aq) and CsNO3(aq) are applied sequentially to the carrier materials and subjected to an ultrasound bath for 0.5 min (Sonorex RK 100H) [4]. The precursors are dried in a circulating air oven at 40°C for 5 h and then oxidized with 100 % O2 (4.8) at 250 °C for 10 min (4000 mlSTP h-1). A reduction with 100 % H2 (6.0) follows at 200 °C for 10 min (1000 mlSTP h-1). The concentrations of the active metal (oxidation number ± 0) components after conditioning are given by:

aqueous active component solution (AgNO3, CsNO3). The two selected Ag catalyst systems are examined using mainly FESEM/EDX. The determination of metal amount were carried out with EDX area analysis and, if necessary, supplemented by EDX-spot analysis. In some cases, characterization of Ag-distributions by EDX mappings were made. Furthermore, for the Monte Carlo (MC)- simulations x-ray line scans of two different SEM preparation techniques (bulk specimen, thin-film supported specimen) were performed to underpin the relationships impressively. Temperature-programmed O2 desorption (O2-TPD) as well as N2 sorption (BET) measurements are important analysis methods in catalysis chemistry, too. Both methods are used to support the FESEM/EDX investigation to provide complementary contributions with regard to the Ag distribution and the properties of the carrier surfaces.

Huge varieties of materials are used in the preparation of heterogeneous catalysts, especially industrial catalysts. Catalysts can be divided into three groups of constituents, namely active catalytic agents, promoters, and supports [1]. Catalysts are manufactured by various methods, such as wet impregnation, leaching, drying and calcination. The major components of the catalyst system are the catalyst support (bulk material, e.g. Al2O3, TiO2, SiO2), which might influence the catalytic activity of the active components (metal-support interactions, MSI) [2] and the active metal e.g. Ag, Pd, Pt is the active agent. Increasing the surface area of the active agent is one function of the support. Maintaining a high dispersion of the active components is the other function. α-Al2O3 with its small specific surface area has proved to be a wear-resistant carrier material for Ag and to be highly suited for the

The Ag catalysts for this research are produced by sequential incipient wetness impregnation of two mineralogically different carrier materials with an aqueous solution of AgNO3 and CsNO3 as active components. The following catalyst supports are applied:

• SC13 (Almatis) mineralogical: 80 % alpha-Al2O3, 20 % γ-Al2O3, Chemical composition:

• SLA92 (Almatis) mineralogical: Main phase Ca hexa aluminate (CA6) hibonite, secondary phase alpha-Al2O3, chemical composition: 91% Al2O3, 8.5 % CaO, 0.04 %

Dry SC13 and SLA92 are pre-sieved and subjected to wet sieving (Retsch laboratory sieves manufactured according to DIN 3310, mesh width 0.045 mm – 0.063 mm). The sieve fractions are dried at 120°C in a circulating air oven for 5 h. The desired active component solutions AgNO3(aq) and CsNO3(aq) are applied sequentially to the carrier materials and subjected to an ultrasound bath for 0.5 min (Sonorex RK 100H) [4]. The precursors are dried in a circulating air oven at 40°C for 5 h and then oxidized with 100 % O2 (4.8) at 250 °C for 10 min (4000 mlSTP h-1). A reduction with 100 % H2 (6.0) follows at 200 °C for 10 min (1000 mlSTP h-1). The concentrations of the active metal (oxidation number ± 0) components after

99 % Al2O3, 0.05 % CaO, 0.03 % Fe2O3, 0.02% SiO2, 0.4 % Na2O.

**2. Characterization and measurement methods** 

**2.1 Ag catalyst systems** 

selective oxidation of 1,3-butadiene [3].

Fe2O3, 0.07 % SiO2, 0.4 % Na2O.

**2.2 Catalyst preparation** 

conditioning are given by:

D1, SC13, 45/63 µm, 5 % Ag D2, SC13, 45/63 µm, 5 % Ag, 1500 ppm Cs D3, SC13, 45/63 µm, 10 % Ag D4, SC13, 45/63 µm, 10 % Ag, 1500 ppm Cs D5, SC13, 45/63 µm, 20 % Ag D6 SC13, 45/63 µm, 20 % Ag, 1500 ppm Cs MZ06, SLA 92, 45/63 µm, 5 % Ag, 1500 ppm Cs MZ09, SLA 92, 45/63 µm, 10 % Ag, 1500 ppm Cs

#### **2.3 Electron interactions with the specimen**

Scanning electron microscopy in combination with energy-dispersive X-ray spectrometry (SEM/EDX) is a well-established and versatile method for the characterization of heterogeneous catalysts, especially Ag catalysts. They are predominantly composed of small metal particles dispersed onto a supporting material, generally a chemically inert oxide. Information about particle size distribution, deposition on the substrate surface, and elemental compositions can be obtained easily. In FESEM, a high brightness Schottky-type field emission (FE) cathode with its small beam diameter (spot size) enables imaging of features with high resolution and high contrast down to the nanoscale even on bulk substrates. Coincidently, the element composition of the specimen is available by excitation of inner shell electrons to collect characteristic X-rays with sufficient intensity for analytical information. Due to the high depth of field (D ≈ d2 / λ, λ20 keV = 0.0086 nm), where D is the depth of field, d is the apparent resolution, and λ is the electron wavelength, impressive images of differently shaped catalyst particles can be generated [5]. In the SEM, the primary electron beam creates different types of electron interactions, while penetrating the specimen. For this research, however, we utilize the following 4 important electron interactions with the specimen, namely the generation of


Secondary and backscattered electrons are essentially for the topographical imaging of the specimen surface and therefore are described in more detail in chapter 2.4. Transmitted electrons (TE) interact less with the specimen. The specimen appears more or less "transparent" for electrons, depending on the *thickness and density* of the specimen. In energy-dispersive X-ray Spectroscopy (EDX), the X-rays are produced by inelastic scattering of primary beam electrons with bound inner shell electrons during their penetration into the matter. Subsequent deexcitation by transition of outer shell electrons to the inner shell vacancy results in emission of an element specific X-ray quantum [6]. These characteristic Xrays are essential for the determination of the elemental composition of a specimen. The electron penetration depth depends mainly on the primary beam energy and the target composition. The X-ray production range also depends on the critical excitation energy of the specific X-ray line (e.g. Kα or Lα) and is always smaller than the electron range. The electron range is the travelling distance from the primary beam electron incident at the target surface to the point where the electrons lost their energy by multiple interaction processes within the material. Figure 1 illustrates the different kinds of electron interactions with specimen and the according electron detectors.

Catalyst Characterization with FESEM/EDX by

**2.5 Specimen preparation for SEM / T-SEM** 

transmitted electron measurements.

**2.6 SEM equipment** 

**2.7 O2-TPD** 

**2.8 N2-BET** 

**3. Discussion** 

of the desorbed products.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 371

The specimens are fixed with conductive tabs onto Al-stubs as bulk specimen for conventional SEM imaging. For T-SEM imaging, the samples are suspended in ethanol, and a droplet is poured onto a carbon-filmed TEM-grid (400 meshes Cu, ca. 8 nm C) for

The electron microscopic investigations are carried out with a DSM 982 Gemini, Zeiss corp., Germany, equipped with a 4-quadrant solid-state BSE detector, a high brightness in-lens SE detector and a lateral SE detector. The DSM 982 GEMINI is applied with a thermal (Schottky-type) field-emission electron source (SFE). Element-specific quantification is performed by the dedicated EDX unit, equipped with a 30 mm2 Si(Li) detector INCA

O2-TPD analysis is performed using a BELCAT-B (BEL INC. Japan) system. It is coupled with a GAM 400 quadrupole mass spectrometer (In Process Instruments, Germany) as a mass-selective detector. The samples are subjected to a preliminary in-situ treatment in an O2 / He test gas mixture. For this purpose, the sample is heated to 250°C in steps of 5 K/min and then kept at 250°C for 1 h. After this, it is cooled down to room temperature (RT). At RT, the non-adsorbed O2 is removed by rinsing with He. In the subsequent O2-TPD experiment the pre-treated samples are heated linearly in He. A thermal conductivity detector (WLD) is applied. The quadrupole mass spectrometer determines the composition

The BET surface area (S. **B**runauer, P. H. **E**mmett, and E. **T**eller) is determined with N2 (77 K) (BELSORP-mini II, BEL INC. Japan). All samples are subjected to a preliminary treatment in a vacuum at 200°C for 5 h. To determine the sorption isotherms, the amount of molecules adsorbed on the samples are measured as a function of the relative pressure p/p0. The specific surfaces are calculated from the adsorption parts of the isotherms at 77 K in the

FESEM and the dedicated EDX unit are important aids in the determination of the relationship among catalyst particle (cluster) sizes, dispersion onto support, support

X-ray analysis with reduced scattering background [7], [8].

Pentafet™, FWHM 129 eV @ MnKα (Oxford corp., England).

relative pressure range from 0.01 to 0.35 p/p0 with N2.

**3.1 Electron microscopy affecting parameters and MC simulation** 

thickness contrast is enabled. The application of the TE- detector can provide additional information on the sub-surface structure of many particles which cannot be resolved clearly in the corresponding SE and BSE images. The thin-film-supported specimen permits imaging with enhanced contrast due to an improved signal-to-noise ratio and

Fig. 1. Different types of electron interactions with specimen and related detection modes.

## **2.4 Imaging modes**

Secondary electrons (SE), backscattered electrons (BSE) and transmitted electrons (TE) are responsible for three different imaging modes in SEM. The fourth type of electron interaction is the generation of X-rays and, hence, not a typical imaging mode.

The 3 imaging modes are explained as follows:


thickness contrast is enabled. The application of the TE- detector can provide additional information on the sub-surface structure of many particles which cannot be resolved clearly in the corresponding SE and BSE images. The thin-film-supported specimen permits imaging with enhanced contrast due to an improved signal-to-noise ratio and X-ray analysis with reduced scattering background [7], [8].

## **2.5 Specimen preparation for SEM / T-SEM**

The specimens are fixed with conductive tabs onto Al-stubs as bulk specimen for conventional SEM imaging. For T-SEM imaging, the samples are suspended in ethanol, and a droplet is poured onto a carbon-filmed TEM-grid (400 meshes Cu, ca. 8 nm C) for transmitted electron measurements.

#### **2.6 SEM equipment**

370 Scanning Electron Microscopy

Inlens SE-det. Backscattered electrons

Specimen

(BSE)

*Interaction volume*

Primary electron beam

lateral SE-det.

*Transmitted electrons T-SEM*

Fig. 1. Different types of electron interactions with specimen and related detection modes.

Secondary electrons (SE), backscattered electrons (BSE) and transmitted electrons (TE) are responsible for three different imaging modes in SEM. The fourth type of electron

1. Backscattered electrons provide contrast based on atomic number (Z-contrast) and density. The BSE-detector will be used as a quick response to visualize materials heterogeneously composed and distributed. It will be applied often in combination with the EDX- unit to capture images for subsequent microanalysis. Depending on the primary beam energy, backscattered electrons are created inside the specimen by elastic scattering. They possess approximately the energy of the primary electrons. As a rule of thumb, the exit depth of the BSE is half the primary electron range. As a consequence, the imaging resolution achieved by backscattered electrons is worse than those

2. Low energetic (< 50 eV) secondary electrons are used for true surface imaging, because they are created in the vicinity of the primary beam impact on the target surface. They

3. Transmission-type images (also called T-SEM mode) are obtained by a special mounting device for TEM grids with a diode-type detector beneath. The detector is mounted like a specimen stub instead and also adjusted to the electron beam. The specimen must be sufficiently thin (< 200 nm) to permit penetration of beam electrons at 20-30 kV, which is the typical operation voltage in T-SEM mode. Imaging of mass-

interaction is the generation of X-rays and, hence, not a typical imaging mode.

are responsible for high-resolution imaging (in-lens SE detection).

*Characteristic X-rays (EDX)*

*Specimen current*

The 3 imaging modes are explained as follows:

achievable by secondary electrons.

**2.4 Imaging modes** 

The electron microscopic investigations are carried out with a DSM 982 Gemini, Zeiss corp., Germany, equipped with a 4-quadrant solid-state BSE detector, a high brightness in-lens SE detector and a lateral SE detector. The DSM 982 GEMINI is applied with a thermal (Schottky-type) field-emission electron source (SFE). Element-specific quantification is performed by the dedicated EDX unit, equipped with a 30 mm2 Si(Li) detector INCA Pentafet™, FWHM 129 eV @ MnKα (Oxford corp., England).

## **2.7 O2-TPD**

O2-TPD analysis is performed using a BELCAT-B (BEL INC. Japan) system. It is coupled with a GAM 400 quadrupole mass spectrometer (In Process Instruments, Germany) as a mass-selective detector. The samples are subjected to a preliminary in-situ treatment in an O2 / He test gas mixture. For this purpose, the sample is heated to 250°C in steps of 5 K/min and then kept at 250°C for 1 h. After this, it is cooled down to room temperature (RT). At RT, the non-adsorbed O2 is removed by rinsing with He. In the subsequent O2-TPD experiment the pre-treated samples are heated linearly in He. A thermal conductivity detector (WLD) is applied. The quadrupole mass spectrometer determines the composition of the desorbed products.

#### **2.8 N2-BET**

The BET surface area (S. **B**runauer, P. H. **E**mmett, and E. **T**eller) is determined with N2 (77 K) (BELSORP-mini II, BEL INC. Japan). All samples are subjected to a preliminary treatment in a vacuum at 200°C for 5 h. To determine the sorption isotherms, the amount of molecules adsorbed on the samples are measured as a function of the relative pressure p/p0. The specific surfaces are calculated from the adsorption parts of the isotherms at 77 K in the relative pressure range from 0.01 to 0.35 p/p0 with N2.

## **3. Discussion**

#### **3.1 Electron microscopy affecting parameters and MC simulation**

FESEM and the dedicated EDX unit are important aids in the determination of the relationship among catalyst particle (cluster) sizes, dispersion onto support, support

Catalyst Characterization with FESEM/EDX by

(Al2O3, 13).

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 373

The different penetration depths for heterogeneously composed materials will be overcome by using the T-SEM technique as it will be discussed later. As depicted in Figure 2 and Figure 3 the MC simulations elucidate the different excitation volumes for 5 keV and 20 keV beam energies. They interact with an Alox (Al2O3) support covered with an Ag cluster that is assumed to be 100 nm in thickness. It demonstrates the differences in the electron ranges as a function of beam energies and material density whereas the relative atomic number Ag (107.8) and the relative molecular weight Al2O3 (101.9) are close together. Remarkable is the beam spreading effect caused by multiple scattering in the bulk specimen, which leads to a pear-like shape of the electron trajectories (interaction volume). This will be drastically reduced by using thin supporting foils and thinning of the specimen or using sufficiently small particles,

Fig. 2. MC simulation of electron trajectories (interaction volumes) created by primary beam

Obviously, specimens that are heterogeneously composed of materials strongly different in density and atomic number require a careful X-ray analysis. Nevertheless, due to the irregular shape of the analyzed particles, the systematic error of EDX measurements is increased compared to that of a homogeneous specimen with flat and smooth surfaces. Commonly, the commercially available "standard less analysis software" are adjusted to 'ideally' smooth samples. Usually, the EDX analysis results are expressed in weight-percent (wt.-%), in which the collected X-ray counts (intensity = f(element conc.)) will be converted into concentrations by means of an evaluation program taking into account fundamental X-ray parameters and the detector efficiency. The procedure is known as "standard less analysis" and is already established by the suppliers. To reduce errors resulting through different X-ray exit and scattering angles due to the different particle sizes, irregular dispersion, or agglomerated

energy of 5 keV. Note that N is equal to the number of used trajectories (100) for MC simulation. NR corresponds to the number of BS-electrons in case of Ag (38) and Alox

depending on the beam voltage and density of the specimen (see Figure 10).

morphology and the influence of promoters. For a better understanding it makes sense to calculate the practical electron range by the Monte Carlo simulation program MOCASIM™ [9]. Additionally, the depth of the X-ray generating region (X-ray range) is estimated for the materials under consideration. It is based on an analytical expression useful for most elements and is calculated by the equation 1 of Andersen-Hasler:

$$R = \frac{0.064}{\rho} \cdot \left( E\_0^{1.68} - E\_c^{1.68} \right) \tag{1}$$

Whereby R (µm) is the X-ray range, E0 (keV) is the primary electron (beam) energy, Ec (keV) is the critical excitation energy for the characteristic (analytical) X-ray line and ρ is the density of the elements. The dimension of the primary X-ray generation volume is important for the information depth obtained. It depends on the beam energy and the Xray line chosen for the measurement [10]. The dependency of the practical electron range (penetration depth) and the X-ray range on the material density and beam voltage (beam energy) of the catalyst materials Ag, Al, and Al2O3 is depicted in Tab. 1. The higher the density of the element or specimen, the less is the penetration depth of the electrons (practical electron range) and the X-ray range (information depth). As a consequence, the material should be ideally homogeneous over the electron range to minimize errors caused by specimen heterogeneity. The critical excitation energy is determined by the specific absorption edge for the electron shell of an element from which the analytical Xray line will be emitted. Therefore, Ec is always slightly higher than the corresponding Xray line.


Table 1. Parameters affecting the electron range and X-ray range. Ec is the critical excitation energy for the corresponding analytical X-ray line. The practical (pract.) electron range is calculated by MOCASIM™.

morphology and the influence of promoters. For a better understanding it makes sense to calculate the practical electron range by the Monte Carlo simulation program MOCASIM™ [9]. Additionally, the depth of the X-ray generating region (X-ray range) is estimated for the materials under consideration. It is based on an analytical expression useful for most

Whereby R (µm) is the X-ray range, E0 (keV) is the primary electron (beam) energy, Ec (keV) is the critical excitation energy for the characteristic (analytical) X-ray line and ρ is the density of the elements. The dimension of the primary X-ray generation volume is important for the information depth obtained. It depends on the beam energy and the Xray line chosen for the measurement [10]. The dependency of the practical electron range (penetration depth) and the X-ray range on the material density and beam voltage (beam energy) of the catalyst materials Ag, Al, and Al2O3 is depicted in Tab. 1. The higher the density of the element or specimen, the less is the penetration depth of the electrons (practical electron range) and the X-ray range (information depth). As a consequence, the material should be ideally homogeneous over the electron range to minimize errors caused by specimen heterogeneity. The critical excitation energy is determined by the specific absorption edge for the electron shell of an element from which the analytical Xray line will be emitted. Therefore, Ec is always slightly higher than the corresponding X-

> Ec / keV

Al 2.7 1.487 (Kα) 1.56 5 361 304

Al2O3 3.5 - - 5 278 235

Ag 10.5 2.984 (Lα1,2) 3.35 5 93 45

Table 1. Parameters affecting the electron range and X-ray range. Ec is the critical excitation energy for the corresponding analytical X-ray line. The practical (pract.) electron range is

HV / kV

( ) 1.68 1.68 0 0.064 *R EEc* ρ

=⋅ − (1)

pract. Electron range / nm

10 1146 1085 15 2252 2192 20 3639 3585

10 884 837 15 1738 1691 20 2807 2766

10 295 245 15 579 530 20 936 888

X-ray range / nm

elements and is calculated by the equation 1 of Andersen-Hasler:

ray line.

Specimen ρ / g

cm-3

calculated by MOCASIM™.

X-ray line / keV

The different penetration depths for heterogeneously composed materials will be overcome by using the T-SEM technique as it will be discussed later. As depicted in Figure 2 and Figure 3 the MC simulations elucidate the different excitation volumes for 5 keV and 20 keV beam energies. They interact with an Alox (Al2O3) support covered with an Ag cluster that is assumed to be 100 nm in thickness. It demonstrates the differences in the electron ranges as a function of beam energies and material density whereas the relative atomic number Ag (107.8) and the relative molecular weight Al2O3 (101.9) are close together. Remarkable is the beam spreading effect caused by multiple scattering in the bulk specimen, which leads to a pear-like shape of the electron trajectories (interaction volume). This will be drastically reduced by using thin supporting foils and thinning of the specimen or using sufficiently small particles, depending on the beam voltage and density of the specimen (see Figure 10).

Fig. 2. MC simulation of electron trajectories (interaction volumes) created by primary beam energy of 5 keV. Note that N is equal to the number of used trajectories (100) for MC simulation. NR corresponds to the number of BS-electrons in case of Ag (38) and Alox (Al2O3, 13).

Obviously, specimens that are heterogeneously composed of materials strongly different in density and atomic number require a careful X-ray analysis. Nevertheless, due to the irregular shape of the analyzed particles, the systematic error of EDX measurements is increased compared to that of a homogeneous specimen with flat and smooth surfaces. Commonly, the commercially available "standard less analysis software" are adjusted to 'ideally' smooth samples. Usually, the EDX analysis results are expressed in weight-percent (wt.-%), in which the collected X-ray counts (intensity = f(element conc.)) will be converted into concentrations by means of an evaluation program taking into account fundamental X-ray parameters and the detector efficiency. The procedure is known as "standard less analysis" and is already established by the suppliers. To reduce errors resulting through different X-ray exit and scattering angles due to the different particle sizes, irregular dispersion, or agglomerated

Catalyst Characterization with FESEM/EDX by

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 375

Fig. 5. High-magnification (30,000) image of a single Al2O3 particle covered with different

Therefore a great number of particles should be included in the analyzed region to average the influence of different shaped and sized particles. As an example, a low-magnified, large number of supporting alumina (Alox = Al2O3) particles partially covered with Ag is shown in Figure 4 and produced under conditions mentioned in chapter 2.2. The magnification of 100 x is recommended for an EDX analysis to determine the amount of Ag deposited. The alumina particle diameter varies between 30 µm and 80 µm. For comparison, a highly magnified single alumina particle (sample D4) with differently sized and shaped Ag deposits is presented in Figure 5. It represents a high-magnification (30,000 x) image of a single substrate particle, covered with differently sized Ag clusters. The size range of the

Figure 6 shows an EDX line scan which is simulated by MC calculation using a bulk specimen geometry with an Ag deposit of 100 nm in thickness onto an alumina (Alox) support. It reveals the course of Ag-Lα, Al-Kα and O-Kα X-rays across the specimen. The Xray signals of Al and O dropped drastically down over the whole Ag cluster, contrarily the Ag signal becomes dominant. The lack in signal strength (intensity) is caused by absorption of the weak Al and O X- rays in the dense Ag particle. The maximum of the Ag signal, observed at the ends of the Ag deposit (edges) in the simulation is conspicuous. This is not

Figure 7 is an example of an EDX line scan across the Ag agglomerate (approximately 620 nm) on a selected surface image of sample D6, which is generated by backscattered electrons. The line scan is shown in detail in Figure 8. One can see the pronounced

sized Ag clusters, prepared on a conventional specimen stub.

deposited Ag is between 20 nm and 850 nm.

observed in practice, only in the simulation.

**3.2 EDX line scans** 

catalyst deposits, a huge population of particles should be chosen for elemental analysis of bulk specimen. To obtain e.g. the amount of metal covering the support, data collection at low magnification as demonstrated in Fig.4. should preferably be performed.

Fig. 3. MC simulation of electron trajectories created by a primary beam energy of 20 keV. Note the progress in the interaction volumes compared to Figure 2. N is equal to the number of used trajectories (100) for MC simulation. NR corresponds to the number of BSelectrons in case of Ag (32) and Alox (Al2O3, 13).

Fig. 4. Magnified (100 x) BSE image of a large population of Al2O3 particles differently covered with Ag, prepared on a conventional specimen stub.

catalyst deposits, a huge population of particles should be chosen for elemental analysis of bulk specimen. To obtain e.g. the amount of metal covering the support, data collection at low

Fig. 3. MC simulation of electron trajectories created by a primary beam energy of 20 keV. Note the progress in the interaction volumes compared to Figure 2. N is equal to the number of used trajectories (100) for MC simulation. NR corresponds to the number of BS-

Fig. 4. Magnified (100 x) BSE image of a large population of Al2O3 particles differently

covered with Ag, prepared on a conventional specimen stub.

electrons in case of Ag (32) and Alox (Al2O3, 13).

magnification as demonstrated in Fig.4. should preferably be performed.

Fig. 5. High-magnification (30,000) image of a single Al2O3 particle covered with different sized Ag clusters, prepared on a conventional specimen stub.

Therefore a great number of particles should be included in the analyzed region to average the influence of different shaped and sized particles. As an example, a low-magnified, large number of supporting alumina (Alox = Al2O3) particles partially covered with Ag is shown in Figure 4 and produced under conditions mentioned in chapter 2.2. The magnification of 100 x is recommended for an EDX analysis to determine the amount of Ag deposited. The alumina particle diameter varies between 30 µm and 80 µm. For comparison, a highly magnified single alumina particle (sample D4) with differently sized and shaped Ag deposits is presented in Figure 5. It represents a high-magnification (30,000 x) image of a single substrate particle, covered with differently sized Ag clusters. The size range of the deposited Ag is between 20 nm and 850 nm.

#### **3.2 EDX line scans**

Figure 6 shows an EDX line scan which is simulated by MC calculation using a bulk specimen geometry with an Ag deposit of 100 nm in thickness onto an alumina (Alox) support. It reveals the course of Ag-Lα, Al-Kα and O-Kα X-rays across the specimen. The Xray signals of Al and O dropped drastically down over the whole Ag cluster, contrarily the Ag signal becomes dominant. The lack in signal strength (intensity) is caused by absorption of the weak Al and O X- rays in the dense Ag particle. The maximum of the Ag signal, observed at the ends of the Ag deposit (edges) in the simulation is conspicuous. This is not observed in practice, only in the simulation.

Figure 7 is an example of an EDX line scan across the Ag agglomerate (approximately 620 nm) on a selected surface image of sample D6, which is generated by backscattered electrons. The line scan is shown in detail in Figure 8. One can see the pronounced

Catalyst Characterization with FESEM/EDX by

Fig. 8. Line scan in detail of Figure 7.

support.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 377

To demonstrate the imaging power of the T-SEM mode an example of a very tiny single alumina particle (D4) partially covered with Ag deposits in comparison to a extended particle (Figure 5) is shown in Figure 9. The dense Ag clusters appear as dark spots on the nearly "electron-transparent" alumina matrix. The graphical representation of an MC calculation based on an Al2O3 (Alox) substrate of 92 nm in thickness and covered with a 20 nm thick Ag deposit on a TEM-grid support is depicted in Figure 10. It illustrates the transmitted electron

Fig. 9. Transmitted (TE) electron image (100,000 x) of an irregularly shaped single alumina particle (light grey) partly covered with Ag clusters (dark), prepared on a TEM-grid

trajectories with a strongly reduced excitation volume in contrary to Figure 3.

maximum of the Ag signal and the minimum of the Al signal, whereas the minimum of the O signal is less distinct as expected. It is a hint for the presence of subsurface oxygen or Ag bulk-dissolved oxygen, which will discussed in chapter 3.6 in detail by means of O2-TPD and BET. The comparison between the experimental EDX line scan of sample D6 (Figure 8) and the simulated line scan (see Figure 6) is feasible.

Fig. 6. MC simulation of an X-ray line scan (20 kV beam voltage) across a single particle deposited on an alumina (Alox) substrate.

Fig. 7. BSE image of sample D6 and EDX line scan across a selected Ag deposit.

maximum of the Ag signal and the minimum of the Al signal, whereas the minimum of the O signal is less distinct as expected. It is a hint for the presence of subsurface oxygen or Ag bulk-dissolved oxygen, which will discussed in chapter 3.6 in detail by means of O2-TPD and BET. The comparison between the experimental EDX line scan of sample D6 (Figure 8)

Fig. 6. MC simulation of an X-ray line scan (20 kV beam voltage) across a single particle

Fig. 7. BSE image of sample D6 and EDX line scan across a selected Ag deposit.

and the simulated line scan (see Figure 6) is feasible.

deposited on an alumina (Alox) substrate.

Fig. 8. Line scan in detail of Figure 7.

To demonstrate the imaging power of the T-SEM mode an example of a very tiny single alumina particle (D4) partially covered with Ag deposits in comparison to a extended particle (Figure 5) is shown in Figure 9. The dense Ag clusters appear as dark spots on the nearly "electron-transparent" alumina matrix. The graphical representation of an MC calculation based on an Al2O3 (Alox) substrate of 92 nm in thickness and covered with a 20 nm thick Ag deposit on a TEM-grid support is depicted in Figure 10. It illustrates the transmitted electron trajectories with a strongly reduced excitation volume in contrary to Figure 3.

Fig. 9. Transmitted (TE) electron image (100,000 x) of an irregularly shaped single alumina particle (light grey) partly covered with Ag clusters (dark), prepared on a TEM-grid support.

Catalyst Characterization with FESEM/EDX by

0

10

20

30

surface coverage [%]

concentration measured by EDX.

colored areas show the matrix (Al2O3).

(by image analysis)

40

50

60

70

*r 2 = 0.93*

**3.3 Ag distribution by EDX** 

the intensity of the BSE signal.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 379

Thinner samples have better performance in resolution and contrast. In order to get highly magnified images even of tiny (sub nanometer) Ag particles, a TEM grid has to be prepared.

In Figure 12 one can see the correlation of measured Ag concentration and the percentage of Ag area coverage visualized by image analysis (Figure 13). The image analysis is based on

> 0 5 10 15 20 25 30 35 40 45 Ag [wt.-%] measured by EDX (20 kV)

Fig. 12. Ag area coverage derived from the BSE signal and the correlation to the Ag

Fig. 13. Color rendering of a BSE image. The yellow areas represent Ag deposits. The red

The MC simulation of the electron beam trajectories in Figure 10 and the X-ray line scan in Figure 11 visualizes the interaction of primary electrons with matter of different densities and thickness on a thin-film support. The broadening of the beam (skirt) depends on the specimen thickness and density.

Fig. 10. MC simulation of the electron trajectories of an Ag deposit on an Alox (Al2O3) substrate, supported by an 8 nm carbon film (C) (TEM-grid). Note the broadening of the beam by passing the beam electrons through the matter. Backscattering events are drastically reduced (NR =1).

Fig. 11. X-ray line scan simulation of the thin film-supported (C) specimen. The course of the X-ray scan across the specimen is similar to that in Figure 6, but the intensity is decreased (indicated by the statistical fluctuations) due to the reduced beam interaction volume.

Thinner samples have better performance in resolution and contrast. In order to get highly magnified images even of tiny (sub nanometer) Ag particles, a TEM grid has to be prepared.

## **3.3 Ag distribution by EDX**

378 Scanning Electron Microscopy

The MC simulation of the electron beam trajectories in Figure 10 and the X-ray line scan in Figure 11 visualizes the interaction of primary electrons with matter of different densities and thickness on a thin-film support. The broadening of the beam (skirt) depends on the

Fig. 10. MC simulation of the electron trajectories of an Ag deposit on an Alox (Al2O3) substrate, supported by an 8 nm carbon film (C) (TEM-grid). Note the broadening of the beam by passing the beam electrons through the matter. Backscattering events are

Fig. 11. X-ray line scan simulation of the thin film-supported (C) specimen. The course of the X-ray scan across the specimen is similar to that in Figure 6, but the intensity is decreased (indicated by the statistical fluctuations) due to the reduced beam interaction volume.

specimen thickness and density.

drastically reduced (NR =1).

In Figure 12 one can see the correlation of measured Ag concentration and the percentage of Ag area coverage visualized by image analysis (Figure 13). The image analysis is based on the intensity of the BSE signal.

Fig. 12. Ag area coverage derived from the BSE signal and the correlation to the Ag concentration measured by EDX.

Fig. 13. Color rendering of a BSE image. The yellow areas represent Ag deposits. The red colored areas show the matrix (Al2O3).

Catalyst Characterization with FESEM/EDX by

Figure 14 (100 x, 20 kV).

elements.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 381

**EDX results sample D6 C O Al Ag Cs**  Int1 3.0 51.8 29.8 15.1 0.12 Q1 2.6 51.3 30.2 15.9 0.00 Q2 5.4 51.2 30.3 13.1 0.00 Q3 1.9 52.2 30.6 15.2 0.15 Q4 3.1 51.4 29.1 16.4 0.00 average 3,3 51.6 30.0 15.1 0.01 standard deviation 1.3 0.40 0.60 1.30 0.07 max. 5.4 52.1 30.6 16.4 0.15 min. 1.9 51.2 29.1 13.1 0.00

Table 2. Results (wt %) of the EDX analysis of sample D6 shown in the FESEM image

Fig. 15. EDX analysis of sample D6 (Figure 14) revealing Ag, Al, O, and Cs as significant

According to Tab. 2, the average Ag concentration is 15.1% (average of Int1, Q1 to Q4) and the Ag concentrations in the quadrants do not vary significantly (Q1 = 15.9 % Ag, Q2 =13.1 % Ag, Q3 = 15.2 % Ag, and Q4 = 16.4 % Ag). But the Ag concentrations doesn't correlate well with the absolute amounts of Ag (20 wt %) used for catalyst preparation. In general, the heterogeneous catalysts are influenced considerably by the penetration depth of the electron beam because of layer thickness, Ag cluster size, surface coverage, preparation performance and, under certain conditions, Ag bulk-dissolved oxygen (see chapter 3.6). Hence, the Ag values determined by EDX could differ from the absolute Ag concentrations of the prepared samples. The accuracy is strongly influenced by morphological effects and heterogeneity of the Ag coverage of the analyzed samples. Availability of reliable standards in this respect would be beneficial. In either case, for an absolute Ag determination a quantitative digestion should be carried out (4 wt% HF) and analyzed by ICP-AES (Inductive coupled plasma - atom emission spectroscopy) under appropriate conditions.

The yellow spots represent the Ag deposits, approximately 13.5% from the total area. It corresponds to approximately 11 wt.-% Ag measured by EDX. This procedure will not replace the numerical calculation by the evaluation program, but visualizes in a first approximation the dependence of the Ag concentration on the BSE contrast. The Ag distribution on the carrier material is discussed with regard to the penetration depth of primary beam electrons and lower limits of detection for X-ray analysis. It is crucial to catalytic activity that Ag particles cover the carrier material almost homogeneously. Sintering of Ag particles to Ag agglomerates has to be prevented [11]. Using sample D6 (20 % Ag / 1500 ppm Cs) as an example, the FESEM images (Figure 14) and EDX analysis results (Tab. 2) shall be discussed. The image reveals uniform grain sizes of the carrier material and variable coverage (bright areas) by Ag particles or Ag clusters. In the quadrants Q1, Q2, Q3, and Q4 of the SEM image of sample D6 the coverage of the carrier grains by Ag particles (Ag-clusters) varies (bright areas). The apparently uniform catalyst grains are assumed to result from the wet sieving of the carrier material (SLA92 and SC13). The EDX spectrum in Figure 15 reveals all significant elements Ag, Al, O, and Cs. Obviously, some of the grains are densely covered with Ag, others not. Figure 16 shows the shape of huge Ag agglomerates of the D6 sample in a higher magnification (30,000 x). There is no significant difference in the measured Ag surface content between the 4 quadrants in Figure 14, which indicates an "apparently homogeneous" distribution of Ag.

Fig. 14. SEM image of sample D6 (100 x, 20 kV) showing the 4 quadrants and the whole (integral) surface area analyzed by EDX (Tab.2).

The yellow spots represent the Ag deposits, approximately 13.5% from the total area. It corresponds to approximately 11 wt.-% Ag measured by EDX. This procedure will not replace the numerical calculation by the evaluation program, but visualizes in a first approximation the dependence of the Ag concentration on the BSE contrast. The Ag distribution on the carrier material is discussed with regard to the penetration depth of primary beam electrons and lower limits of detection for X-ray analysis. It is crucial to catalytic activity that Ag particles cover the carrier material almost homogeneously. Sintering of Ag particles to Ag agglomerates has to be prevented [11]. Using sample D6 (20 % Ag / 1500 ppm Cs) as an example, the FESEM images (Figure 14) and EDX analysis results (Tab. 2) shall be discussed. The image reveals uniform grain sizes of the carrier material and variable coverage (bright areas) by Ag particles or Ag clusters. In the quadrants Q1, Q2, Q3, and Q4 of the SEM image of sample D6 the coverage of the carrier grains by Ag particles (Ag-clusters) varies (bright areas). The apparently uniform catalyst grains are assumed to result from the wet sieving of the carrier material (SLA92 and SC13). The EDX spectrum in Figure 15 reveals all significant elements Ag, Al, O, and Cs. Obviously, some of the grains are densely covered with Ag, others not. Figure 16 shows the shape of huge Ag agglomerates of the D6 sample in a higher magnification (30,000 x). There is no significant difference in the measured Ag surface content between the 4 quadrants in

Figure 14, which indicates an "apparently homogeneous" distribution of Ag.

Fig. 14. SEM image of sample D6 (100 x, 20 kV) showing the 4 quadrants and the whole

(integral) surface area analyzed by EDX (Tab.2).


Fig. 15. EDX analysis of sample D6 (Figure 14) revealing Ag, Al, O, and Cs as significant elements.

According to Tab. 2, the average Ag concentration is 15.1% (average of Int1, Q1 to Q4) and the Ag concentrations in the quadrants do not vary significantly (Q1 = 15.9 % Ag, Q2 =13.1 % Ag, Q3 = 15.2 % Ag, and Q4 = 16.4 % Ag). But the Ag concentrations doesn't correlate well with the absolute amounts of Ag (20 wt %) used for catalyst preparation. In general, the heterogeneous catalysts are influenced considerably by the penetration depth of the electron beam because of layer thickness, Ag cluster size, surface coverage, preparation performance and, under certain conditions, Ag bulk-dissolved oxygen (see chapter 3.6). Hence, the Ag values determined by EDX could differ from the absolute Ag concentrations of the prepared samples. The accuracy is strongly influenced by morphological effects and heterogeneity of the Ag coverage of the analyzed samples. Availability of reliable standards in this respect would be beneficial. In either case, for an absolute Ag determination a quantitative digestion should be carried out (4 wt% HF) and analyzed by ICP-AES (Inductive coupled plasma - atom emission spectroscopy) under appropriate conditions.

Catalyst Characterization with FESEM/EDX by

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 383

Fig. 17. BSE image of a selected area of an Ag catalyst particle (MZ06). The marked (red)

Fig. 18. Map of Ag-Lα showing the inhomogeneous distribution of Ag. The Ag

concentration correlates with the intensity of the green area (measuring time about 5 h).

areas P1, P2, P3 and P4 represent analyzed regions.

Fig. 16. Shape of huge Ag agglomerates of the D6 sample at 20 kV and 30,000 x.

## **3.4 EDX mapping**

The dispersion of the Ag and Cs metal deposited on the catalyst support can also be characterized by the EDX mapping technique. Topographical and elemental imaging is possible at the same time. EDX mapping is a valuable tool to indicate the quality of the Ag dispersion. The electron beam is scanned pixel by pixel across a selected area of interest. In the following example, the element mappings for a selected specimen area (Figure 17, BSE image, bright spots indicating metal) revealed an inhomogeneous distribution of Ag (Figure 18) and Cs (Figure 19) of the catalyst support. Generally, the brighter the color appears, the higher is the concentration of the specific element. In both cases it correlates with the results in Tab. 3. Cs as a promoter facilitates Ag distribution on the α-Al2O3 surface based on the high coverage and lack of crystallites with large contact angles [12]. The presence of Cs in the catalyst improves the distribution of Ag over the support and the Ag/Al interfacial area. Area P1 (see Figure 17 and Tab. 3), for instance, shows a remarkably high silver content of 66%, which is about twice as high as the average. The deviation from the average amount of Cs is not noticeable, because Cs is only present in small quantities (ppm). Nevertheless, the distribution of Ag and Cs can be shown very impressively by the time-consuming mapping. Spectra of the analyzed regions are shown in Figure 20. In summary it can be said that the inhomogeneity of metal distribution on the catalyst carrier material can be shown very clearly also for small amounts of metal. This example demonstrates a less quality of the preparation. One can see that the texture of the substrate SLA2 is very rough in comparison to SC31, which means that a homogeneous distribution of Ag and Cs is not so easy to achieve and requires an improvement in preparation. In case of an excellent preparation, this is a optimal Ag distribution for both catalyst systems, the question is which of the two catalyst systems is more favourable for the Ag-catalyzed epoxidation of 1,3-butadiene (1,3-BD) to 3,4-epoxy-1-butene (EpB) in the reactor.

Fig. 16. Shape of huge Ag agglomerates of the D6 sample at 20 kV and 30,000 x.

epoxidation of 1,3-butadiene (1,3-BD) to 3,4-epoxy-1-butene (EpB) in the reactor.

The dispersion of the Ag and Cs metal deposited on the catalyst support can also be characterized by the EDX mapping technique. Topographical and elemental imaging is possible at the same time. EDX mapping is a valuable tool to indicate the quality of the Ag dispersion. The electron beam is scanned pixel by pixel across a selected area of interest. In the following example, the element mappings for a selected specimen area (Figure 17, BSE image, bright spots indicating metal) revealed an inhomogeneous distribution of Ag (Figure 18) and Cs (Figure 19) of the catalyst support. Generally, the brighter the color appears, the higher is the concentration of the specific element. In both cases it correlates with the results in Tab. 3. Cs as a promoter facilitates Ag distribution on the α-Al2O3 surface based on the high coverage and lack of crystallites with large contact angles [12]. The presence of Cs in the catalyst improves the distribution of Ag over the support and the Ag/Al interfacial area. Area P1 (see Figure 17 and Tab. 3), for instance, shows a remarkably high silver content of 66%, which is about twice as high as the average. The deviation from the average amount of Cs is not noticeable, because Cs is only present in small quantities (ppm). Nevertheless, the distribution of Ag and Cs can be shown very impressively by the time-consuming mapping. Spectra of the analyzed regions are shown in Figure 20. In summary it can be said that the inhomogeneity of metal distribution on the catalyst carrier material can be shown very clearly also for small amounts of metal. This example demonstrates a less quality of the preparation. One can see that the texture of the substrate SLA2 is very rough in comparison to SC31, which means that a homogeneous distribution of Ag and Cs is not so easy to achieve and requires an improvement in preparation. In case of an excellent preparation, this is a optimal Ag distribution for both catalyst systems, the question is which of the two catalyst systems is more favourable for the Ag-catalyzed

**3.4 EDX mapping** 

Fig. 17. BSE image of a selected area of an Ag catalyst particle (MZ06). The marked (red) areas P1, P2, P3 and P4 represent analyzed regions.

Fig. 18. Map of Ag-Lα showing the inhomogeneous distribution of Ag. The Ag concentration correlates with the intensity of the green area (measuring time about 5 h).

Catalyst Characterization with FESEM/EDX by

> Al2O3 > C sequence [2].

area of surface.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 385

The support can modify the electronic character of the metal particle regarding to its adsorption and reactivity properties. Furthermore, the bond between the metal particle and the support can influence the shape of the metal particle (clusters). Both effects are so-called metal-support interactions (MSI). This effect decreases for supported Ag catalysts in the SiO2

Spectrum O Na Al Si Ca Ag Cs Sum P1 21.8 0.45 10.08 0.15 0.67 **66.33 0.45** 100 P2 54.1 0.32 31.16 0.00 6.62 **7.60 0.18** 100 P3 52.5 0.18 34.09 0.08 3.59 **9.53 0.00** 100 P4 38.6 0.28 19.59 0.04 2.16 **38.32 0.92** 100 average 41.8 0.31 23.73 0.07 3.26 **30.45 0.39** 100

Another tool to elucidate the distribution of elements is the so called CAMEO™ imaging. Figure 21 represents a CAMEO™ image from the examined specimen which is based on BSE detection. CAMEOTM is a tool to convert X-ray energies into visible wavelengths. In comparison to the X-ray mapping technique, the CAMEO™ procedure is much faster, but it may lead in some cases to a false color rendering (color overlap) caused by adjacent X-ray energies. As depicted in Figure 21 one can see that the brownish areas obscured the small Cs-spots. Therefore, Cs and Ag are not distinguishable because they are located at the same

max. 54.1 0.45 34.09 0.15 6.62 **66.33 0.92**  min. 21.8 0.18 10.08 0.00 0.67 **7.60 0.00** 

Fig. 21. CAMEO™ rendering of the area shown in Figure 17. The green colored areas

represent Ag, whereas the brownish areas represent Al, respectively.

Table 3. All results in wt.-%, derived from the BSE image in Figure 17.

Fig. 19. Map of Cs-Lα showing the inhomogeneous distribution of Cs. The Cs concentration is correlated with the intensity of the blue area(measuring time about 5 h).

Fig. 20. Spectra of the analyzed regions (see Figure 17).

The rate of a catalyzed reaction should be proportional to the surface area of the active agent. Therefore it is desirable to have the active phase in form of the smallest possible particle. But the most undesired contribution to the reduction of the active surface (deactivation) is sintering (welding together of particles by applying heat below the melting point). The function of the support is to increase the active surface and to reduce the rate of sintering of the metal particles. On the other hand, the interaction between the lattice oxygen of the carriers and the metal particles also influences the behaviour of the active metal agent.

Fig. 19. Map of Cs-Lα showing the inhomogeneous distribution of Cs. The Cs concentration

The rate of a catalyzed reaction should be proportional to the surface area of the active agent. Therefore it is desirable to have the active phase in form of the smallest possible particle. But the most undesired contribution to the reduction of the active surface (deactivation) is sintering (welding together of particles by applying heat below the melting point). The function of the support is to increase the active surface and to reduce the rate of sintering of the metal particles. On the other hand, the interaction between the lattice oxygen of the carriers and the metal particles also influences the behaviour of the active metal agent.

is correlated with the intensity of the blue area(measuring time about 5 h).

Fig. 20. Spectra of the analyzed regions (see Figure 17).

The support can modify the electronic character of the metal particle regarding to its adsorption and reactivity properties. Furthermore, the bond between the metal particle and the support can influence the shape of the metal particle (clusters). Both effects are so-called metal-support interactions (MSI). This effect decreases for supported Ag catalysts in the SiO2 > Al2O3 > C sequence [2].


Table 3. All results in wt.-%, derived from the BSE image in Figure 17.

Another tool to elucidate the distribution of elements is the so called CAMEO™ imaging. Figure 21 represents a CAMEO™ image from the examined specimen which is based on BSE detection. CAMEOTM is a tool to convert X-ray energies into visible wavelengths. In comparison to the X-ray mapping technique, the CAMEO™ procedure is much faster, but it may lead in some cases to a false color rendering (color overlap) caused by adjacent X-ray energies. As depicted in Figure 21 one can see that the brownish areas obscured the small Cs-spots. Therefore, Cs and Ag are not distinguishable because they are located at the same area of surface.

Fig. 21. CAMEO™ rendering of the area shown in Figure 17. The green colored areas represent Ag, whereas the brownish areas represent Al, respectively.

Catalyst Characterization with FESEM/EDX by

characterization methods applied on sample D1.

Promoter CsNO3 / wt.-% ICP

**3.6 O2-TPD and BET measurement** 

assume the following characteristic forms:

• Surface oxygen • Subsurface oxygen

• Ag bulk-dissolved oxygen

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 387

caused by charge effects due to the specific surface of particles and distribution of pores. In the course of our various measurements, no significant charging was observed. Compared to common *inductively coupled plasma atomic emission spectroscopy* (ICP-AES) the values of metal deposition determined by EDX are frequently higher. One explanation is a sometimes observable incomplete chemical digestion prior to ICP measurements. In the case of Al2O3 we recommend a digestion with HF (4%). As already mentioned, EDX with an electron penetration depth in the order of several nanometers up to microns is a technique for surface analysis, while the ICP technique is applicable to the quantitative determination of the bulk composition with the detection limits in the µg/l (ppb) range. Due to the incipient wetness impregnation technique applied here for surface preparation, the values of metal deposition analyzed by EDX are expected to be higher than those by ICP-AES [16]. The differences in the amounts of Ag are plausible. The reason therefore is that the D-samples and MZ-samples are completely chemically digested, which is a physical homogenization. In contrast, the sample preparation for FESEM/EDX is non-destructive, which means that the sample is not physically homogenized. Tab. 5 gives a selected overview about catalysts

Ag / wt.-%

0.15 4.46 5.76 6.03

characterization of a catalyst with nominal 5 wt.-% Ag, grain fraction 45-63 µm (sample D1).

To get a deeper insight it makes sense to combine SEM with O2-TPD experiments and BET measurements, which additionally were carried out. The TPD experiment measures the temperature-dependent desorption rate of a molecule from the catalyst surface [17]. Typically, O2 / Ag interactions are studied on the Ag monocrystal surfaces (110) and (111) [18]. This means that silver may storage O2, with the amount adsorbed being dependent on temperature and O2 partial pressure. O2 dissociatively adsorbs on the Ag catalyst and may

Ag bulk-dissolved oxygen may act as a storage of converted surface oxygen and, hence, be supplied later on. Subsurface O2 increases the coordination number of Ag surface atoms, which results in a smaller binding strength of surface oxygen and favourably influences the epoxidation reaction of 1,3-BD. The surface area has been corrected by the subsurface oxygen value. Comparison of the measured amount of O2 desorbed with the amount theoretically required for an O2 monolayer shows that the measured amount of desorbed O2 is higher by a factor of 2. This may be explained by the presence of subsurface O2 [19].

Table 5. A listing of commonly applied laboratory methods (ICP, EDX, BET) for

EDX Ag / wt.-% BET m2g-1

#### **3.5 Remarks**

All analytical results have been obtained at 20 kV acceleration voltages, which turned out to be the best choice for excitation conditions. Because the in-lens detector is switched off above 20kV, therefore high resolution SE imaging is disabled. The EDX results are determined by the manufacturer's spectrum evaluation software (Oxford corp.). Originally, all quantitative results are calculated with two significant fractional decimal digits provided with 1σ errors, which includes the errors resulting from spectra processing (background subtraction, filtered least squares fitting, peak overlap). Notably, this may not reflect uncertainties caused by surface topography and other systematic influences. According to experience, the actual uncertainties are considerably higher, especially for light elements. We suggest for C and O a relative uncertainty of 5 – 20%, for all other elements 1-5% [13]. Another important issue is the estimation of the *limit of detection* (LOD), which can be calculated from a synthetic spectrum by the equation 2.

$$LOD\_{3\sigma} = 3 \cdot \sqrt{B} \cdot \frac{C}{P} \tag{2}$$

Where B is the number of background counts, 3·B1/2 represents 3σ error of background measurement, C is the concentration of the element, P corresponds to the number of counts in the X-ray line after background subtraction. In practice, calculation will be performed by a special program tool named '*spectrum synthesis'*, which is provided with the INCA-Energy evaluation software [14].


Table 4. Calculated 3σ LOD's regarding to equation 2 and [14]. The matrix composition, except for alumina, is assumed to be 1.8 wt.-% Ca, 0.3 wt.-% Na, 0.2 wt.-% P.

For the 3σ LOD estimation, the composition of the sample matrix and the acquisition parameters like measuring time tm, beam voltage U, current I, detector parameters, X-ray take-off angle are required. Tab. 4 shows 3σ LOD's which are calculated for 3 different measuring times and aperture adjustments (beam current). For Ag, this is of minor importance, since the amounts of Ag are sufficiently high in comparison to the promoters.The pore size of the alumina support is in a similar order of magnitude as the Xray generating range (see Fig. 1b), which leads to an uncertainty in the measurements in these regions. As a consequence, the EDX analysis is considered to be semi-quantitative on such Al2O3 supports. A loss of X-rays (Al-Kα and O-Kα) emitted from mesoporous media compared to that of dense monocrystalline alumina is described in literature [15]. It may be

All analytical results have been obtained at 20 kV acceleration voltages, which turned out to be the best choice for excitation conditions. Because the in-lens detector is switched off above 20kV, therefore high resolution SE imaging is disabled. The EDX results are determined by the manufacturer's spectrum evaluation software (Oxford corp.). Originally, all quantitative results are calculated with two significant fractional decimal digits provided with 1σ errors, which includes the errors resulting from spectra processing (background subtraction, filtered least squares fitting, peak overlap). Notably, this may not reflect uncertainties caused by surface topography and other systematic influences. According to experience, the actual uncertainties are considerably higher, especially for light elements. We suggest for C and O a relative uncertainty of 5 – 20%, for all other elements 1-5% [13]. Another important issue is the estimation of the *limit of detection* (LOD), which can be

<sup>3</sup> <sup>3</sup>*<sup>C</sup> LOD B*

Where B is the number of background counts, 3·B1/2 represents 3σ error of background measurement, C is the concentration of the element, P corresponds to the number of counts in the X-ray line after background subtraction. In practice, calculation will be performed by a special program tool named '*spectrum synthesis'*, which is provided with the INCA-Energy

**LOD 3σ / w t.- % I / nA tm / s U / kV** 

0.21 - 0.23 60 20 0.18 0.27 0.23 100 20 0.09 0.15 0.71 100 20 0.06 0.09 0.71 300 20

Table 4. Calculated 3σ LOD's regarding to equation 2 and [14]. The matrix composition,

For the 3σ LOD estimation, the composition of the sample matrix and the acquisition parameters like measuring time tm, beam voltage U, current I, detector parameters, X-ray take-off angle are required. Tab. 4 shows 3σ LOD's which are calculated for 3 different measuring times and aperture adjustments (beam current). For Ag, this is of minor importance, since the amounts of Ag are sufficiently high in comparison to the promoters.The pore size of the alumina support is in a similar order of magnitude as the Xray generating range (see Fig. 1b), which leads to an uncertainty in the measurements in these regions. As a consequence, the EDX analysis is considered to be semi-quantitative on such Al2O3 supports. A loss of X-rays (Al-Kα and O-Kα) emitted from mesoporous media compared to that of dense monocrystalline alumina is described in literature [15]. It may be

except for alumina, is assumed to be 1.8 wt.-% Ca, 0.3 wt.-% Na, 0.2 wt.-% P.

σ

*P*

=⋅ ⋅ (2)

calculated from a synthetic spectrum by the equation 2.

Ag Cs

**3.5 Remarks** 

evaluation software [14].

caused by charge effects due to the specific surface of particles and distribution of pores. In the course of our various measurements, no significant charging was observed. Compared to common *inductively coupled plasma atomic emission spectroscopy* (ICP-AES) the values of metal deposition determined by EDX are frequently higher. One explanation is a sometimes observable incomplete chemical digestion prior to ICP measurements. In the case of Al2O3 we recommend a digestion with HF (4%). As already mentioned, EDX with an electron penetration depth in the order of several nanometers up to microns is a technique for surface analysis, while the ICP technique is applicable to the quantitative determination of the bulk composition with the detection limits in the µg/l (ppb) range. Due to the incipient wetness impregnation technique applied here for surface preparation, the values of metal deposition analyzed by EDX are expected to be higher than those by ICP-AES [16]. The differences in the amounts of Ag are plausible. The reason therefore is that the D-samples and MZ-samples are completely chemically digested, which is a physical homogenization. In contrast, the sample preparation for FESEM/EDX is non-destructive, which means that the sample is not physically homogenized. Tab. 5 gives a selected overview about catalysts characterization methods applied on sample D1.


Table 5. A listing of commonly applied laboratory methods (ICP, EDX, BET) for characterization of a catalyst with nominal 5 wt.-% Ag, grain fraction 45-63 µm (sample D1).

#### **3.6 O2-TPD and BET measurement**

To get a deeper insight it makes sense to combine SEM with O2-TPD experiments and BET measurements, which additionally were carried out. The TPD experiment measures the temperature-dependent desorption rate of a molecule from the catalyst surface [17]. Typically, O2 / Ag interactions are studied on the Ag monocrystal surfaces (110) and (111) [18]. This means that silver may storage O2, with the amount adsorbed being dependent on temperature and O2 partial pressure. O2 dissociatively adsorbs on the Ag catalyst and may assume the following characteristic forms:


Ag bulk-dissolved oxygen may act as a storage of converted surface oxygen and, hence, be supplied later on. Subsurface O2 increases the coordination number of Ag surface atoms, which results in a smaller binding strength of surface oxygen and favourably influences the epoxidation reaction of 1,3-BD. The surface area has been corrected by the subsurface oxygen value. Comparison of the measured amount of O2 desorbed with the amount theoretically required for an O2 monolayer shows that the measured amount of desorbed O2 is higher by a factor of 2. This may be explained by the presence of subsurface O2 [19].

Catalyst Characterization with FESEM/EDX by

versus temperature (red line).

**as,B ET [m2**

Ag 10% Ag 10% + Cs Ag 20%

Table 6. Specific surface areas from sample D3 – D6 and MZ06 and MZ09.

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 389

Fig. 23. O2 detection (WLD) by O2-TPD of samples D5 (20 % Ag) and D6 (20 % Ag + Cs)

D3 D4 D5 D6 MZ06 MZ09

The BET reveals that there is a significant difference in the catalyst surface, which is expressed by the topological images of FESEM of the D samples (SC31) and MZ (SLA92) samples. Note: Very small amounts (0,1g) of sample are used for the investigations (FESEM, TPD). In the case of BET-Measurement the sample weight is in the range of 0.1 g up to 10 g, depending on the specific surface. The carrier material SC13 (high BET Surface) was not found to be suited for the epoxidation of 1,3-butadiene (Figure 24) on Ag particles. Already at 180°C a total oxidation of 1,3-butadiene to CO2 and H2O does occur, also when Cs-doped catalysts are used. In contrast to this, epoxidation of the Ag catalyst with SLA29 (less BET surface) carrier material results in an EpB selectivity of 74 % (200° C, SV = 2590 h-1) at a 1,3- BD conversion rate of 15 % [22]. The constitution of the oxametallacycle intermediate is depicted in Figure 25. The guiding hypothesis is that surface oxametallacycles are key intermediate for epoxidation on Ag / Cs catalysts. The intermediate EpB(ads), finally leading to molecular EpB, is probably strongly adsorbed on the catalyst surface indicated by theoretically calculations which also support its identity as an oxametallacycle. The oxametallacycle intermediate is more thermodynamically stable than EpB by approx. 24 kcal/mol. Moreover, the transition state for EpB formation from the oxametallacycle intermediate is actually lower in energy than the reactants, butadiene and oxygen [23].

 **g-1] 6,0424 6,5521 7,6083 8,0461 0,80722 0,7009** 

Ag 20% +

Cs Ag 5% + Cs Ag 10% + Cs

O2-TPD studies reveal significant differences between MZ samples with an SLA92 carrier and D samples with an SC13 carrier for the same Ag contents. In general it can be said that the desorbed O2 amounts of the MZ samples are below those of the D samples by a factor of 5. Moreover, the temperatures of the desorption maximums of the MZ samples are lower. This difference (binding strength of oxygen) results in a strongly variable reactivity of the adsorbed oxygen species. Figure 22 shows O2 desorption and temperature of the samples D1, D3, and D5 with different Ag concentration on the support that is *not doped with Cs*. With increasing Ag concentration, the desorptions maxima are shifted towards lower temperatures (red line). This indicates that epoxidation already may start at lower temperatures. Figure 23 shows that the sample D6 doped with Cs reaches a higher temperature at the desorption maximum than the non-doped sample D5.

Fig. 22. O2 detection (WLD) by O2-TPD of the samples D1 (5 % Ag), D3 (10 % Ag), and D5 (20 % Ag) versus temperature (red line).

This indicates that the adsorbate is stabilized by the presence and the grade of distribution of Cs and Ag on the surface, which correlates with TPD results, FESEM, EDX mapping and the reaction performance. The determination of the specific surface areas of the catalysts D3, D4, D5, D6, MZ06 and MZ09 gives an important hint regarding to metal distribution of MSI effects. Tab. 6 lists the specific surface areas for the mentioned catalysts determined with N2 as adsorptive. All isotherms are of the IUPAC type II ("sshaped") and, hence, can be evaluated according to the BET theory [20], [21]. The Csdoped samples (D4, D6) have a slightly higher surface than the non-doped samples (D3, D5) with the same Ag content, which correlates to a higher desorption rate for O2-TPD. In case of the samples MZ06 and MZ09 one can see that the difference in BET surface is not remarkable, even in the presence of different Ag amounts. The total error for BET measurement is about 0.1 m2/g.

O2-TPD studies reveal significant differences between MZ samples with an SLA92 carrier and D samples with an SC13 carrier for the same Ag contents. In general it can be said that the desorbed O2 amounts of the MZ samples are below those of the D samples by a factor of 5. Moreover, the temperatures of the desorption maximums of the MZ samples are lower. This difference (binding strength of oxygen) results in a strongly variable reactivity of the adsorbed oxygen species. Figure 22 shows O2 desorption and temperature of the samples D1, D3, and D5 with different Ag concentration on the support that is *not doped with Cs*. With increasing Ag concentration, the desorptions maxima are shifted towards lower temperatures (red line). This indicates that epoxidation already may start at lower temperatures. Figure 23 shows that the sample D6 doped with Cs reaches a higher

Fig. 22. O2 detection (WLD) by O2-TPD of the samples D1 (5 % Ag), D3 (10 % Ag), and D5

This indicates that the adsorbate is stabilized by the presence and the grade of distribution of Cs and Ag on the surface, which correlates with TPD results, FESEM, EDX mapping and the reaction performance. The determination of the specific surface areas of the catalysts D3, D4, D5, D6, MZ06 and MZ09 gives an important hint regarding to metal distribution of MSI effects. Tab. 6 lists the specific surface areas for the mentioned catalysts determined with N2 as adsorptive. All isotherms are of the IUPAC type II ("sshaped") and, hence, can be evaluated according to the BET theory [20], [21]. The Csdoped samples (D4, D6) have a slightly higher surface than the non-doped samples (D3, D5) with the same Ag content, which correlates to a higher desorption rate for O2-TPD. In case of the samples MZ06 and MZ09 one can see that the difference in BET surface is not remarkable, even in the presence of different Ag amounts. The total error for BET

(20 % Ag) versus temperature (red line).

measurement is about 0.1 m2/g.

temperature at the desorption maximum than the non-doped sample D5.

Fig. 23. O2 detection (WLD) by O2-TPD of samples D5 (20 % Ag) and D6 (20 % Ag + Cs) versus temperature (red line).


Table 6. Specific surface areas from sample D3 – D6 and MZ06 and MZ09.

The BET reveals that there is a significant difference in the catalyst surface, which is expressed by the topological images of FESEM of the D samples (SC31) and MZ (SLA92) samples. Note: Very small amounts (0,1g) of sample are used for the investigations (FESEM, TPD). In the case of BET-Measurement the sample weight is in the range of 0.1 g up to 10 g, depending on the specific surface. The carrier material SC13 (high BET Surface) was not found to be suited for the epoxidation of 1,3-butadiene (Figure 24) on Ag particles. Already at 180°C a total oxidation of 1,3-butadiene to CO2 and H2O does occur, also when Cs-doped catalysts are used. In contrast to this, epoxidation of the Ag catalyst with SLA29 (less BET surface) carrier material results in an EpB selectivity of 74 % (200° C, SV = 2590 h-1) at a 1,3- BD conversion rate of 15 % [22]. The constitution of the oxametallacycle intermediate is depicted in Figure 25. The guiding hypothesis is that surface oxametallacycles are key intermediate for epoxidation on Ag / Cs catalysts. The intermediate EpB(ads), finally leading to molecular EpB, is probably strongly adsorbed on the catalyst surface indicated by theoretically calculations which also support its identity as an oxametallacycle. The oxametallacycle intermediate is more thermodynamically stable than EpB by approx. 24 kcal/mol. Moreover, the transition state for EpB formation from the oxametallacycle intermediate is actually lower in energy than the reactants, butadiene and oxygen [23].

Catalyst Characterization with FESEM/EDX by

selectivity and conversion rate of the educts.

Sara Essig, (preparation), KIT, Germany

Verlag GmbH Weinheim, 1998

Publishing, Bristol a. Philadelphia, 2003

and Handbook, Münster, Germany 1998

[16] C. Xu, J. Zhu, Nanotechnology 2004, 15, 1671-1681

[18] F. M. Leibsle et al., Phys. Rev. Lett. 1994, 72, 569, 569-2572.

Kluwer Acdemic/Plenum Publishers, New York, 2003

Schüth, J. Weitkamp) Wiley-VCH, Weinheim 2008, 561

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[19] G.W. Busser, O. Hinrichsen, M. Muhler, Cata. Lett. 2002, 79 (1 - 4), 49

VCH Verlag GmbH, 2000

Doreen Neumann-Walter, (preparation), KIT, Germany

Bernhard Powietzka, (preparation, reaction control), KIT, Germany

Dr. Angela Puls, (BET measurement), Rubotherm GmbH Bochum, Germany Dr. Volker Hagen (O2-TPD measurement), Rubokat GmbH Bochum, Germany

[3] J. R. Monnier, *Prepr. Pap*. - *Am. Chem. Soc.,Div. Fuel Chem*. 2007, 52 *(2)*, 163

**5. Acknowledgement** 

**6. References** 

(4), 392

326

393-399

the Example of Silver-Catalyzed Epoxidation of 1,3-Butadiene 391

the efficiency of Ag-loading and promoters with TPD measurements can be determined. The analytical result reflects the final behaviour of the epoxidation regarding to the product

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Fig. 24. Epoxidation of 1,3-butadiene over Ag / Cs catalyst on Al2O3 to 3,4-epoxy-1-butene.

Fig. 25. The "intermediate EpB" finally leading to molecular EpB. The oxametallacycle intermediate is more thermodynamically stable than EpB. Moreover, the transition state for EpB formation from the oxametallacycle intermediate is actually lower in energy than the reactants, butadiene and oxygen

### **4. Conclusions and outlook**

Production of Ag catalysts based on a corundum-containing (SC13) and a calcium hexa aluminate-containing (SLA92) carrier material is crucial to the selective epoxidation of 1,3 butadiene. Optimum distribution and morphology of the Ag particles must be ensured by controlled, tailored catalyst synthesis. An increase in the activity by enhanced Ag dispersion on a corundum-containing carrier material with a larger surface area leads to completely unselective catalysts. FESEM/EDX results provide major information with regard to the Ag distribution and the properties of the carrier surfaces. The carrier material SC13 was not found to be suited for the epoxidation of 1,3-butadiene on Ag particles. At 180°C already does a total oxidation of 1,3-butadiene to CO2 and H2O occur also when Cs-doped catalysts are used. In contrast to this, epoxidation of the Ag catalyst with SLA29 carrier material results in an EpB selectivity of 74 % (200 °C, SV = 2590 h-1) at a 1,3-BD conversion rate of 15 %. All the analysis methods complement each other to form an overall impression, which is reflected in the product selectivity, catalyst activity and educts conversion during the reaction. The distribution and composition of metal particles on the surfaces can be seen and detected with EDX and FESEM. Also topological and morphological effects can be shown. BET measurements allow drawing a conclusion for successful metal loading. Furthermore, the efficiency of Ag-loading and promoters with TPD measurements can be determined. The analytical result reflects the final behaviour of the epoxidation regarding to the product selectivity and conversion rate of the educts.

## **5. Acknowledgement**

390 Scanning Electron Microscopy

**Ag / Cs - Al2O3 <sup>O</sup>**

**3,4-epoxy-1-butene**

**O2**

Fig. 24. Epoxidation of 1,3-butadiene over Ag / Cs catalyst on Al2O3 to 3,4-epoxy-1-butene.

Fig. 25. The "intermediate EpB" finally leading to molecular EpB. The oxametallacycle intermediate is more thermodynamically stable than EpB. Moreover, the transition state for EpB formation from the oxametallacycle intermediate is actually lower in energy than the

Production of Ag catalysts based on a corundum-containing (SC13) and a calcium hexa aluminate-containing (SLA92) carrier material is crucial to the selective epoxidation of 1,3 butadiene. Optimum distribution and morphology of the Ag particles must be ensured by controlled, tailored catalyst synthesis. An increase in the activity by enhanced Ag dispersion on a corundum-containing carrier material with a larger surface area leads to completely unselective catalysts. FESEM/EDX results provide major information with regard to the Ag distribution and the properties of the carrier surfaces. The carrier material SC13 was not found to be suited for the epoxidation of 1,3-butadiene on Ag particles. At 180°C already does a total oxidation of 1,3-butadiene to CO2 and H2O occur also when Cs-doped catalysts are used. In contrast to this, epoxidation of the Ag catalyst with SLA29 carrier material results in an EpB selectivity of 74 % (200 °C, SV = 2590 h-1) at a 1,3-BD conversion rate of 15 %. All the analysis methods complement each other to form an overall impression, which is reflected in the product selectivity, catalyst activity and educts conversion during the reaction. The distribution and composition of metal particles on the surfaces can be seen and detected with EDX and FESEM. Also topological and morphological effects can be shown. BET measurements allow drawing a conclusion for successful metal loading. Furthermore,

**1,3-butadiene**

reactants, butadiene and oxygen

**4. Conclusions and outlook** 

Doreen Neumann-Walter, (preparation), KIT, Germany

Bernhard Powietzka, (preparation, reaction control), KIT, Germany

Sara Essig, (preparation), KIT, Germany

Dr. Angela Puls, (BET measurement), Rubotherm GmbH Bochum, Germany

Dr. Volker Hagen (O2-TPD measurement), Rubokat GmbH Bochum, Germany

## **6. References**


**20** 

Yoshio Ichida

*Japan* 

*Utsunomiya University,* 

**Fractal Analysis of Micro Self-Sharpening** 

Self-sharpening phenomenon of the grain cutting edges during grinding is the main factor controlling the performance and the tool life of grinding wheels. Therefore, many studies on the relationship between the wear behavior and the self-sharpening of the grain cutting edges have been carried out (Yoshikawa, 1960; Tsuwa, 1961; Ichida et al., 1989, 1995; Malkin, 1989; Show, 1996). However, it is very difficult to evaluate this relation quantitatively because of the complexity in wear mechanism and the irregularity in shape and distribution of the grain cutting edges (Webster & Tricard, 2004). Especially, self-sharpening of the cutting edges in the grinding process with cBN wheels has not yet been clarified sufficiently (Ichida et al. 1997, 2006; Guo etal., 2007). To develop an innovative machining system using cBN grinding wheels, it is essential to clarify the self-sharpening mechanism due to the micro fracturing of the cutting edges that is the most important factor controlling the grinding ability of cBN wheel during the grinding process (Ichida et al. 2006; Kalpakjian, 1995; Comley et al., 2006). The main purpose of this study is to evaluate quantitatively such a complicated selfsharpening phenomenon of the cutting edges in cBN grinding on the basis of fractal analysis. The changes in three-dimensional surface profile of cBN grain cutting edge in the grinding process are measured using a scanning electron microscope with four electron

There are several methods for calculating fractal dimension (Mandelbrot, 1983; Mandelbrot et al. 1984; Hagiwara et al., 1995; Itoh et al., 1990). In this report, we have used a 3D-fractal analysis that is expanded based on the idea in the fractal analysis using two-dimensional mesh counting method (Sakai et al., 1998). The analysis method is shown as follows. A 3Dprofile under test is divided by cube grid with a mesh size of *r*. And then, the number of cubes intersected with 3D-profile *N*(*r*) is counted. If there is a fractal nature in this 3D-

> ( ) *<sup>D</sup> Nr r* α

<sup>−</sup> = ⋅ *<sup>S</sup>* (1)

profile, the relationship between *N*(*r*), *r* and fractal dimension *DS* is given by

detectors and evaluated by means of fractal dimension.

**2. Three-dimensional fractal analysis** 

**1. Introduction** 

**Phenomenon in Grinding with Cubic** 

**Boron Nitride (cBN) Wheels** 


## **Fractal Analysis of Micro Self-Sharpening Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels**

Yoshio Ichida *Utsunomiya University, Japan* 

## **1. Introduction**

392 Scanning Electron Microscopy

[20] D. D. Do, Adsorption Analysis: Equilibria and Kinetics, Imperial College Press, London

[22] T. N. Otto, P. Pfeifer, S. Pitter, B. Powietzka, *Chem. Ing. Tech.* 2009, 81 *(3)*, 349

[23] Mark A. Barteau, Topics in Catalysis Vol. 22, Nos. 1/2, January 2003

[21] P. Christopher and S. Linic, ChemCatChem 2010, (1), 2, 78

1998

Self-sharpening phenomenon of the grain cutting edges during grinding is the main factor controlling the performance and the tool life of grinding wheels. Therefore, many studies on the relationship between the wear behavior and the self-sharpening of the grain cutting edges have been carried out (Yoshikawa, 1960; Tsuwa, 1961; Ichida et al., 1989, 1995; Malkin, 1989; Show, 1996). However, it is very difficult to evaluate this relation quantitatively because of the complexity in wear mechanism and the irregularity in shape and distribution of the grain cutting edges (Webster & Tricard, 2004). Especially, self-sharpening of the cutting edges in the grinding process with cBN wheels has not yet been clarified sufficiently (Ichida et al. 1997, 2006; Guo etal., 2007). To develop an innovative machining system using cBN grinding wheels, it is essential to clarify the self-sharpening mechanism due to the micro fracturing of the cutting edges that is the most important factor controlling the grinding ability of cBN wheel during the grinding process (Ichida et al. 2006; Kalpakjian, 1995; Comley et al., 2006).

The main purpose of this study is to evaluate quantitatively such a complicated selfsharpening phenomenon of the cutting edges in cBN grinding on the basis of fractal analysis. The changes in three-dimensional surface profile of cBN grain cutting edge in the grinding process are measured using a scanning electron microscope with four electron detectors and evaluated by means of fractal dimension.

## **2. Three-dimensional fractal analysis**

There are several methods for calculating fractal dimension (Mandelbrot, 1983; Mandelbrot et al. 1984; Hagiwara et al., 1995; Itoh et al., 1990). In this report, we have used a 3D-fractal analysis that is expanded based on the idea in the fractal analysis using two-dimensional mesh counting method (Sakai et al., 1998). The analysis method is shown as follows. A 3Dprofile under test is divided by cube grid with a mesh size of *r*. And then, the number of cubes intersected with 3D-profile *N*(*r*) is counted. If there is a fractal nature in this 3Dprofile, the relationship between *N*(*r*), *r* and fractal dimension *DS* is given by

$$N(r) = \alpha \cdot r^{-D\_{\mathfrak{z}}} \tag{1}$$

Fractal Analysis of Micro Self-Sharpening

wheel (Fujimoto, 2006).

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 395

EDM3000) (Ichida, 2008). The cBN segment detached for the observation can be precisely returned to former state again. It was confirmed experimentally that the cBN wheel with the replaceable cBN segment has almost the same grinding ability as the usual complete cBN

Fig. 1. Method of 3D-fractal analysis (Sample: surface profile of cBN abrasive grain).

where αis constant number.

Area of square with mesh size *r* is expressed *r2*. Therefore, the surface area of 3D-profile *S*(*r*) based on *N*(*r*) is given by

$$S(r) = r^2 \cdot N(r) = \alpha \cdot r^2 \cdot r^{-D\_s} \tag{2}$$

If the logarithm of both sides is taken, eq. (2) is rewritten as follows;

$$
\log S(r) = \log \alpha + (2 - D\_s) \log r \tag{3}
$$

Fractal dimension *DS* is calculated by the following equation using the proportionality constant between log S(*r*) and log *r* in eq. (3).

$$D\_s = 2 - \frac{d\log S(r)}{d\log r} \tag{4}$$

However, actual fractal analysis is conducted according to the following procedures by computer processing in this study. As shown in Fig. 1 (a), a square grid with mesh size *r*1 is set on a 3D-profile of the top surface of grain cutting edge. It is divided to two triangular elements with mesh size *r*1. Surface areas of each triangle *s*1(*r*1) and *s*2(*r*1) are evaluated using height coordinates in each grid point and *S*(*r*1) is decided by sum of these surface areas. Next, as shown in Fig. 1 (b), each triangle is divided with mesh size *r*2 that is half a size of *r*1. Surface areas of 8 triangles *s*1(*r*2), …, *s*8(*r*2) are evaluated using height coordinates in 9 grid points and *S*(*r*2) is decided by sum of these surface areas. In addition, as shown in Fig. 1 (c), 8 triangles are divided with mesh size *r*3 that is half a size of *r*2. Surface areas of 32 triangles *s*1(*r*3), …,S32(r3) are evaluated using height coordinates in 25 grid points and *S*(*r*3) is decided by sum of these surface areas. Afterward, mesh size *r* is scaled down and the surface area of 3D-profile is evaluated as follows;

$$S\left(r\_n\right) = \sum\_{i=1}^{2^{2^{n-1}}} s\_i\left(r\_n\right) \tag{5}$$

On the basis of these equations, *r* is taken on the horizontal log axis, and *S*(*r*) is taken on the vertical log axis. When data points are on a straight line in double log plot, fractal dimension *DS* is given by a slope of the straight line. Figure 2 shows an example of relationship between *S*(*r*) and *r* (Sample: surface profile of cBN cutting edge shown in Fig.1). Fractal nature is approved in a region of 0.4 < *r* < 4 μm. From a slope of the straight line, it is decided that fractal dimension is 2.015.

#### **3. Three-dimensional observation of wheel working surface**

#### **3.1 Experimental procedure**

Grinding experiments were conducted with surface plunge grinding method on a horizontal spindle surface grinding machine. The schematic illustration of the experimental setup is shown in Fig. 3. A vitrified cBN wheel with a replaceable cBN segment shown in Fig. 3 was used to observe directly the profile of the wheel surface in the grinding process using a three-dimensional (3D) scanning electron microscope with four electron probes (3D-SEM/

Area of square with mesh size *r* is expressed *r2*. Therefore, the surface area of 3D-profile *S*(*r*)

Fractal dimension *DS* is calculated by the following equation using the proportionality

However, actual fractal analysis is conducted according to the following procedures by computer processing in this study. As shown in Fig. 1 (a), a square grid with mesh size *r*1 is set on a 3D-profile of the top surface of grain cutting edge. It is divided to two triangular elements with mesh size *r*1. Surface areas of each triangle *s*1(*r*1) and *s*2(*r*1) are evaluated using height coordinates in each grid point and *S*(*r*1) is decided by sum of these surface areas. Next, as shown in Fig. 1 (b), each triangle is divided with mesh size *r*2 that is half a size of *r*1. Surface areas of 8 triangles *s*1(*r*2), …, *s*8(*r*2) are evaluated using height coordinates in 9 grid points and *S*(*r*2) is decided by sum of these surface areas. In addition, as shown in Fig. 1 (c), 8 triangles are divided with mesh size *r*3 that is half a size of *r*2. Surface areas of 32 triangles *s*1(*r*3), …,S32(r3) are evaluated using height coordinates in 25 grid points and *S*(*r*3) is decided by sum of these surface areas. Afterward, mesh size *r* is scaled down and the surface area of

> () () 2 1 2

> > =

On the basis of these equations, *r* is taken on the horizontal log axis, and *S*(*r*) is taken on the vertical log axis. When data points are on a straight line in double log plot, fractal dimension *DS* is given by a slope of the straight line. Figure 2 shows an example of relationship between *S*(*r*) and *r* (Sample: surface profile of cBN cutting edge shown in Fig.1). Fractal nature is approved in a region of 0.4 < *r* < 4 μm. From a slope of the straight line, it is

Grinding experiments were conducted with surface plunge grinding method on a horizontal spindle surface grinding machine. The schematic illustration of the experimental setup is shown in Fig. 3. A vitrified cBN wheel with a replaceable cBN segment shown in Fig. 3 was used to observe directly the profile of the wheel surface in the grinding process using a three-dimensional (3D) scanning electron microscope with four electron probes (3D-SEM/

**3. Three-dimensional observation of wheel working surface** 

*n n in i Sr s r* −

1

log ( ) <sup>2</sup> log *<sup>S</sup>*

*d Sr*

α

<sup>−</sup>*DS* (2)

( ) 2 lo *D r <sup>S</sup>* g (3)

*d r* = − (4)

<sup>=</sup> (5)

() () *Sr r Nr r r* = ⋅ =⋅ ⋅ 2 2

log*S r*( ) = +− logα

*D*

If the logarithm of both sides is taken, eq. (2) is rewritten as follows;

where α

is constant number.

constant between log S(*r*) and log *r* in eq. (3).

3D-profile is evaluated as follows;

decided that fractal dimension is 2.015.

**3.1 Experimental procedure** 

based on *N*(*r*) is given by

EDM3000) (Ichida, 2008). The cBN segment detached for the observation can be precisely returned to former state again. It was confirmed experimentally that the cBN wheel with the replaceable cBN segment has almost the same grinding ability as the usual complete cBN wheel (Fujimoto, 2006).

Fig. 1. Method of 3D-fractal analysis (Sample: surface profile of cBN abrasive grain).

Fractal Analysis of Micro Self-Sharpening

Fig. 4. Measurements of grinding characteristic palameters.

(M2/ASTM) is used as the workpiece material.

Grinding wheel CBN80L100V

Peripheral wheel speed *vs* 33 [m/s] Work speed *vw* 0.1 [m/s] Wheel depth of cut *a* 10 [μm]

Table 1. Grinding conditions.

cBN grain Single crystal cBN

Grinding fluid Soluble type (JIS W-2-2)

Workpiece High speed steel (JIS/SKH51)

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 397

The expermental conditions are listed in Table 1. Representative single crystal cBN grain was used for cBN wheel. The dressing of cBN wheel was performed using a rotary diamond dresser (Dressing wheel: SD40Q75M) equipped with an AE sensor under the following dressing conditions: peripheral dressing speed 16.5 m/s, peripheral wheel speed ratio 0.5, dressing lead 0.1 mm/rev, dressing depth of cut 2μm×5 times. High speed steel SKH51/JIS

2% dilution

Hardness: 65HRC Dimensions:100*<sup>l</sup>*

×5*t*×30*h* [mm]

Dimensions:200 *D*× 10*T* [mm]

Grinding method Surface plunge grinding(Up-cut)

Fig. 2. Relationship between surface area *S*(*r*) and mesh size *r* (Sample: surface profile shown in Fig.1).

Fig. 3. Schematic illustration of the experimental setup.

0.1 1 5

Coolant

Charge amplifier

PC

Work

speed *vw*

Mesh size *r* μm

2-*DS*

1

550

Fig. 2. Relationship between surface area *S*(*r*) and mesh size *r*

Surface area

(Sample: surface profile shown in Fig.1).

Workpiece

Peripheral wheel speed *vs*

Wheel depth of cut *a*

Replaceable cBN segment

20mm

10mm

Piezoelectric dynamometer

Fig. 3. Schematic illustration of the experimental setup.

*S*(*r*) μ

m2

*DS*

cBN wheel

= 2.015

600

610

Fig. 4. Measurements of grinding characteristic palameters.

The expermental conditions are listed in Table 1. Representative single crystal cBN grain was used for cBN wheel. The dressing of cBN wheel was performed using a rotary diamond dresser (Dressing wheel: SD40Q75M) equipped with an AE sensor under the following dressing conditions: peripheral dressing speed 16.5 m/s, peripheral wheel speed ratio 0.5, dressing lead 0.1 mm/rev, dressing depth of cut 2μm×5 times. High speed steel SKH51/JIS (M2/ASTM) is used as the workpiece material.


Table 1. Grinding conditions.

Fractal Analysis of Micro Self-Sharpening

sharpening'(Ichida, 2008).

**4. Grinding wheel wear and wheel working surface** 

Fig. 6. Wear mechanisms of abrasive grain during grinding.

Δ

removal (cumulative volume of material removed per unit grinding width) *Vw*' when grinding under the conditions indicated in Table 1. At the same time, some typical sequential SEM images of the wheel working surface with an increase of stock removal are shown in this figure. The wear process of grinding wheel can be divided into two different regions: a) initial wear region over stock removal range from 0 to 1000 mm3/mm, in which a rapid increase of wheel wear occurs with increasing stock removal, b) steady-state wear region over stock removal range larger than 1000 mm3/mm, in which the wheel wear rate maintains a nearly constant value. In the initial wear region, a releasing of grain due to bond fracture and grain

*R* with increasing the accumulated stock

Fig. 7 shows the change in radial wheel wear

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 399

Grinding wheel wear is an important consideration because adversely affects the shape and accuracy of ground surface. Grinding wheel wear by three different mechanisms: attritious grain wear, grain fracture and, and bond fracture, as shown in Fig.6. In attritious wear, the cutting edges of a sharp grain dull by attrition, developing a wear flat. Wear is caused by the interaction of the grain with the workpiece material, involving both physical and chemical reactions. These reactions are complex and involve diffusion, chemical degradation or decomposition of the grain, fracture at a microscopic scale, plastic deformation, and melting. If the wear flat caused by attritious wear is excessive, the grain becomes dull and grinding becomes inefficient and produces undesirable high temperatures. Optimally, the grain should fracture or fragment at a moderate rate, so that new sharp cutting edges are produced continuously during grinding. This phenomenon is self-sharpening. However, self-sharpening by a large fracture is not suitable for precision grinding, because it gives large wheel wear and bad surface roughness during grinding. Therefore, self-sharpening due to micro fracture as shown in Fig.6 is suitable for effective precision grinding, because it offers small wheel wear and good surface roughness. We call this phenomenon 'micro self

## **3.2 Measuring method of wheel surface profile with 3D-SEM**

This 4-channel secondary electron (SE) detection system enables quantitative surface roughness measurements and enhances the topography by displaying the differential signal calculated from the 4 signals. The intensities of these detected signals are determined by the tilt angle of the specimen surface in relation to the geometric positioning of the detectors. The quantitative angular information can be obtained by the subtraction between the signal intensities of the detectors. By calculating 4 tilting angles (two in X-direction and two in Ydirection) on many spots in the X-Y matrix taken on the specimen, the surface topography of the specimen can be accurately re-constructed by integrating these angles over the matrix.

In this system, no eucentric tiling for stereo pairs is required, thereby simplifying operation and allowing much better precision and resolution than conventional SEMs using stereo photogrammetry. The vertical resolution in measuring a 3D profile using this 3D-SEM is 1 nm.

Fig. 5. Illustration of the 4-channel SE detector layout detailing the measurement principle of 3D-SEM.

This 4-channel secondary electron (SE) detection system enables quantitative surface roughness measurements and enhances the topography by displaying the differential signal calculated from the 4 signals. The intensities of these detected signals are determined by the tilt angle of the specimen surface in relation to the geometric positioning of the detectors. The quantitative angular information can be obtained by the subtraction between the signal intensities of the detectors. By calculating 4 tilting angles (two in X-direction and two in Ydirection) on many spots in the X-Y matrix taken on the specimen, the surface topography of the specimen can be accurately re-constructed by integrating these angles over the matrix.

In this system, no eucentric tiling for stereo pairs is required, thereby simplifying operation and allowing much better precision and resolution than conventional SEMs using stereo photogrammetry. The vertical resolution in measuring a 3D profile using this 3D-SEM is

Fig. 5. Illustration of the 4-channel SE detector layout detailing the measurement principle of

**3.2 Measuring method of wheel surface profile with 3D-SEM** 

1 nm.

3D-SEM.

## **4. Grinding wheel wear and wheel working surface**

Grinding wheel wear is an important consideration because adversely affects the shape and accuracy of ground surface. Grinding wheel wear by three different mechanisms: attritious grain wear, grain fracture and, and bond fracture, as shown in Fig.6. In attritious wear, the cutting edges of a sharp grain dull by attrition, developing a wear flat. Wear is caused by the interaction of the grain with the workpiece material, involving both physical and chemical reactions. These reactions are complex and involve diffusion, chemical degradation or decomposition of the grain, fracture at a microscopic scale, plastic deformation, and melting. If the wear flat caused by attritious wear is excessive, the grain becomes dull and grinding becomes inefficient and produces undesirable high temperatures. Optimally, the grain should fracture or fragment at a moderate rate, so that new sharp cutting edges are produced continuously during grinding. This phenomenon is self-sharpening. However, self-sharpening by a large fracture is not suitable for precision grinding, because it gives large wheel wear and bad surface roughness during grinding. Therefore, self-sharpening due to micro fracture as shown in Fig.6 is suitable for effective precision grinding, because it offers small wheel wear and good surface roughness. We call this phenomenon 'micro self sharpening'(Ichida, 2008).

Fig. 6. Wear mechanisms of abrasive grain during grinding.

Fig. 7 shows the change in radial wheel wear Δ*R* with increasing the accumulated stock removal (cumulative volume of material removed per unit grinding width) *Vw*' when grinding under the conditions indicated in Table 1. At the same time, some typical sequential SEM images of the wheel working surface with an increase of stock removal are shown in this figure. The wear process of grinding wheel can be divided into two different regions: a) initial wear region over stock removal range from 0 to 1000 mm3/mm, in which a rapid increase of wheel wear occurs with increasing stock removal, b) steady-state wear region over stock removal range larger than 1000 mm3/mm, in which the wheel wear rate maintains a nearly constant value. In the initial wear region, a releasing of grain due to bond fracture and grain

Fractal Analysis of Micro Self-Sharpening

(*Vw'*

=4 000mm3/mm).

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 401

Fig. 8. High magnification SEM image and its contour map of grain A in Fig.7

Fig. 9. Measuring method of attritious wear flat percentage.

fracture are sometimes observed, as shown in grains C, D and so on. However, they are not observed so much in the steady-state wear region. The wheel wear in steady-state region dominantly occurs due to attritious wear and micro fracture, as shown in grains A, B and so on. As a typical example, a high magnification SEM image of grain A is shown in Fig.8 (a). Fig.8 (b) is its contour map. Wear flat developed due to attritiou wear and some brittle surfaces generated by micro fracture can be observed on the point of the grain.

Fig. 7. Change of radial wheel wear with increasing stock removal and typical sequential SEM images of wheel working suraface.

There is little research that has quantitatively evaluated self-sharpening phenomenon of grinding wheel. We have tried to grasp the actual behavior of self-sharpening and evaluate it by the attritious wear flat area percentage of the grain cutting edge (Ichida, 2008, 2009).

fracture are sometimes observed, as shown in grains C, D and so on. However, they are not observed so much in the steady-state wear region. The wheel wear in steady-state region dominantly occurs due to attritious wear and micro fracture, as shown in grains A, B and so on. As a typical example, a high magnification SEM image of grain A is shown in Fig.8 (a). Fig.8 (b) is its contour map. Wear flat developed due to attritiou wear and some brittle surfaces

Fig. 7. Change of radial wheel wear with increasing stock removal and typical sequential

There is little research that has quantitatively evaluated self-sharpening phenomenon of grinding wheel. We have tried to grasp the actual behavior of self-sharpening and evaluate it by the attritious wear flat area percentage of the grain cutting edge (Ichida, 2008, 2009).

SEM images of wheel working suraface.

generated by micro fracture can be observed on the point of the grain.

Fig. 8. High magnification SEM image and its contour map of grain A in Fig.7 (*Vw'* =4 000mm3/mm).

Fig. 9. Measuring method of attritious wear flat percentage.

Fractal Analysis of Micro Self-Sharpening

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 403

stock removal increases. Such a complicated wear process is evaluated using 3D-fractal dimension. Fractal dimension is calculated in an area of 27.4 × 20.6 μm2 enclosed with white frame in Fig. 10. The center of these areas is almost located in the top part of the cutting edge

Fig. 10. Change in shape of grain cutting edge with accumulated stock removal (Area X on

grain A in Fig.8(a)) (A: attritious wear, MF: micro fracture, F: fracture).

that acts as an effective edge. The range of mesh size *r* is 0.11 < *r* < 6.4 μm.

Figure 9 shows the measuring method of the attritious wear flat percentage. Attritious wear flat area *ag* in the area *S* to be observed is measured using SEM image and contour map made by 3D-profiles. Here, attritious wear flat percentage *Ag* is given by:

$$A\_{\mathcal{g}} = \frac{a\_{\mathcal{g}}}{S} \times 100 \text{[\%]} \tag{6}$$

#### **5. Self-sharpening phenomenon due to micro fracture of cutting edges**

Grain cutting edges on the wheel surface change their shapes in various forms with the progress of wheel wear when the accumulated stock removal *Vw*' increases. Many sequential observations of the grain cutting edge with accumulated stock removal have been carried out using 3D-SEM. The typical result is shown in Fig. 10. Those are high magnification images of area X on grain A in Fig. 8(a).

The surface with a micro unevenness formed by the diamond dresser is observed on the tip of the grain cutting edge after dressing, as shown in Fig. 10 (a). And after grinding the stock removal *V'w* = 500 mm3/mm, an attritious wear flat is observed in the center part on the top surface of the grain cutting edge, as shown in Fig. 10 (b). Moreover, at *V'w* = 2000 mm3/mm, the wear flat becomes larger than that at *V'w* = 500 mm3/mm, as indicated in the comparison between Figs. 10 (b) and (c). The ductile attritious wear flat area takes the largest value at *V'w* = 2000 mm3/mm in the grinding process, as seen from all SEM images in Fig.4. However, between the stock removals from 2000 to 4000 mm3/mm, some micro fractures take place at the lower left side part of cutting edge and consequently the wear flat area decreases a little, as indicated in the comparison between Figs. 10(c) and (d). Moreover,between the stock removals from 4000 to 10000 mm3/mm, as many micro fractures take place repeatedly, the ductile attritious wear flat area is decreased and some new sharp edges are formed on the top surface of cutting edge, as shown in the comparison between Figs. 10 (e) and (f).

In addition, between the stock removals from 10000 to 12000 mm3/mm, a small fracture takes place at the right side part of cutting edge and some new sharp edges are formed, and at the same time the wear flat is formed slightly in the center part on the cutting edge surface, as indicated in the comparison between Figs. 10 (f) and (g). Afterward, between the stock removals from 12000 to 14000 mm3/mm, some new sharp edges due to the micro fracture are observed in the middle part on the cutting edge top surface, while the new attritious wear flat is formed again at the upper part of the cutting edge, as indicated in the comparison between Figs. 10 (g) and (h).

Thus, although the grain cutting edges become dull due to the ductile attritious wear, they can reproduce and maintain their sharpness due to the micro fractures occurred repeatedly on their top surfaces. Namely, an actual behavior of the self-sharpening phenomenon due to the micro fracture may be grasped on the basis on the sequential SEM observation method used in this study.

#### **6. Evaluation of self-sharpening using fractal dimension**

As mentioned above, the shape of the cutting edges on the wheel working surface is variously changed due to the fracture wear or the attritious wear when the accumulated

Figure 9 shows the measuring method of the attritious wear flat percentage. Attritious wear flat area *ag* in the area *S* to be observed is measured using SEM image and contour map

100 %[ ] *<sup>g</sup>*

= × (6)

made by 3D-profiles. Here, attritious wear flat percentage *Ag* is given by:

of cutting edge, as shown in the comparison between Figs. 10 (e) and (f).

**6. Evaluation of self-sharpening using fractal dimension** 

images of area X on grain A in Fig. 8(a).

comparison between Figs. 10 (g) and (h).

used in this study.

*g a*

*S*

Grain cutting edges on the wheel surface change their shapes in various forms with the progress of wheel wear when the accumulated stock removal *Vw*' increases. Many sequential observations of the grain cutting edge with accumulated stock removal have been carried out using 3D-SEM. The typical result is shown in Fig. 10. Those are high magnification

The surface with a micro unevenness formed by the diamond dresser is observed on the tip of the grain cutting edge after dressing, as shown in Fig. 10 (a). And after grinding the stock removal *V'w* = 500 mm3/mm, an attritious wear flat is observed in the center part on the top surface of the grain cutting edge, as shown in Fig. 10 (b). Moreover, at *V'w* = 2000 mm3/mm, the wear flat becomes larger than that at *V'w* = 500 mm3/mm, as indicated in the comparison between Figs. 10 (b) and (c). The ductile attritious wear flat area takes the largest value at *V'w* = 2000 mm3/mm in the grinding process, as seen from all SEM images in Fig.4. However, between the stock removals from 2000 to 4000 mm3/mm, some micro fractures take place at the lower left side part of cutting edge and consequently the wear flat area decreases a little, as indicated in the comparison between Figs. 10(c) and (d). Moreover,between the stock removals from 4000 to 10000 mm3/mm, as many micro fractures take place repeatedly, the ductile attritious wear flat area is decreased and some new sharp edges are formed on the top surface

In addition, between the stock removals from 10000 to 12000 mm3/mm, a small fracture takes place at the right side part of cutting edge and some new sharp edges are formed, and at the same time the wear flat is formed slightly in the center part on the cutting edge surface, as indicated in the comparison between Figs. 10 (f) and (g). Afterward, between the stock removals from 12000 to 14000 mm3/mm, some new sharp edges due to the micro fracture are observed in the middle part on the cutting edge top surface, while the new attritious wear flat is formed again at the upper part of the cutting edge, as indicated in the

Thus, although the grain cutting edges become dull due to the ductile attritious wear, they can reproduce and maintain their sharpness due to the micro fractures occurred repeatedly on their top surfaces. Namely, an actual behavior of the self-sharpening phenomenon due to the micro fracture may be grasped on the basis on the sequential SEM observation method

As mentioned above, the shape of the cutting edges on the wheel working surface is variously changed due to the fracture wear or the attritious wear when the accumulated

**5. Self-sharpening phenomenon due to micro fracture of cutting edges** 

*A*

stock removal increases. Such a complicated wear process is evaluated using 3D-fractal dimension. Fractal dimension is calculated in an area of 27.4 × 20.6 μm2 enclosed with white frame in Fig. 10. The center of these areas is almost located in the top part of the cutting edge that acts as an effective edge. The range of mesh size *r* is 0.11 < *r* < 6.4 μm.

Fig. 10. Change in shape of grain cutting edge with accumulated stock removal (Area X on grain A in Fig.8(a)) (A: attritious wear, MF: micro fracture, F: fracture).

Fractal Analysis of Micro Self-Sharpening

550

2.005

2.010

2.015

Fractal dimension

accumulated stock removal.

stock removal.

*DS*

2.020

a

2.025

2.030

600

Surface area

*S*(*r*) μ

m2

700

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 405

<sup>d</sup> <sup>f</sup>

0.1 1 5

0 5000 10000 15000

l

Mesh size *r* μm

Fig. 12. Relationship between surface area *S*(*r*) and mesh size *r* in areas shown in Fig.11.

k

Stock removal *V'w*

To consider the reason for such complicated change of fractal dimension, the attritious wear flat area percentage of the cutting edge was measured. In this study, a percentage of ductile attritious wear area in same area of 27.4 × 20.6 μm2 used for fractal analysis is measured and defined as attritious wear flat area percentage *Ag*. Figure 14 shows the change in the attritious wear flat area percentage *Ag* of the grain cutting edge with increasing accumulated

d j

Fig. 13. Change in fractal dimension on top surface profile of grain cutting edge with

c

i b

Area c e a

b

h

<sup>g</sup> <sup>f</sup>

e

mm

m

3 /mm

<sup>h</sup> <sup>g</sup>

Figure 11 shows the 3D-profiles of the typical eight areas a, b, ----, h on the cutting edge used for fractal analysis (areas enclosed with white frame in Fig. 10). The relationships between mesh size *r* and surface area *S*(*r*) obtained in these typical eight areas are shown in Fig. 12. This figure indicates that the fractal nature is approved in a region of 0.4 < *r* < 4 μm. Using this relationship, fractal dimension is calculated. The results obtained are shown in Fig. 13. The fractal dimension changes complexly and randomly when the accumulated stock removal increases.

Fig. 11. Sequential 3D-profiles of typical area on grain cutting edge used for fractal analysis (DA: ductile attritious wear, MF: micro fracture).

Figure 11 shows the 3D-profiles of the typical eight areas a, b, ----, h on the cutting edge used for fractal analysis (areas enclosed with white frame in Fig. 10). The relationships between mesh size *r* and surface area *S*(*r*) obtained in these typical eight areas are shown in Fig. 12. This figure indicates that the fractal nature is approved in a region of 0.4 < *r* < 4 μm. Using this relationship, fractal dimension is calculated. The results obtained are shown in Fig. 13. The fractal dimension changes complexly and randomly when the accumulated

Fig. 11. Sequential 3D-profiles of typical area on grain cutting edge used for fractal analysis

(DA: ductile attritious wear, MF: micro fracture).

stock removal increases.

Fig. 12. Relationship between surface area *S*(*r*) and mesh size *r* in areas shown in Fig.11.

Fig. 13. Change in fractal dimension on top surface profile of grain cutting edge with accumulated stock removal.

To consider the reason for such complicated change of fractal dimension, the attritious wear flat area percentage of the cutting edge was measured. In this study, a percentage of ductile attritious wear area in same area of 27.4 × 20.6 μm2 used for fractal analysis is measured and defined as attritious wear flat area percentage *Ag*. Figure 14 shows the change in the attritious wear flat area percentage *Ag* of the grain cutting edge with increasing accumulated stock removal.

Fractal Analysis of Micro Self-Sharpening

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 407

because the attritious wear flat area decreases and micro fracture occurs repeatedly, i.e., selfsharpening due to micro fracture takes place actively. Afterward, although the fractal dimension decreases because of increasing in attritious wear flat at the stock removal 13000 mm3/mm, it increases again because self-sharpening due to micro fracture takes place

As mentioned above, self-sharpening of the grain cutting edge can be characterized using fractal dimension. Especially, these results show that there is a close relationship between fractal dimension *DS* and attritious wear flat percentage *Ag*. Figure 15 shows relationship between fractal dimension and attritious wear flat percentage. The alphabets in Fig.15 correspond to those in Figs.10, 11, 12, 13 and 14. As shown in this figure, fractal dimension decreases with increasing the attritious wear flat percentage and then becomes 2.0 at *Ag* = 100 % (perfect smooth surface) as a limit value. Thus, there is a negative correlation between

Fig.16 and 17 show the changes of grinding forces and ground surface roughness with increasing accumulated stock removal, respectively. Under this grinding condition, grinding forces maintains a stable level in the steady-state wear region. Especially, tangential grinding force keeps a small variation between 4 and 6 N/mm in this wear region. On the other hand, although surface roughness increases with increasing stock removal, its increasing rate maintains comparatively low level. Thus, high grinding ability of cBN wheel is brought from such self-sharpening due to micro fracture of grain cutting edges, that is,

Grinding wheel: cBN80L100V

v w

vs

Accumulated stock removal

Fig. 16. Changes in grinding forces with increasing accumulated stock removal.

Wheel depth of cut

Wheel speed

Work speed

0 2000 4000 6000 8000 10000120001400016000

Initial wear region Steady-state wear region

a

= 10μm

Normal grinding force

Tangential grinding force

V w ' mm 3 /mm

Ft

Fn '

'

= 33m/s

= 0.1m/s

actively over a range of stock removals from 13000 to 14000 mm3/mm.

fractal dimension and attritious wear flat percentage.

micro self-sharpening phenomenon.

0

10

Grinding force

F<sup>n</sup>

Ft

' N/mm

,

'

20

30

40

50

**7. Effect of self-sharpening on grinding characteristics** 

Fig. 14. Change in attritious wear flat percentage on top surface of grain cutting edge with accumulated stock removal.

Fig. 15. Relationship between fractal dimension *DS* and attritious wear flat percentage *Ag*

As shown in Figs. 13 and 14, the cutting edge after dressing comparatively takes a high fractal dimension (*Ds* =2.02), because it has complicated surface with a micro ruggedness formed by the diamond dresser. And then, between the stock removals from 500 to 3000mm3/mm, fractal dimension decreases because the attritious wear flat increases with the accumulated stock removal. In addition, between the stock removals from 2000 to 4000 mm3/mm, fractal dimension indicates the lowest value (*Ds* =2.01) because the attritious wear flat takes the highest value. Afterward, over a range of stock removals from 4000 to 6000 mm3/mm, fractal dimension tends to increase because the attritious wear flat decreases and new sharp cutting edges are formed by self-sharpening due to micro fractures. However, between the stock removals from 6000 to 8000 mm3/mm, fractal dimension decreases slightly because of a little increase in attritious wear flat area. Moreover, fractal dimension increases rapidly over a range of stock removals from 8000 to 10000 mm3/mm

d j

0 5000 10000 15000

Stock removal *V'w* mm

Fig. 14. Change in attritious wear flat percentage on top surface of grain cutting edge with

Fig. 15. Relationship between fractal dimension *DS* and attritious wear flat percentage *Ag*

As shown in Figs. 13 and 14, the cutting edge after dressing comparatively takes a high fractal dimension (*Ds* =2.02), because it has complicated surface with a micro ruggedness formed by the diamond dresser. And then, between the stock removals from 500 to 3000mm3/mm, fractal dimension decreases because the attritious wear flat increases with the accumulated stock removal. In addition, between the stock removals from 2000 to 4000 mm3/mm, fractal dimension indicates the lowest value (*Ds* =2.01) because the attritious wear flat takes the highest value. Afterward, over a range of stock removals from 4000 to 6000 mm3/mm, fractal dimension tends to increase because the attritious wear flat decreases and new sharp cutting edges are formed by self-sharpening due to micro fractures. However, between the stock removals from 6000 to 8000 mm3/mm, fractal dimension decreases slightly because of a little increase in attritious wear flat area. Moreover, fractal dimension increases rapidly over a range of stock removals from 8000 to 10000 mm3/mm

k

l

h

m

g f

3 /mm

e

0

10

20

percentage

*Ag* %

Attritious wear flat

accumulated stock removal.

30

40

c

i

b

a

50

because the attritious wear flat area decreases and micro fracture occurs repeatedly, i.e., selfsharpening due to micro fracture takes place actively. Afterward, although the fractal dimension decreases because of increasing in attritious wear flat at the stock removal 13000 mm3/mm, it increases again because self-sharpening due to micro fracture takes place actively over a range of stock removals from 13000 to 14000 mm3/mm.

As mentioned above, self-sharpening of the grain cutting edge can be characterized using fractal dimension. Especially, these results show that there is a close relationship between fractal dimension *DS* and attritious wear flat percentage *Ag*. Figure 15 shows relationship between fractal dimension and attritious wear flat percentage. The alphabets in Fig.15 correspond to those in Figs.10, 11, 12, 13 and 14. As shown in this figure, fractal dimension decreases with increasing the attritious wear flat percentage and then becomes 2.0 at *Ag* = 100 % (perfect smooth surface) as a limit value. Thus, there is a negative correlation between fractal dimension and attritious wear flat percentage.

## **7. Effect of self-sharpening on grinding characteristics**

Fig.16 and 17 show the changes of grinding forces and ground surface roughness with increasing accumulated stock removal, respectively. Under this grinding condition, grinding forces maintains a stable level in the steady-state wear region. Especially, tangential grinding force keeps a small variation between 4 and 6 N/mm in this wear region. On the other hand, although surface roughness increases with increasing stock removal, its increasing rate maintains comparatively low level. Thus, high grinding ability of cBN wheel is brought from such self-sharpening due to micro fracture of grain cutting edges, that is, micro self-sharpening phenomenon.

Fig. 16. Changes in grinding forces with increasing accumulated stock removal.

Fractal Analysis of Micro Self-Sharpening

859021-0, pp.55-62.

0289

1087-1357

0007-8506

pp.94-99, ISSN 1344-7653.

pp.1760-1764, ISSN0912-0289

pp.106-113, ISSN 1344-7653.

*Engineering*, Vol.24, No.2, pp.148-149.

Vol.55, No.1, pp.347-350, ISSN 0007-8506

New York, (1983), pp.109-111, ISBN4-532-06254-3

Phenomenon in Grinding with Cubic Boron Nitride (cBN) Wheels 409

Tsuwa, H. (1961). On the Behaviors of Abrasive Grains in Grinding Process (Part 4)-

Kalpakjian, S. (1995), *Manufacturing Engineering and Technology*, Third Edition, Addison-Wesley Publishing Company Inc. , ISBN 0-201-53846-6, New York, pp.795-798. Show, M. C. (1996), *Principles of Abrasive Processing*, Clarendon Press, Oxford, ISBN 0-19-

Malkin, S. (1989). *Grinding Technology: Theory and Applications of Machining with Abrasives*, Ellis Horwood Limited, Chichester, UK, PP.197-202. ISBN 0-85312-756-5 Ichida, Y.; Fredj, N. B. & Usui, N. (1995). The Micro Fracture Wear of Cutting Edges in CBN Grinding, *The Second International ABTEC Conference*, Vol. 11, (1995), pp. 501-504. Ichida, Y.; Kishi, K.; Suyama, Y. & Okubo, J. (1989). Study of Creep Feed Grinding with CBN

Ichida, Y. & Kishi, K. (1997). The Development of Nanocrystalline cBN for Enhanced

Webster, J. & Tricard, M. (2004). Innovations in Abrasive Production for Precision Grinding,

Ichida, Y.; Sato, R.: Morimoto, Y. & Inoue, Y. (2006). Profile Grinding of Superalloys with

Guo, C.; Shi, Z.; Attia, H. & Mclntosh, D. (2007). Power and Wheel Wear for Grinding Nickel

Comley, P.; Walton, I.; Jin, T. & Stephenson, D. J. (2006). A High Material Removal Rate

Mandelbrot, B. B. (1983). The Fractal Geometry of Nature, Freeman, W. H. and Company,

Mandelbrot, B. B.; Passoja, D. E. & Paullay, A. J. (1984). Fractal Characterization of Fracture

Hagiwara, S.; Obikawa, T. & Yanai, H. (1995). Evaluation of Lapping Grains Based on Shape

Itoh, N.; Tsukada, T.& Sasajima, K. (1990). Three-Dimensional Characerization of Engineer-

Sakai, T.; Sakai, T. & Ueno, A. (1998). Fractal Analysis of Metal Surface Mechanically

*Engineers, Series A*, Vol. 64, No. 620, (1998-4), pp.1104-1112, ISSN 1884-8338. Fujimoto, M.; Ichida, Y.; Sato, R. & Morimoto, Y. (2006), Characterization of Wheel Surface

Surfaces of Metals, *Nature*, Vol. 308, pp.1571-1572, ISSN 0028-0836.

*Annals of the CIRP*, Vol.53, No.2, pp.597-617, ISSN 0007-8506

*Engineering*, Vol. 27, No. 11,(1961), pp. 719-725 , ISSN0912-0289

Microscopic Observations of Cutting Edges-, *Journal of the Japan Society for Precision* 

Wheels, -Characteristics of Wheel Wear-, Vol.55, No.8, pp.1468-1474, ISSN0912-

Superalloy Grinding Performance, *Transactions of the ASME, Journal of Manufacturing, Science and Engineering*, Vol. 119, No. 1, (1997), pp.110-117. ISSN

Ultrafine-Crystalline cBN Wheels, *JSME International Journal, Series C*, Vol.49, No.1,

Alloy with Plated CBN Wheels, *Annals of the CIRP*, Vol.56, No.1, pp.343-346, ISSN

Grinding Process for the Production of Automotive Crankshafts, *Annals of the CIRP*,

Characteristics, *Journal of the Japan Society for Precision Engineering*, Vol. 61, No.12,

ing Surface by Fractal Dimension, *Bulletin of the Japan Society for Prcision* 

Finished by Several Methods, *Transactions of the Japan Society of Mechanical* 

Topography in cBN Grinding, *JSME International Journal, Series C,* Vol.49, No.1,

Fig. 17. Change of ground surface roughness with increasing accumulated stock removal.

### **8. Conclusions**

The changes in three-dimensional surface profile of grain cutting edge in the grinding process with cBN wheels have been measured using a 3D-SEM and evaluated by means of fractal dimension. The main results obtained in this study are summarized as follows;


#### **9. Acknowledgment**

This research was supported in part by Grants-in-Aid for General Science Research (C) (No.19560106) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.

#### **10. References**

Yoshikawa, H. (1960). Process of Wear in Grinding Wheel with Fracture of Bond and Grain, *Journal of the Japan Society for Precision Engineering*, Vol.26, No. 11, (1960), pp.691- 700, ISSN0912-0289

Grinding wheel: cBN80L100V

v w

v s

Accumulated stock removal

Fig. 17. Change of ground surface roughness with increasing accumulated stock removal.

The changes in three-dimensional surface profile of grain cutting edge in the grinding process with cBN wheels have been measured using a 3D-SEM and evaluated by means of fractal dimension. The main results obtained in this study are summarized as follows;

1. Actual behavior of self-sharpening phenomenon due to the micro fracture in the grinding process can be grasped using sequential observation method with 3D-SEM. 2. The fractal dimension for surface profile of the cutting edge formed by the micro fracture is higher than that of the cutting edge formed due to ductile attitious wear. An increase in ductile attritous wear flat area on the grain cutting edge results in a decrease

3. The complicated changes in shape of the cutting edge due to self-sharpening can be

This research was supported in part by Grants-in-Aid for General Science Research (C) (No.19560106) from the Ministry of Education, Culture, Sports, Science and Technology of

Yoshikawa, H. (1960). Process of Wear in Grinding Wheel with Fracture of Bond and Grain,

*Journal of the Japan Society for Precision Engineering*, Vol.26, No. 11, (1960), pp.691-

Wheel depth of cut

Wheel speed

Work speed

0 2000 4000 6000 8000 10000120001400016000

Initial wear region Steady-state wear region

= 33m/s

a

= 10μm

V w ' mm 3 /mm

= 0.1m/s

0.0

in fractal dimension for its surface profile.

evaluated quantitatively using fractal dimension.

0.5

1.0

Srface roughness Ra

**8. Conclusions** 

**9. Acknowledgment** 

**10. References** 

700, ISSN0912-0289

Japan.

μ

m

1.5

2.0


**21** 

*University of Žilina, Slovak Republic* 

**Evolution of Phases in a Recycled Al-Si Cast** 

Aluminium has been acquiring increasing significance for the past few decades due to its excellent properties and diversified range of applications. Aluminium has been recognized as one of the best candidate materials for various applications by different sectors such as automotive, construction, aerospace, etc. The increasing demand for aluminium-based products and further globalization of the aluminium industry have contributed significantly to the higher consumption of aluminium scrap for re-production of aluminium alloys

Secondary aluminium alloys are made out of aluminium scrap and workable aluminium garbage by recycling. Production of aluminium alloys belong to heavy source fouling of life environs. Care of environment in industry of aluminium connects with the decreasing consumptions resource as energy, materials, waters and soil, with increase recycling and extension life of products. More than half aluminium on the present produce in European Union comes from recycled raw material. By primary aluminium production we need a lot of energy and constraints decision mining of bauxite so European Union has big interest of share recycling aluminium, and therefore increase interest about secondary aluminium

The increase in recycled metal becoming available is a positive trend, as secondary aluminium produced from recycled metal requires only about 2.8 kWh/kg of metal produced while primary aluminium production requires about 45 kWh/kg produced. It is to the aluminium industry's advantage to maximize the amount of recycled metal, for both the energy-savings and the reduction of dependence upon overseas sources. The remelting of recycled metal saves almost 95 % of the energy needed to produce prime aluminium from ore, and, thus, triggers associated reductions in pollution and greenhouse emissions from mining, ore refining, and melting. Increasing the use of recycled metal is also quite important from an ecological standpoint, since producing aluminium by recycling creates only about 5 % as much CO2 as by primary production (Das, 2006; Das & Gren, 2010).

Today, a large amount of new aluminium products are made by recycled (secondary) alloys. This represents a growing ''energy bank'' of aluminium available for recycling at the end of components' lives, and thus recycling has become a major issue. The future growth offers an

alloys and cast stock from them (Sencakova & Vircikova, 2007).

**1. Introduction** 

(Mahfoud et al., 2010).

**Alloy During Solution Treatment** 

Eva Tillová, Mária Chalupová and Lenka Hurtalová


## **Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment**

Eva Tillová, Mária Chalupová and Lenka Hurtalová *University of Žilina, Slovak Republic* 

## **1. Introduction**

410 Scanning Electron Microscopy

Ichida, Y.; Sato, R.; Fujimoto, M. & Tanaka, H. (2008), Fractal Analysis of Grain Cutting Edge

Ichida, Y.; Fujimoto, M.; Akbari, J. & Sato, R. (2008). Evaluation of Cutting Edge wear in cBN

*Symposium on Manufacturing and Materials*, Monastir, Tunisia, pp.287-294.

*Systems, and Manufacturing*, Vol.2, No.4, pp.640-650 ISSN 1881-3054 . Ichida, Y.; Sato, R.; Fujimoto, M. & Fredj, N. B. (2009). Fractal Analysis of Self-Sharpening

ISBN-13978-0-87849-364-7

Wear in Superabrasive Grinding, *JSME Journal of Advanced Mechanical Design,* 

Phenomenon in cBN Grinding, *Key Engineering*, Vols. 389-390, (2009), pp.42-47,

Grinding Based on Fractal Analysis, *6th International Scientific and technical* 

Aluminium has been acquiring increasing significance for the past few decades due to its excellent properties and diversified range of applications. Aluminium has been recognized as one of the best candidate materials for various applications by different sectors such as automotive, construction, aerospace, etc. The increasing demand for aluminium-based products and further globalization of the aluminium industry have contributed significantly to the higher consumption of aluminium scrap for re-production of aluminium alloys (Mahfoud et al., 2010).

Secondary aluminium alloys are made out of aluminium scrap and workable aluminium garbage by recycling. Production of aluminium alloys belong to heavy source fouling of life environs. Care of environment in industry of aluminium connects with the decreasing consumptions resource as energy, materials, waters and soil, with increase recycling and extension life of products. More than half aluminium on the present produce in European Union comes from recycled raw material. By primary aluminium production we need a lot of energy and constraints decision mining of bauxite so European Union has big interest of share recycling aluminium, and therefore increase interest about secondary aluminium alloys and cast stock from them (Sencakova & Vircikova, 2007).

The increase in recycled metal becoming available is a positive trend, as secondary aluminium produced from recycled metal requires only about 2.8 kWh/kg of metal produced while primary aluminium production requires about 45 kWh/kg produced. It is to the aluminium industry's advantage to maximize the amount of recycled metal, for both the energy-savings and the reduction of dependence upon overseas sources. The remelting of recycled metal saves almost 95 % of the energy needed to produce prime aluminium from ore, and, thus, triggers associated reductions in pollution and greenhouse emissions from mining, ore refining, and melting. Increasing the use of recycled metal is also quite important from an ecological standpoint, since producing aluminium by recycling creates only about 5 % as much CO2 as by primary production (Das, 2006; Das & Gren, 2010).

Today, a large amount of new aluminium products are made by recycled (secondary) alloys. This represents a growing ''energy bank'' of aluminium available for recycling at the end of components' lives, and thus recycling has become a major issue. The future growth offers an

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 413

exists that they may perform quite satisfactorily in applications such as those listed where

As an experimental material recycled (secondary) hypoeutectic AlSi9Cu3 alloy, in the form of 12.5 kg ingots, was used. The alloy was molten into the sand form (sand casting). The melting temperature was maintained at 760 °C ± 5 °C. Molten metal was before casting purified with salt AlCu4B6. The melt was not modified or grain refined. The chemical analysis of AlSi9Cu3 cast alloy was carried out using arc spark spectroscopy. The chemical

> Si Cu Mn Fe Mg Ni Pb Zn Ti Al 10.7 2.4 0.22 < 0.8 0.47 0.08 0.11 1.1 0.03 rest

AlSi9Cu3 cast alloy has lower corrosion resistance and is suitable for high temperature applications (dynamic exposed casts, where are not so big requirements on mechanical properties) - it means to max. 250 °C. Experimental samples (standard tensile test specimens) were given a T4 heat treatment - solution treatment for 2, 4, 8, 16 or 32 hours at three temperatures (505 °C, 515 °C and 525 °C); water quenching at 40 °C and natural aging for 24 hours at room temperature. After heat treatment samples were subjected to mechanical test. For as cast state, each solution temperature and each aging time, a

Metallographic samples were prepared from selected tensile specimens (after testing) and the microstructures were examined by optical (Neophot 32) and scanning electron microscopy. Samples were prepared by standards metallographic procedures (mounting in bakelite, wet ground, DP polished with 3 μm diamond pastes, finally polished with commercial fine silica slurry (STRUERS OP-U) and etched by Dix-Keller. For setting of Ferich intermetallic phases was used etching by H2SO4. For setting of Cu-rich intermetallic

Some samples were also deep-etched for 30 s in HCl solution in order to reveal the threedimensional morphology of the eutectic silicon and intermetallic phases (Tillova & Chalupova, 2001, 2009). The specimen preparation procedure for deep-etching consists of dissolving the aluminium matrix in a reagent that will not attack the eutectic components or intermetallic phases. The residuals of the etching products should be removed by intensive rinsing in alcohol. The preliminary preparation of the specimen is not necessary, but removing the superficial deformed or contaminated layer can shorten the process. To determine the chemical composition of the intermetallic phases was employed scanning electron microscopy (SEM) TESCAN VEGA LMU with EDX analyser BRUKER QUANTAX. Quantitative metallography (Skocovsky & Vasko, 2007; Vasko & Belan, 2007; Belan, 2008; Vasko, 2008; Martinkovic, 2010) was carried out on an Image Analyzer NIS - Elements 3.0 to quantify phase's changes during heat treatment. A minimum of 20 pictures at 500 x

service life is determined by other factors (Taylor, 2004).

**2. Experimental material and methodology** 

Table 1. Chemical composition of the alloy (wt. %)

minimum of five specimens were tested.

phases was used etching by HNO3.

magnification of the polish per specimen were taken.

composition is given in the table 1.

opportunity for new recycling technologies and practices to maximize scrap quality; improve efficiency and reduce cost.

Aluminium-silicon (Al-Si) cast alloys are fast becoming the most universal and popular commercial materials, comprising 85 % to 90 % of the aluminium cast parts produced for the automotive industry, due to their high strength-to-weight ratio, excellent castability, high corrosion resistant and chemical stability, good mechanical properties, machinability and wear resistance. Mg or Cu addition makes Al-Si alloy heat treatable.

The alloys of the Al-Si-Cu system have become increasingly important in recent years, mainly in the automotive industry that uses recycled (secondary) aluminium in the form of various motor mounts, pistons, cylinder heads, heat exchangers, air conditioners, transmissions housings, wheels, fenders and so on due to their high strength at room and high temperature (Rios & Caram, 2003; Li et al., 2004; Michna et al., 2007). The increased use of these recycled alloys demands a better understanding of its response to mechanical properties.

The quality of recycled Al-Si casting alloys is considered to be a key factor in selecting an alloy casting for a particular engineering application. Based on the Al-Si system, the main alloying elements are copper (Cu) or magnesium (Mg) and certain amount of iron (Fe), manganese (Mn) and more, that are present either accidentally, or they are added deliberately to provide special material properties. These elements partly go into solid solution in the matrix and partly form intermetallic particles during solidification. The size, volume and morphology of intermetallic phases are functions of chemistry, solidification conditions and heat treatment (Li, 1996; Paray & Gruzleski, 1994; Tillova & Panuskova, 2007, 2008).

Copper substantially improves strength and hardness in the as-cast and heat-treated conditions. Alloys containing 4 % to 6 % Cu respond most strongly to thermal treatment. Copper generally reduces resistance to general corrosion and, in specific compositions and material conditions, stress corrosion susceptibility. Additions of copper also reduce hot tear resistance and decrease castability. Magnesium is the basis for strength and hardness development in heat-treated Al-Si alloys too and is commonly used in more complex Al-Si alloys containing copper, nickel, and other elements for the same purpose.

Iron considers the principal impurity and detrimental alloying element for Al-Si-Cu alloys. Iron improves hot tear resistance and decreases the tendency for die sticking or soldering in die casting. Increases in iron content are, however, accompanied by substantially decreased ductility. Iron reacts to form a myriad of insoluble phases in aluminium alloy melts, the most common of which are Al3Fe, Al6FeMn, and α-Al5FeSi. These essentially insoluble phases are responsible for improvements in strength, especially at elevated temperature. As the fraction of insoluble phase increases with increased iron content, casting considerations such as flowability and feeding characteristics are adversely affected. Iron also lead to the formation of excessive shrinkage porosity defects in castings (Warmuzek, 2004a; Taylor, 2004; Shabestari, 2004; Caceres et al., 2003; Wang et al. 2001; Tillova & Chalupova, 2010).

It is clear that the morphology of Fe-rich intermetallic phases influences harmfully also fatigue properties (Taylor, 2004; Tillova & Chalupova, 2010). It is recognized that recycled Al-Si-Cu alloys are not likely to be suitable for fracture-critical components, where higher levels of Fe and Si have been shown to degrade fracture resistance. However the likelihood exists that they may perform quite satisfactorily in applications such as those listed where service life is determined by other factors (Taylor, 2004).

## **2. Experimental material and methodology**

412 Scanning Electron Microscopy

opportunity for new recycling technologies and practices to maximize scrap quality;

Aluminium-silicon (Al-Si) cast alloys are fast becoming the most universal and popular commercial materials, comprising 85 % to 90 % of the aluminium cast parts produced for the automotive industry, due to their high strength-to-weight ratio, excellent castability, high corrosion resistant and chemical stability, good mechanical properties, machinability and

The alloys of the Al-Si-Cu system have become increasingly important in recent years, mainly in the automotive industry that uses recycled (secondary) aluminium in the form of various motor mounts, pistons, cylinder heads, heat exchangers, air conditioners, transmissions housings, wheels, fenders and so on due to their high strength at room and high temperature (Rios & Caram, 2003; Li et al., 2004; Michna et al., 2007). The increased use of these recycled alloys demands a better understanding of its response to mechanical

The quality of recycled Al-Si casting alloys is considered to be a key factor in selecting an alloy casting for a particular engineering application. Based on the Al-Si system, the main alloying elements are copper (Cu) or magnesium (Mg) and certain amount of iron (Fe), manganese (Mn) and more, that are present either accidentally, or they are added deliberately to provide special material properties. These elements partly go into solid solution in the matrix and partly form intermetallic particles during solidification. The size, volume and morphology of intermetallic phases are functions of chemistry, solidification conditions and heat treatment

Copper substantially improves strength and hardness in the as-cast and heat-treated conditions. Alloys containing 4 % to 6 % Cu respond most strongly to thermal treatment. Copper generally reduces resistance to general corrosion and, in specific compositions and material conditions, stress corrosion susceptibility. Additions of copper also reduce hot tear resistance and decrease castability. Magnesium is the basis for strength and hardness development in heat-treated Al-Si alloys too and is commonly used in more complex Al-Si

Iron considers the principal impurity and detrimental alloying element for Al-Si-Cu alloys. Iron improves hot tear resistance and decreases the tendency for die sticking or soldering in die casting. Increases in iron content are, however, accompanied by substantially decreased ductility. Iron reacts to form a myriad of insoluble phases in aluminium alloy melts, the most common of which are Al3Fe, Al6FeMn, and α-Al5FeSi. These essentially insoluble phases are responsible for improvements in strength, especially at elevated temperature. As the fraction of insoluble phase increases with increased iron content, casting considerations such as flowability and feeding characteristics are adversely affected. Iron also lead to the formation of excessive shrinkage porosity defects in castings (Warmuzek, 2004a; Taylor, 2004; Shabestari, 2004; Caceres et al., 2003; Wang et al. 2001; Tillova & Chalupova, 2010).

It is clear that the morphology of Fe-rich intermetallic phases influences harmfully also fatigue properties (Taylor, 2004; Tillova & Chalupova, 2010). It is recognized that recycled Al-Si-Cu alloys are not likely to be suitable for fracture-critical components, where higher levels of Fe and Si have been shown to degrade fracture resistance. However the likelihood

wear resistance. Mg or Cu addition makes Al-Si alloy heat treatable.

(Li, 1996; Paray & Gruzleski, 1994; Tillova & Panuskova, 2007, 2008).

alloys containing copper, nickel, and other elements for the same purpose.

improve efficiency and reduce cost.

properties.

As an experimental material recycled (secondary) hypoeutectic AlSi9Cu3 alloy, in the form of 12.5 kg ingots, was used. The alloy was molten into the sand form (sand casting). The melting temperature was maintained at 760 °C ± 5 °C. Molten metal was before casting purified with salt AlCu4B6. The melt was not modified or grain refined. The chemical analysis of AlSi9Cu3 cast alloy was carried out using arc spark spectroscopy. The chemical composition is given in the table 1.


Table 1. Chemical composition of the alloy (wt. %)

AlSi9Cu3 cast alloy has lower corrosion resistance and is suitable for high temperature applications (dynamic exposed casts, where are not so big requirements on mechanical properties) - it means to max. 250 °C. Experimental samples (standard tensile test specimens) were given a T4 heat treatment - solution treatment for 2, 4, 8, 16 or 32 hours at three temperatures (505 °C, 515 °C and 525 °C); water quenching at 40 °C and natural aging for 24 hours at room temperature. After heat treatment samples were subjected to mechanical test. For as cast state, each solution temperature and each aging time, a minimum of five specimens were tested.

Metallographic samples were prepared from selected tensile specimens (after testing) and the microstructures were examined by optical (Neophot 32) and scanning electron microscopy. Samples were prepared by standards metallographic procedures (mounting in bakelite, wet ground, DP polished with 3 μm diamond pastes, finally polished with commercial fine silica slurry (STRUERS OP-U) and etched by Dix-Keller. For setting of Ferich intermetallic phases was used etching by H2SO4. For setting of Cu-rich intermetallic phases was used etching by HNO3.

Some samples were also deep-etched for 30 s in HCl solution in order to reveal the threedimensional morphology of the eutectic silicon and intermetallic phases (Tillova & Chalupova, 2001, 2009). The specimen preparation procedure for deep-etching consists of dissolving the aluminium matrix in a reagent that will not attack the eutectic components or intermetallic phases. The residuals of the etching products should be removed by intensive rinsing in alcohol. The preliminary preparation of the specimen is not necessary, but removing the superficial deformed or contaminated layer can shorten the process. To determine the chemical composition of the intermetallic phases was employed scanning electron microscopy (SEM) TESCAN VEGA LMU with EDX analyser BRUKER QUANTAX.

Quantitative metallography (Skocovsky & Vasko, 2007; Vasko & Belan, 2007; Belan, 2008; Vasko, 2008; Martinkovic, 2010) was carried out on an Image Analyzer NIS - Elements 3.0 to quantify phase's changes during heat treatment. A minimum of 20 pictures at 500 x magnification of the polish per specimen were taken.

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 415

A number of Fe-rich intermetallic phases, including α (Al8Fe2Si or Al15(FeMn)3Si2), β (Al5FeSi), π (Al8Mg3FeSi6), and δ (Al4FeSi2), have been identified in Al-Si cast alloys (Samuel et al., 1996; Taylor, 2004; Seifeddine, 2007; Seifeddine et al. 2008; Moustafa, 2009; Fang et al.,

a) optical microscopy b) SEM Fig. 1. Microstructure of recycled AlSi9Cu3 cast alloy (1 – α-phase, 2 – eutectic silicon,

a) deep etch. HCl, SEM b) etch. Dix-Keller

In experimental AlSi9Cu3 alloy was observed the two main types of Fe-rich intermetallic phases, Al5FeSi with monoclinic crystal structure (know as beta- or β-phase) and Al15(FeMn)3Si2 (know as alpha- or α-phase) with cubic crystal structure. The first phase (Al5FeSi) precipitates in the interdendritic and intergranular regions as platelets (appearing as needles in the metallographic microscope - Fig. 3). Long and brittle Al5FeSi platelets (more than 500 µm) can adversely affect mechanical properties, especially ductility, and also

3 – Fe-rich phases, 4 – Cu-rich phases), etch. Dix-Keller

Fig. 2. Morphology of eutectic silicon

2007; Lu & Dahle, 2005).

Hardness measurement was preformed by a Brinell hardness tester with a load of 62.5 kp, 2.5 mm diameter ball and a dwell time of 15 s. The Brinell hardness value at each state was obtained by an average of at least six measurements. The phases Vickers microhardness was measured using a MHT-1 microhardness tester under a 1g load for 10 s (HV 0.01). Twenty measurements were taken per sample and the median microhardness was determined.

## **3. Results and discussion**

### **3.1 Microstructure of recycled AlSi9Cu3 cast alloy**

Controlling the microstructure during solidification is, therefore, very important. The Al-Si eutectic and intermetallic phases form during the final stage of the solidification. How the eutectic nucleates and grows have been shown to have an effect on the formation of defects such as porosity and microporosity too. The defects, the morphology of eutectic and the morphology of intermetallic phases have an important effect on the ultimate mechanical properties of the casting.

As recycling of aluminium alloys becomes more common, sludge will be a problem of increasing importance due to the concentration of Fe, Mn, Cr and Si in the scrap cycle. During the industrial processing of the Al-Si alloys, these elements go into solid solution but they also form different intermetallic phases. The formation of these phases should correspond to successive reaction during solidification - table 2 (Krupiński et al., 2011; Maniara et al., 2007; Mrówka-Nowotnik & Sieniawski, 2011; Dobrzański et al., 2007, Tillova & Chalupova, 2009). Thus, control of these phases e. g. quantitative analysis (Vasko & Belan, 2007; Martinkovic, 2010) is of considerable technological importance. Typical structures of the recycled as-cast AlSi9Cu3 alloys are shown in Fig. 1. The microstructure consists of dendrites α-phase (1), eutectic (mixture of α-matrix and spherical Si-phases - 2) and variously type's intermetallic Fe- and Cu-rich phases (3 and 4).


Table 2. Reactions occurring during the solidification of AlSi9Cu3 alloys

The α-matrix precipitates from the liquid as the primary phase in the form of dendrites and is nominally comprised of Al and Si. Experimental material was not modified and so eutectic Si particles are in a form of platelets (Fig. 2a), which on scratch pattern are in a form of needles – Fig. 2b (Skocovsky et al., 2009; Tillova & Chalupova, 2001; 2009).

Iron is one of the most critical alloying elements, because Fe is the most common and usually detrimental impurity in cast Al-Si alloys. Iron impurities can either come from the use of steel tools or scrap materials or be acquired during subsequent melting, remelting and casting, e.g. by contamination from the melting pot etc.

Hardness measurement was preformed by a Brinell hardness tester with a load of 62.5 kp, 2.5 mm diameter ball and a dwell time of 15 s. The Brinell hardness value at each state was obtained by an average of at least six measurements. The phases Vickers microhardness was measured using a MHT-1 microhardness tester under a 1g load for 10 s (HV 0.01). Twenty measurements were taken per sample and the median microhardness was determined.

Controlling the microstructure during solidification is, therefore, very important. The Al-Si eutectic and intermetallic phases form during the final stage of the solidification. How the eutectic nucleates and grows have been shown to have an effect on the formation of defects such as porosity and microporosity too. The defects, the morphology of eutectic and the morphology of intermetallic phases have an important effect on the ultimate mechanical

As recycling of aluminium alloys becomes more common, sludge will be a problem of increasing importance due to the concentration of Fe, Mn, Cr and Si in the scrap cycle. During the industrial processing of the Al-Si alloys, these elements go into solid solution but they also form different intermetallic phases. The formation of these phases should correspond to successive reaction during solidification - table 2 (Krupiński et al., 2011; Maniara et al., 2007; Mrówka-Nowotnik & Sieniawski, 2011; Dobrzański et al., 2007, Tillova & Chalupova, 2009). Thus, control of these phases e. g. quantitative analysis (Vasko & Belan, 2007; Martinkovic, 2010) is of considerable technological importance. Typical structures of the recycled as-cast AlSi9Cu3 alloys are shown in Fig. 1. The microstructure consists of dendrites α-phase (1), eutectic (mixture of α-matrix and spherical Si-phases - 2) and

Reactions Temperature, °C

α - dendritic network 609

Liq. → α - phase + Al15Mn3Si2 + Al5FeSi 590 Liq. → α - phase + Si + Al5FeSi 575 Liq. → α - phase + Al2Cu + Al5FeSi + Si 525 Liq. → α - phase + Al2Cu + Si + Al5Mg8Si6Cu2 507

The α-matrix precipitates from the liquid as the primary phase in the form of dendrites and is nominally comprised of Al and Si. Experimental material was not modified and so eutectic Si particles are in a form of platelets (Fig. 2a), which on scratch pattern are in a form

Iron is one of the most critical alloying elements, because Fe is the most common and usually detrimental impurity in cast Al-Si alloys. Iron impurities can either come from the use of steel tools or scrap materials or be acquired during subsequent melting, remelting

**3. Results and discussion** 

properties of the casting.

**3.1 Microstructure of recycled AlSi9Cu3 cast alloy** 

variously type's intermetallic Fe- and Cu-rich phases (3 and 4).

Table 2. Reactions occurring during the solidification of AlSi9Cu3 alloys

of needles – Fig. 2b (Skocovsky et al., 2009; Tillova & Chalupova, 2001; 2009).

and casting, e.g. by contamination from the melting pot etc.

A number of Fe-rich intermetallic phases, including α (Al8Fe2Si or Al15(FeMn)3Si2), β (Al5FeSi), π (Al8Mg3FeSi6), and δ (Al4FeSi2), have been identified in Al-Si cast alloys (Samuel et al., 1996; Taylor, 2004; Seifeddine, 2007; Seifeddine et al. 2008; Moustafa, 2009; Fang et al., 2007; Lu & Dahle, 2005).

a) optical microscopy b) SEM

Fig. 1. Microstructure of recycled AlSi9Cu3 cast alloy (1 – α-phase, 2 – eutectic silicon, 3 – Fe-rich phases, 4 – Cu-rich phases), etch. Dix-Keller

a) deep etch. HCl, SEM b) etch. Dix-Keller

Fig. 2. Morphology of eutectic silicon

In experimental AlSi9Cu3 alloy was observed the two main types of Fe-rich intermetallic phases, Al5FeSi with monoclinic crystal structure (know as beta- or β-phase) and Al15(FeMn)3Si2 (know as alpha- or α-phase) with cubic crystal structure. The first phase (Al5FeSi) precipitates in the interdendritic and intergranular regions as platelets (appearing as needles in the metallographic microscope - Fig. 3). Long and brittle Al5FeSi platelets (more than 500 µm) can adversely affect mechanical properties, especially ductility, and also

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 417

The deleterious effect of Al5FeSi can be reduced by increasing the cooling rate, superheating the molten metal, or by the addition of a suitable "neutralizer" like Mn, Co, Cr, Ni, V, Mo and Be. The most common addition has been manganese. Excess Mn may reduce Al5FeSi phase and promote formation Fe-rich phases Al15(FeMn)3Si2 in form "skeleton like" or in form "Chinese script" (Seifeddine et al., 2008; Taylor, 2004) (Fig. 4). This compact morphology "Chinese script" (or skeleton - like) does not initiate cracks in the cast material to the same extent as Al5FeSi does and phase Al15(FeMn)3Si2 is considered less harmful to the mechanical properties than β phase (Ma et al., 2008; Kim et al., 2006). The amount of manganese needed to neutralize iron is not well established. A common "rule of thumb"

Alloying with Mn and Cr, caution has to be taken in order to avoid the formation of hard complex intermetallic multi-component sludge, Al15(FeMnCr)3Si2 - phase (Fig. 5). These intermetallic compounds are hard and can adversely affect the overall properties of the casting. The formation of sludge phases is a temperature dependent process in a combination with the concentrations of iron, manganese and chromium independent of the silicon content. If Mg is also present with Si, an alternative called pi-or π-phase can form, Al5Si6Mg8Fe2. Al5Si6Mg8Fe2 has a script-like morphology. The Fe-rich particles can be twice as large as the Si particles, and the cooling rate has a direct impact on the kinetics, quantities

etch. Dix-Keller

deep etch., SEM (BSE detector) Mn-mapping

Cu is in Al-Si-Cu cast alloys present primarily as phases: Al2Cu, Al-Al2Cu-Si or Al5Mg8Cu2Si6 (Rios et al., 2003; Tillova & Chalupova, 2009; Tillova et al.; 2010). The average size of the Cu-phase decreases upon Sr modification. The Al2Cu phase is often observed to precipitate both in a small blocky shape with microhardness 185 HV 0.01. Al-Al2Cu-Si phase is observed in very fine multi-phase eutectic-like deposits with microhardness 280 HV 0.01

appears to be ratio between iron and manganese concentration of 2:1.

and size of Fe-rich intermetallic present in the microstructure.

Fig. 5. Morphology of sludge phase Al15(FeMnCr)3Si2

lead to the formation of excessive shrinkage porosity defects in castings (Caceres et al., 2003). Platelets are effective pore nucleation sites. It was also shown that the Al5FeSi needles can act as nucleation sites for Cu-rich Al2Cu phases (Tillova et al., 2010).

deep etch., SEM Fe-mapping

Fig. 3. Morphology of Fe-phase Al5FeSi

Fig. 4. Morphology of Fe-phase Al15(FeMn)3Si2

lead to the formation of excessive shrinkage porosity defects in castings (Caceres et al., 2003). Platelets are effective pore nucleation sites. It was also shown that the Al5FeSi needles

deep etch., SEM Fe-mapping

etch. H2SO4

deep etch., SEM Fe-mapping

Fig. 3. Morphology of Fe-phase Al5FeSi

Fig. 4. Morphology of Fe-phase Al15(FeMn)3Si2

etch. H2SO4

can act as nucleation sites for Cu-rich Al2Cu phases (Tillova et al., 2010).

The deleterious effect of Al5FeSi can be reduced by increasing the cooling rate, superheating the molten metal, or by the addition of a suitable "neutralizer" like Mn, Co, Cr, Ni, V, Mo and Be. The most common addition has been manganese. Excess Mn may reduce Al5FeSi phase and promote formation Fe-rich phases Al15(FeMn)3Si2 in form "skeleton like" or in form "Chinese script" (Seifeddine et al., 2008; Taylor, 2004) (Fig. 4). This compact morphology "Chinese script" (or skeleton - like) does not initiate cracks in the cast material to the same extent as Al5FeSi does and phase Al15(FeMn)3Si2 is considered less harmful to the mechanical properties than β phase (Ma et al., 2008; Kim et al., 2006). The amount of manganese needed to neutralize iron is not well established. A common "rule of thumb" appears to be ratio between iron and manganese concentration of 2:1.

Alloying with Mn and Cr, caution has to be taken in order to avoid the formation of hard complex intermetallic multi-component sludge, Al15(FeMnCr)3Si2 - phase (Fig. 5). These intermetallic compounds are hard and can adversely affect the overall properties of the casting. The formation of sludge phases is a temperature dependent process in a combination with the concentrations of iron, manganese and chromium independent of the silicon content. If Mg is also present with Si, an alternative called pi-or π-phase can form, Al5Si6Mg8Fe2. Al5Si6Mg8Fe2 has a script-like morphology. The Fe-rich particles can be twice as large as the Si particles, and the cooling rate has a direct impact on the kinetics, quantities and size of Fe-rich intermetallic present in the microstructure.

Cu is in Al-Si-Cu cast alloys present primarily as phases: Al2Cu, Al-Al2Cu-Si or Al5Mg8Cu2Si6 (Rios et al., 2003; Tillova & Chalupova, 2009; Tillova et al.; 2010). The average size of the Cu-phase decreases upon Sr modification. The Al2Cu phase is often observed to precipitate both in a small blocky shape with microhardness 185 HV 0.01. Al-Al2Cu-Si phase is observed in very fine multi-phase eutectic-like deposits with microhardness 280 HV 0.01

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 419

second phase atoms on cooling in the solidus region (Abdulwahab, 2008; Michna et al., 2007;

• Solution heat - treatment - it is necessary to produce a solid solution. Production of a solid solution consists of soaking the aluminium alloy at a temperature sufficiently high

• Rapid quenching to retain the maximum concentration of hardening constituent (Al2Cu) in solid solution. By quenching it is necessary to avoid slow cooling. Slow cooling can may the precipitation of phases that may be detrimental to the mechanical properties. For these reasons solid solutions formed during solution heat-treatment are quenched rapidly without interruption to produce a supersaturated solution at room

• Combination of artificial and over-ageing to obtain the desired mechanical properties in the casting. Generally artificial aging imparts higher strength and hardness values to

The precipitation sequence for Al-Si-Cu alloy is based upon the formation of Al2Cu based precipitates. The sequence is described as: αss → GP zones → θ´ → θ (Al2Cu). The sequence begins upon aging when the supersaturated solid solution (αss) gives way first to small coherent precipitates called GP zones. These particles are invisible in the optical microscope but macroscopically, this change is observed as an increase in the hardness and tensile strength of the alloy. As the process proceeds, the GP zones start to dissolve, and θ´ begins to form, which results in a further increase in the hardness and tensile strength in the alloy. Continued aging causes the θ´ phase to coarsen and the θ (Al2Cu) precipitate to appear. The θ phase is completely incoherent with the matrix, has a relatively large size, and has a coarse distribution within the aluminium matrix. Macroscopically, this change is observed as an increase in the ductility and a decrease in the hardness and tensile strength of the alloy

and for such a time so as to attain an almost homogeneous solid solution;

aluminium alloys without sacrificing other mechanical properties.

(Abdulwahab, 2008; Michna et al., 2007; Panuskova et al., 2008).

Fig. 7. The schematic diagram of hardening process for Al-Si-Cu cast alloy

Although the morphology, the amount and the distribution of the precipitates during aging process significantly influence the mechanical properties, an appropriate solution treatment is a prerequisite for obtaining desirable aging effect. From this point of view, the solution

ASM Handbook, 1991). Hardening heat treatment involves (Fig. 7):

temperature;

(Tillova & Chalupova, 2009). In recycled AlSi9Cu3 alloy was analysed two Cu-phases: Al2Cu and Al-Al2Cu-Si (Fig. 6).

deep etch., SEM Cu-mapping

Fig. 6. Morphology of Cu-phase - Al-Al2Cu-Si

The microhardness of all observed intermetallic phases was measured in HtW Dresden and the microhardness values are indicated in table 3. It is evident that the eutectic silicon, the Fe-rich phase Al5FeSi and the multicomponent intermetallic Al15(FeMn)3Si2 are the hardest.


Table 3. Microhardness and chemical composition of intermetallic phases

Influence of intermetallic phases to mechanical and fatigue properties of recycled Al-Si cast alloys depends on size, volume and morphology this Fe- and Cu-rich phases.

#### **3.2 Effect of solution treatment on the mechanical properties**

Al-Si-Cu cast alloys are usually heat-treated in order to obtain an optimum combination of strength and ductility. Important attribute of a precipitation hardening alloy system is a temperature and time dependent equilibrium solid-solubility characterized by decreasing solubility with decreasing temperature and then followed by solid-state precipitation of

(Tillova & Chalupova, 2009). In recycled AlSi9Cu3 alloy was analysed two Cu-phases:

etch. Dix-Keller

Al Mg Si Fe Cu Mn

deep etch., SEM Cu-mapping

Al15(MnFe)3Si2 483 61 - 10.3 13.4 2.6 13.6 Al5FeSi 1 475 67.7 - 16.5 15.8 - - Al2Cu 185 53.5 - - - 42.2 - Al-Al2Cu-Si 280 53 4.5 14.8 - 18.5 - Si 1084 - - 99.5 - - -

The microhardness of all observed intermetallic phases was measured in HtW Dresden and the microhardness values are indicated in table 3. It is evident that the eutectic silicon, the Fe-rich phase Al5FeSi and the multicomponent intermetallic Al15(FeMn)3Si2 are the hardest.

Intermetallic phases HV 0.01 Chemical composition, wt. %

Influence of intermetallic phases to mechanical and fatigue properties of recycled Al-Si cast

Al-Si-Cu cast alloys are usually heat-treated in order to obtain an optimum combination of strength and ductility. Important attribute of a precipitation hardening alloy system is a temperature and time dependent equilibrium solid-solubility characterized by decreasing solubility with decreasing temperature and then followed by solid-state precipitation of

Table 3. Microhardness and chemical composition of intermetallic phases

**3.2 Effect of solution treatment on the mechanical properties** 

alloys depends on size, volume and morphology this Fe- and Cu-rich phases.

Al2Cu and Al-Al2Cu-Si (Fig. 6).

Fig. 6. Morphology of Cu-phase - Al-Al2Cu-Si

second phase atoms on cooling in the solidus region (Abdulwahab, 2008; Michna et al., 2007; ASM Handbook, 1991). Hardening heat treatment involves (Fig. 7):


The precipitation sequence for Al-Si-Cu alloy is based upon the formation of Al2Cu based precipitates. The sequence is described as: αss → GP zones → θ´ → θ (Al2Cu). The sequence begins upon aging when the supersaturated solid solution (αss) gives way first to small coherent precipitates called GP zones. These particles are invisible in the optical microscope but macroscopically, this change is observed as an increase in the hardness and tensile strength of the alloy. As the process proceeds, the GP zones start to dissolve, and θ´ begins to form, which results in a further increase in the hardness and tensile strength in the alloy. Continued aging causes the θ´ phase to coarsen and the θ (Al2Cu) precipitate to appear. The θ phase is completely incoherent with the matrix, has a relatively large size, and has a coarse distribution within the aluminium matrix. Macroscopically, this change is observed as an increase in the ductility and a decrease in the hardness and tensile strength of the alloy (Abdulwahab, 2008; Michna et al., 2007; Panuskova et al., 2008).

Fig. 7. The schematic diagram of hardening process for Al-Si-Cu cast alloy

Although the morphology, the amount and the distribution of the precipitates during aging process significantly influence the mechanical properties, an appropriate solution treatment is a prerequisite for obtaining desirable aging effect. From this point of view, the solution

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 421

inter particle spacing and dissolution of the Al2Cu phase. After prolonged solution treatment time up to 16 h at 525 °C, it is clearly that the HB values strongly decrease

probably due to melting of the Al-Al2Cu-Si phase.

Fig. 9. Influence of solution treatment conditions on Brinell hardness

a) untreated state, deep-etch. HCl, SEM etch. Dix-Keller

b) 505 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

heat treatment is critical in determining the final microstructure and mechanical properties of the alloys. Thus, it is very important to investigate the effects of solution heat treatment on the alloys, before moving on to aging issues.

Solution treatment performs three roles (Li, 1996; Lasa & Rodriguez-Ibabe, 2004; Paray & Gruzleski, 1994; Moustafa et al., 2003; Sjölander & Seifeddine, 2010):


For experimental work heat treatment consisted of solution treatment for different temperatures: 505 °C, 515 °C and 525 °C; rapid water quenching (40 °C) and natural ageing (24 hours at room temperature) was used.

Influence of solution treatment on mechanical properties (strength tensile - Rm and Brinell hardness - HBS) is shown in Fig. 8 and Fig. 9.

After solution treatment, tensile strength, ductility and hardness are remarkably improved, compared to the corresponding as-cast condition. Fig. 8 shows the results of tensile strength measurements. The as cast samples have a strength value approximately 204 MPa. After 2 hours the solution treatment, independently of temperature of solution treatment, strength value immediately increases. By increasing the solution holding time from 2 to 4 hours, the tensile strength increased to 273 MPa for 515 °C. With further increase in solution temperature more than 515 °C and solution treatment time more than 4 hours, tensile strength decreases during the whole period as a result of gradual coarsening of eutectic Si, increase of inter particle spacing and dissolution of the Al2Cu phase (at 525 °C).

Fig. 8. Influence of solution treatment conditions on tensile strength

Fig. 9 shows the evolution of Brinell hardness value. Results of hardness are comparable with results of tensile strength. The as cast samples have a hardness value approximately 98 HB. After 2 hours the solution treatment, independently from temperature of solution treatment, hardness value immediately increases. The maximum was observed after 4 hours - approximately 124 HBS for 515 °C. However, after 8 hours solution treatment, the HB values are continuously decreasing as results of the coarsening of eutectic silicon, increase of

heat treatment is critical in determining the final microstructure and mechanical properties of the alloys. Thus, it is very important to investigate the effects of solution heat treatment

Solution treatment performs three roles (Li, 1996; Lasa & Rodriguez-Ibabe, 2004; Paray &

• changes the morphology of eutectic Si phase by fragmentation, spheroidization and

For experimental work heat treatment consisted of solution treatment for different temperatures: 505 °C, 515 °C and 525 °C; rapid water quenching (40 °C) and natural ageing

Influence of solution treatment on mechanical properties (strength tensile - Rm and Brinell

After solution treatment, tensile strength, ductility and hardness are remarkably improved, compared to the corresponding as-cast condition. Fig. 8 shows the results of tensile strength measurements. The as cast samples have a strength value approximately 204 MPa. After 2 hours the solution treatment, independently of temperature of solution treatment, strength value immediately increases. By increasing the solution holding time from 2 to 4 hours, the tensile strength increased to 273 MPa for 515 °C. With further increase in solution temperature more than 515 °C and solution treatment time more than 4 hours, tensile strength decreases during the whole period as a result of gradual coarsening of eutectic Si,

increase of inter particle spacing and dissolution of the Al2Cu phase (at 525 °C).

Fig. 8. Influence of solution treatment conditions on tensile strength

Fig. 9 shows the evolution of Brinell hardness value. Results of hardness are comparable with results of tensile strength. The as cast samples have a hardness value approximately 98 HB. After 2 hours the solution treatment, independently from temperature of solution treatment, hardness value immediately increases. The maximum was observed after 4 hours - approximately 124 HBS for 515 °C. However, after 8 hours solution treatment, the HB values are continuously decreasing as results of the coarsening of eutectic silicon, increase of

coarsening, thereby improving mechanical properties, particularly ductility.

on the alloys, before moving on to aging issues.

• homogenization of as-cast structure;

(24 hours at room temperature) was used.

hardness - HBS) is shown in Fig. 8 and Fig. 9.

Gruzleski, 1994; Moustafa et al., 2003; Sjölander & Seifeddine, 2010):

• dissolution of certain intermetallic phases such as Al2Cu;

inter particle spacing and dissolution of the Al2Cu phase. After prolonged solution treatment time up to 16 h at 525 °C, it is clearly that the HB values strongly decrease probably due to melting of the Al-Al2Cu-Si phase.

Fig. 9. Influence of solution treatment conditions on Brinell hardness

a) untreated state, deep-etch. HCl, SEM etch. Dix-Keller

b) 505 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 423

treatment on morphology of eutectic Si, for holding time 4 hour, is demonstrated in Figures 10b, 10c and 10d. After solution treatment at the temperature of 505 °C were noted that the platelets were fragmentized into smaller platelets with spherical edges (Fig. 10b) (on scratch

The spheroidisation process dominated at 515 °C. Si platelets fragment into smaller segments and these smaller Si particles were spheroidised to rounded shape; see Fig. 10c. By solution treatment 525 °C the spheroidised particles gradually grew larger (coarsening)

Quantitative metallography (Skocovsky & Vasko, 2007; Vasko & Belan, 2007; Belan, 2008; Vasko, 2008; Martinkovic, 2010) was carried out on an Image Analyzer NIS-Elements to quantify eutectic Si (average area of eutectic Si particle) by magnification 500 x. Figure 11 shows the average area of eutectic Si particles obtained in solution heat treated samples.

pattern round needles). The temperature 505 °C is for Si-spheroidisation low.

This graphic relation is in line with work Paray & Gruzleski, 1994.

Fig. 11. Influence of solution treatment on average area of eutectic Si particles

**3.4 Effect of solution treatment on the morphology of Fe-rich phases** 

influential than the quantity of those iron compounds.

which indicated that they undergo fragmentation and break into smaller segments.

Average area of eutectic Si particles decreases with increasing solution temperature and during the whole solution period. During the two hours, the area of Si-particles decreases

Minimum value of average eutectic Si particles was observed by temperature 515 °C (approximately 89 µm2). It's probably context with spheroidisation of eutectic silicon on this temperature. By solution treatment 525°C the spheroidised Si-particles in comparison with temperature 515 °C coarsen. The value of average eutectic Si particles at this temperature was observed from approximately 100 µm2 (2 hour) till 187 µm2 (32 hour). Prolonged solution treatment at 515°C and 525°C leads to a significant coarsening of the spheroidised Si particles.

The influence of iron on mechanical properties of aluminium alloys depends on the type, morphology and quantity of iron in the melt. Nevertheless, the shape of iron phases is more

(Figures 10d).

c) 515 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

d) 525 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

Fig. 10. Effect of solution treatment on morphology of eutectic Si

Obtained results (Fig. 8 and Fig. 9) suggests that to enhance the tensile strength or hardness of this recycled cast alloy by increasing of solution temperature more than 515 °C and by extending the solution time more than 4 hours does not seem suitable.

## **3.3 Effect of solution treatment on the morphology of eutectic silicon**

The mechanical properties of cast component are determined largely by the shape and distribution of Si particles in the matrix. Optimum tensile, impact and fatigue properties are obtained with small, spherical and evenly distributed particles.

It is hypothesized (Paray & Gruzleski, 1994; Li, 1996; Tillova & Chalupova, 2009; Moustafa et al, 2010) that the spheroidisation process of the eutectic silicon throughout heat treatment takes place in two stages: fragmentation or dissolution of the eutectic Si branches and the spheroidisation of the separated branches. Experimental material was not modified or grain refined and so eutectic Si particles without heat treatment (untreated – as cast state) are in a form of platelets (Fig. 10a), which on scratch pattern are in a form of needles.

The solution temperature is the most important parameter that influences the kinetics of Si morphology transformation during the course of solution treatment. The effect of solution

c) 515 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

d) 525 °C, 4 hours, deep-etch. HCl, SEM etch. Dix-Keller

Obtained results (Fig. 8 and Fig. 9) suggests that to enhance the tensile strength or hardness of this recycled cast alloy by increasing of solution temperature more than 515 °C and by

The mechanical properties of cast component are determined largely by the shape and distribution of Si particles in the matrix. Optimum tensile, impact and fatigue properties are

It is hypothesized (Paray & Gruzleski, 1994; Li, 1996; Tillova & Chalupova, 2009; Moustafa et al, 2010) that the spheroidisation process of the eutectic silicon throughout heat treatment takes place in two stages: fragmentation or dissolution of the eutectic Si branches and the spheroidisation of the separated branches. Experimental material was not modified or grain refined and so eutectic Si particles without heat treatment (untreated – as cast state) are in a

The solution temperature is the most important parameter that influences the kinetics of Si morphology transformation during the course of solution treatment. The effect of solution

Fig. 10. Effect of solution treatment on morphology of eutectic Si

obtained with small, spherical and evenly distributed particles.

extending the solution time more than 4 hours does not seem suitable.

**3.3 Effect of solution treatment on the morphology of eutectic silicon** 

form of platelets (Fig. 10a), which on scratch pattern are in a form of needles.

treatment on morphology of eutectic Si, for holding time 4 hour, is demonstrated in Figures 10b, 10c and 10d. After solution treatment at the temperature of 505 °C were noted that the platelets were fragmentized into smaller platelets with spherical edges (Fig. 10b) (on scratch pattern round needles). The temperature 505 °C is for Si-spheroidisation low.

The spheroidisation process dominated at 515 °C. Si platelets fragment into smaller segments and these smaller Si particles were spheroidised to rounded shape; see Fig. 10c. By solution treatment 525 °C the spheroidised particles gradually grew larger (coarsening) (Figures 10d).

Quantitative metallography (Skocovsky & Vasko, 2007; Vasko & Belan, 2007; Belan, 2008; Vasko, 2008; Martinkovic, 2010) was carried out on an Image Analyzer NIS-Elements to quantify eutectic Si (average area of eutectic Si particle) by magnification 500 x. Figure 11 shows the average area of eutectic Si particles obtained in solution heat treated samples. This graphic relation is in line with work Paray & Gruzleski, 1994.

Fig. 11. Influence of solution treatment on average area of eutectic Si particles

Average area of eutectic Si particles decreases with increasing solution temperature and during the whole solution period. During the two hours, the area of Si-particles decreases which indicated that they undergo fragmentation and break into smaller segments.

Minimum value of average eutectic Si particles was observed by temperature 515 °C (approximately 89 µm2). It's probably context with spheroidisation of eutectic silicon on this temperature. By solution treatment 525°C the spheroidised Si-particles in comparison with temperature 515 °C coarsen. The value of average eutectic Si particles at this temperature was observed from approximately 100 µm2 (2 hour) till 187 µm2 (32 hour). Prolonged solution treatment at 515°C and 525°C leads to a significant coarsening of the spheroidised Si particles.

## **3.4 Effect of solution treatment on the morphology of Fe-rich phases**

The influence of iron on mechanical properties of aluminium alloys depends on the type, morphology and quantity of iron in the melt. Nevertheless, the shape of iron phases is more influential than the quantity of those iron compounds.

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 425

d) 525 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

Fig. 13. Influence of solution treatment on surface fraction of Fe-rich phases

surface fraction rather than changes its morphology (Fig. 12 and Fig. 13).

**3.5 Effect of solution treatment o the morphology of Cu-rich phases** 

oval troops (Figures 14a and 15a).

Quantitative metallography was carried out on an Image Analyzer NIS-Elements to quantify Fe-phases changes, during solution treatment. It was established that the temperature increase of solution treatment was attended not only by fragmentation of Al15(FeMn)3Si2 phase, but also by decrease of surface fraction of all Fe-rich phases in AlSi9Cu3 alloy (Fig. 13). For the non-heat treated state the surface fraction of Fe-rich phase was c. 4.8 %, for temperature 515 °C c. 1.6 % and for 525 °C only c. 1.25 %. Solution treatment reduces its

The Cu-rich phase solidifies as fine ternary eutectic (Al-Al2Cu-Si) - Fig. 6. Effect of solution treatment on morphology of Al-Al2Cu-Si is demonstrated on Fig. 14. The changes of morphology of Al-Al2Cu-Si observed after heat treatment are documented for holding time 4 hours. Al-Al2Cu-Si phase without heat treatment (untreated state) occurs in form compact

Fig. 12. Effect of solution treatment on morphology of Fe-rich phases

The evolution of the Fe-rich phases during solution treatment is described for holding time 4 hours in Fig. 12. Al5FeSi phase is dissolved into very small needles (difficult to observe). The Al15(FeMn)3Si2 phase was fragmented to smaller skeleton particles. In the untreated state Al15(FeMn)3Si2 phase has a compact skeleton-like form (Fig. 12a). Solution treatment of this skeleton-like phase by 505°C tends only to fragmentation (Fig. 12b) and by 515°C or 525°C to fragmentation, segmentation and dissolution (Fig. 12c, Fig. 12d).

a) untreated state, deep-etch. HCl, SEM etch. H2SO4

b) 505 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

c) 515 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

The evolution of the Fe-rich phases during solution treatment is described for holding time 4 hours in Fig. 12. Al5FeSi phase is dissolved into very small needles (difficult to observe). The Al15(FeMn)3Si2 phase was fragmented to smaller skeleton particles. In the untreated state Al15(FeMn)3Si2 phase has a compact skeleton-like form (Fig. 12a). Solution treatment of this skeleton-like phase by 505°C tends only to fragmentation (Fig. 12b) and by 515°C or

525°C to fragmentation, segmentation and dissolution (Fig. 12c, Fig. 12d).

a) untreated state, deep-etch. HCl, SEM etch. H2SO4

b) 505 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

c) 515 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

d) 525 °C, 4 hours, deep-etch. HCl, SEM etch. H2SO4

Fig. 12. Effect of solution treatment on morphology of Fe-rich phases

Fig. 13. Influence of solution treatment on surface fraction of Fe-rich phases

Quantitative metallography was carried out on an Image Analyzer NIS-Elements to quantify Fe-phases changes, during solution treatment. It was established that the temperature increase of solution treatment was attended not only by fragmentation of Al15(FeMn)3Si2 phase, but also by decrease of surface fraction of all Fe-rich phases in AlSi9Cu3 alloy (Fig. 13). For the non-heat treated state the surface fraction of Fe-rich phase was c. 4.8 %, for temperature 515 °C c. 1.6 % and for 525 °C only c. 1.25 %. Solution treatment reduces its surface fraction rather than changes its morphology (Fig. 12 and Fig. 13).

#### **3.5 Effect of solution treatment o the morphology of Cu-rich phases**

The Cu-rich phase solidifies as fine ternary eutectic (Al-Al2Cu-Si) - Fig. 6. Effect of solution treatment on morphology of Al-Al2Cu-Si is demonstrated on Fig. 14. The changes of morphology of Al-Al2Cu-Si observed after heat treatment are documented for holding time 4 hours. Al-Al2Cu-Si phase without heat treatment (untreated state) occurs in form compact oval troops (Figures 14a and 15a).

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 427

because of the very fast superificial oxidation of Al-alloys. In some cases, the specimen should be cleaned mechanically by rinsing in ultrasonic cleaner, chemical reagents, or

a) untreated state – compact morphology b) 515 °C, 4 hours - fine round particles

Fractographs of the specimens in untreated state (as cast state) after impact test are documented in Fig. 16. As the experimental material was not modified and eutectic Si particles are in a form of platelets (Fig. 2), fracture surfaces are mainly composed of ductile

Fracture of the α-matrix is transcrystalline ductile with dimples morphology and with plastically transformed walls (Fig. 16a, b). The shape of walls depends on the orientation of Si particles on fracture surface. The brittle eutectic Si and Fe-rich phases (Figures 3-5) are fractured by the transcrystalline cleavage mechanism (Fig. 16c, d, e). Cu-phase (compact ternary eutectic Al-Al2Cu-Si – Fig. 6) is fractured by transcrystalline ductile fracture with the very fine and flat dimples morphology (Fig. 16f). In some cases, to improve the contrast of the matrix/phase interface, detection of backscattered electrons (BSE) in a SEM is a very useful method (Fig. 16c). This method provides another alternative when phase attribution

Fractographs of the specimens after solution treatment are documented in Fig. 17. By temperature 505 °C of solution treatment were noted that the Si-platelets were fragmentized into smaller platelets with spherical edges (Fig. 10b). Spheroidisation process of eutectic silicon was not observed. The morphology from transcrystalline brittle fracture (cleavage) is mainly visible, but some degree of plastic deformation in the aluminium solid solution (αmatrix) also may be noticed in the form of shallow dimples and plastically transformed

After solution treatment at the 515 °C eutectic silicon is completely spheroidised (Fig. 10c). Number of brittle Fe-phases decreases (Fig. 12c). Fracture is transcrystalline ductile with fine dimples morphology (Fig. 17c, d). The size of the dimples shows the size of eutectic silicon.

Fig. 15. Morphology of Cu-rich phases after deep etching, etch. HCl, SEM

fracture with cleavage fracture regions.

by morphology and/or colour, is not clearly.

Local we can observe little cleavage facets of Fe-rich phases.

walls (Fig. 17a, b).

electrolytes (Michna et al., 2007; Tillova & Chalupova, 2009; Warmuzek, 2004b).

After solution treatment by temperature 505 °C these phase disintegrated into fine smaller segments and the amount of Al-Al2Cu-Si phase during heat treatment decreases. This phase is gradually dissolved into the surrounding Al-matrix with an increase in solution treatment time (Fig. 14b). By solution treatment by 515 °C is this phase observed in the form coarsened globular particles and these occurs along the black needles, probably Fe-rich Al5FeSi phase (Figures 14c and 15b). By solution treatment 525 °C is this phase documented in the form molten particles with homogenous shape (Fig. 14d).

a) untreated state b) 505 °C, 4 hours

c) 515 °C, 4 hours d) 525 °C, 4 hours,

Fig. 14. Effect of solution treatment on morphology of Cu-rich phases, etch. HNO3

Compact Al-Al2Cu-Si phase disintegrates to fine separates Al2Cu particles. The amount of these phases was not obvious visible on optical microscope. On SEM microscope we observed these phases in form very small particles for every temperatures of natural aging. By observation we had to use a big extension, because we did not see these elements.

Small precipitates (Al2Cu) incipient by hardening were invisible in the optical microscope and electron microscope so it is necessary observation using TEM microscopy.

#### **3.6 SEM observation of the fracture surface**

Topography of fracture surfaces is commonly examined by SEM. The large depth of field is a very important advantage for fractographic investigations. Fracture surfaces of Al-Si-Cu cast alloys can be observed by means of SEM without almost any special preparation; nevertheless, if it is possible, the specimens should be examined immediately after failure because of the very fast superificial oxidation of Al-alloys. In some cases, the specimen should be cleaned mechanically by rinsing in ultrasonic cleaner, chemical reagents, or electrolytes (Michna et al., 2007; Tillova & Chalupova, 2009; Warmuzek, 2004b).

426 Scanning Electron Microscopy

After solution treatment by temperature 505 °C these phase disintegrated into fine smaller segments and the amount of Al-Al2Cu-Si phase during heat treatment decreases. This phase is gradually dissolved into the surrounding Al-matrix with an increase in solution treatment time (Fig. 14b). By solution treatment by 515 °C is this phase observed in the form coarsened globular particles and these occurs along the black needles, probably Fe-rich Al5FeSi phase (Figures 14c and 15b). By solution treatment 525 °C is this phase documented in the form

a) untreated state b) 505 °C, 4 hours

c) 515 °C, 4 hours d) 525 °C, 4 hours,

Compact Al-Al2Cu-Si phase disintegrates to fine separates Al2Cu particles. The amount of these phases was not obvious visible on optical microscope. On SEM microscope we observed these phases in form very small particles for every temperatures of natural aging.

Small precipitates (Al2Cu) incipient by hardening were invisible in the optical microscope

Topography of fracture surfaces is commonly examined by SEM. The large depth of field is a very important advantage for fractographic investigations. Fracture surfaces of Al-Si-Cu cast alloys can be observed by means of SEM without almost any special preparation; nevertheless, if it is possible, the specimens should be examined immediately after failure

Fig. 14. Effect of solution treatment on morphology of Cu-rich phases, etch. HNO3

By observation we had to use a big extension, because we did not see these elements.

and electron microscope so it is necessary observation using TEM microscopy.

**3.6 SEM observation of the fracture surface** 

molten particles with homogenous shape (Fig. 14d).

a) untreated state – compact morphology b) 515 °C, 4 hours - fine round particles

Fig. 15. Morphology of Cu-rich phases after deep etching, etch. HCl, SEM

Fractographs of the specimens in untreated state (as cast state) after impact test are documented in Fig. 16. As the experimental material was not modified and eutectic Si particles are in a form of platelets (Fig. 2), fracture surfaces are mainly composed of ductile fracture with cleavage fracture regions.

Fracture of the α-matrix is transcrystalline ductile with dimples morphology and with plastically transformed walls (Fig. 16a, b). The shape of walls depends on the orientation of Si particles on fracture surface. The brittle eutectic Si and Fe-rich phases (Figures 3-5) are fractured by the transcrystalline cleavage mechanism (Fig. 16c, d, e). Cu-phase (compact ternary eutectic Al-Al2Cu-Si – Fig. 6) is fractured by transcrystalline ductile fracture with the very fine and flat dimples morphology (Fig. 16f). In some cases, to improve the contrast of the matrix/phase interface, detection of backscattered electrons (BSE) in a SEM is a very useful method (Fig. 16c). This method provides another alternative when phase attribution by morphology and/or colour, is not clearly.

Fractographs of the specimens after solution treatment are documented in Fig. 17. By temperature 505 °C of solution treatment were noted that the Si-platelets were fragmentized into smaller platelets with spherical edges (Fig. 10b). Spheroidisation process of eutectic silicon was not observed. The morphology from transcrystalline brittle fracture (cleavage) is mainly visible, but some degree of plastic deformation in the aluminium solid solution (αmatrix) also may be noticed in the form of shallow dimples and plastically transformed walls (Fig. 17a, b).

After solution treatment at the 515 °C eutectic silicon is completely spheroidised (Fig. 10c). Number of brittle Fe-phases decreases (Fig. 12c). Fracture is transcrystalline ductile with fine dimples morphology (Fig. 17c, d). The size of the dimples shows the size of eutectic silicon. Local we can observe little cleavage facets of Fe-rich phases.

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 429

a) b)

**Si** 

c) d)

**Cu-phase**

e) f)

Fig. 17. Fractographs of the impact test specimen – after solution treatment, SEM

Fig. 16. Fractographs of the impact test specimen – as cast state, SEM

a) b)

**Si Si**

**Fe-phase**

**Cu-phase**

c) d)

e) f)

**Cu-phase**

Fig. 16. Fractographs of the impact test specimen – as cast state, SEM

**Fe-phase**

Fig. 17. Fractographs of the impact test specimen – after solution treatment, SEM

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 431

The fatigue AlSi9Cu3 tests (as-cast, solution heat treated at two temperatures 515 and 525 °C for times 4 hours, then quenched in warm water at 40 °C and natural aged at room temperature for 24 hours) were performed on rotating bending testing machine ROTOFLEX operating at 30 Hz., load ratio R = -1 and at room temperature 20 ± 5 °C on the air. Cylindrical fatigue specimens were produced by lathe-turning and thereafter were heat treated. Geometry of fatigue specimens is given in Fig. 18. The fatigue fracture surfaces of the fatigue - tested samples under different solution heat treatment condition were examined using a scanning electron microscope (SEM) TESCAN VEGA LMU with EDX

Fig. 19. Effect of solution treatment on fatigue behaviour of AlSi9Cu3 cast alloy

The untreated specimens were tested first to provide a baseline on fatigue life. In this study, the number of cycle, 107, is taken as the infinite fatigue life. Thus, the highest applied stress under which a specimen can withstand 107 cycles is defined as the fatigue strength of the alloy. The relationship between the maximum stress level (S), and the fatigue life in the form of the number of fatigue cycles (N), (S-N curves) is given in Fig. 19. Comparison on the fatigue properties of specimens with and without heat treatment was made. In heat untreated state has fatigue strength (σ) at 107 cycles the lowest value, only σ = 49 MPa. It is evident, that after solution treatment increased fatigue strength at 107 cycles. By the conditions of solution treatment 515 °C/4 hours the fatigue strength at 107 cycles increases up to value σ = 70 MPa. The solution treatment by 525 °C/4 hours caused the increasing of fatigue strength at 107 cycles to value σ = 76 MPa. The growths in fatigue strength at 107 cycles with respect to the temperature of solution treatment are 42 and 55 % respectively.

**1,E+02 1,E+03 1,E+04 1,E+05 1,E+06 1,E+07 1,E+08 Number of cycles to failure**

**515°C 525°C untreated**

Fatigue fracture surfaces were examined in the SEM in order to find the features responsible for crack initiation. Typical fractographic surfaces are shown in Fig. 20, Fig. 21, Fig. 22 and Fig. 23. The global view of the fatigue fracture surface for untreated and heat treated specimens is very similar. The process of fatigue consists of three stages – crack initiation

analyser BRUKER QUANTAX after fatigue test.

**5**

**30**

**55**

**80**

**105**

**Stress amplitude [MPa]**

**130**

**155**

**180**

**205**

Fractograph of the specimen after solution treatment at the 525 °C is documented in Fig. 17e. Eutectic silicon is completely spheroidised too (Fig. 10d), but spheroidised Si-particles gradually grew larger. The fracture mechanism was identified as transcrystalline ductile with dimples morphology accompanied by plastically transformed walls (Fig. 17e). The size of the dimples shows the larger size of eutectic silicon as compared with fractograph Fig. 17d. Figure 17f is an example of a transcrystalline ductile fracture of Cu-rich phase after solution treatment at the 515 °C.

## **3.7 Influence of solution annealing on fatigue properties**

To successfully utilize recycled Al-Si-Cu alloys in critical components, it is necessary to thoroughly understand its fatigue property too. Numerous studies have shown that fatigue property of conventional casting aluminium alloys are very sensitive to casting defects (porosity, microshrinkages and voids) and many studies have shown that, whenever a large pore is present at or near the specimen's surface, it will be the dominant cause of fatigue crack initiation (Bokuvka et al., 2002; Caceres et al., 2003; Moreira & Fuoco, 2006; Novy et al.; 2007). The occurrence of cast defects, together with the morphology of microstructural features, is strongly connected with method of casting too. By sand mould is the concentration of hydrogen in melt, as a result of damp cast surroundings, very high. The solubility of hydrogen during solidification of Al-Si cast alloys rapidly decreases and by slow cooling rates (sand casting) keeps in melt in form of pores and microshrinkages (Michna et al., 2007).

Fe is a common impurity in aluminium alloys that leads to the formation of complex Fe-rich intermetallic phases, and how these phases can adversely affect mechanical properties, especially ductility, and also lead to the formation of excessive shrinkage porosity defects in castings (Taylor, 2004; Tillova & Chalupova, 2009).

It is clear, that the morphology of Fe-rich intermetallic phases influences harmfully on fatigue properties too (Palcek et. al., 2003). Much harmful effect proves the cast defects as porosity and microshrinkages, because these defects have larger size as intermetallic phases. A comprehensive understanding of the influence of these microstructural features on the fatigue damage evolution is needed.

In the end heat treatment is considered as an important factor that affects the fatigue behaviour of casting Al-Si-Cu alloys too (Tillova & Chalupova, 2010).

Fig. 18. Fatigue specimen geometry (all dimension in mm)

Fractograph of the specimen after solution treatment at the 525 °C is documented in Fig. 17e. Eutectic silicon is completely spheroidised too (Fig. 10d), but spheroidised Si-particles gradually grew larger. The fracture mechanism was identified as transcrystalline ductile with dimples morphology accompanied by plastically transformed walls (Fig. 17e). The size of the dimples shows the larger size of eutectic silicon as compared with fractograph Fig. 17d. Figure 17f is an example of a transcrystalline ductile fracture of Cu-rich phase after

To successfully utilize recycled Al-Si-Cu alloys in critical components, it is necessary to thoroughly understand its fatigue property too. Numerous studies have shown that fatigue property of conventional casting aluminium alloys are very sensitive to casting defects (porosity, microshrinkages and voids) and many studies have shown that, whenever a large pore is present at or near the specimen's surface, it will be the dominant cause of fatigue crack initiation (Bokuvka et al., 2002; Caceres et al., 2003; Moreira & Fuoco, 2006; Novy et al.; 2007). The occurrence of cast defects, together with the morphology of microstructural features, is strongly connected with method of casting too. By sand mould is the concentration of hydrogen in melt, as a result of damp cast surroundings, very high. The solubility of hydrogen during solidification of Al-Si cast alloys rapidly decreases and by slow cooling rates (sand casting) keeps in melt in form of pores and microshrinkages

Fe is a common impurity in aluminium alloys that leads to the formation of complex Fe-rich intermetallic phases, and how these phases can adversely affect mechanical properties, especially ductility, and also lead to the formation of excessive shrinkage porosity defects in

It is clear, that the morphology of Fe-rich intermetallic phases influences harmfully on fatigue properties too (Palcek et. al., 2003). Much harmful effect proves the cast defects as porosity and microshrinkages, because these defects have larger size as intermetallic phases. A comprehensive understanding of the influence of these microstructural features on the

In the end heat treatment is considered as an important factor that affects the fatigue

behaviour of casting Al-Si-Cu alloys too (Tillova & Chalupova, 2010).

Fig. 18. Fatigue specimen geometry (all dimension in mm)

solution treatment at the 515 °C.

(Michna et al., 2007).

**3.7 Influence of solution annealing on fatigue properties** 

castings (Taylor, 2004; Tillova & Chalupova, 2009).

fatigue damage evolution is needed.

The fatigue AlSi9Cu3 tests (as-cast, solution heat treated at two temperatures 515 and 525 °C for times 4 hours, then quenched in warm water at 40 °C and natural aged at room temperature for 24 hours) were performed on rotating bending testing machine ROTOFLEX operating at 30 Hz., load ratio R = -1 and at room temperature 20 ± 5 °C on the air. Cylindrical fatigue specimens were produced by lathe-turning and thereafter were heat treated. Geometry of fatigue specimens is given in Fig. 18. The fatigue fracture surfaces of the fatigue - tested samples under different solution heat treatment condition were examined using a scanning electron microscope (SEM) TESCAN VEGA LMU with EDX analyser BRUKER QUANTAX after fatigue test.

Fig. 19. Effect of solution treatment on fatigue behaviour of AlSi9Cu3 cast alloy

The untreated specimens were tested first to provide a baseline on fatigue life. In this study, the number of cycle, 107, is taken as the infinite fatigue life. Thus, the highest applied stress under which a specimen can withstand 107 cycles is defined as the fatigue strength of the alloy. The relationship between the maximum stress level (S), and the fatigue life in the form of the number of fatigue cycles (N), (S-N curves) is given in Fig. 19. Comparison on the fatigue properties of specimens with and without heat treatment was made. In heat untreated state has fatigue strength (σ) at 107 cycles the lowest value, only σ = 49 MPa. It is evident, that after solution treatment increased fatigue strength at 107 cycles. By the conditions of solution treatment 515 °C/4 hours the fatigue strength at 107 cycles increases up to value σ = 70 MPa. The solution treatment by 525 °C/4 hours caused the increasing of fatigue strength at 107 cycles to value σ = 76 MPa. The growths in fatigue strength at 107 cycles with respect to the temperature of solution treatment are 42 and 55 % respectively.

Fatigue fracture surfaces were examined in the SEM in order to find the features responsible for crack initiation. Typical fractographic surfaces are shown in Fig. 20, Fig. 21, Fig. 22 and Fig. 23. The global view of the fatigue fracture surface for untreated and heat treated specimens is very similar. The process of fatigue consists of three stages – crack initiation

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 433

Important to the stress concentration and to fatigue crack nucleation is the presence of casting defects as microporosities, oxide inclusions and shrinkage porosities, since the size of these defects can by much larger than the size of the microstructure particles. It was confirmed, that if are in structure marked cast defects, then behaved preferentially as an

The cast defects were detected on the surface of test fatigue specimens. Details of the initiations site are shown in Fig. 21. For low stress amplitudes were observed one initiation place (Fig. 21a). For high stress amplitudes existed more initiation places (Fig. 21b). The occurrence of these cast defects (Fig. 22) causes the small solubility of hydrogen during

The main micrographic characteristics of the fatigue fracture near the initiating site are the tear ridges (Fig. 23a-c) in the direction of the crack propagation and the fatigue striation in a direction perpendicular to the crack propagation. The striations are barely seen (Figure 23d). Fig. 23b illustrates the same fatigue surface as Fig. 23a, near the initiating site, in BSE electron microscopy. The result of BSE observation presents the contrast improvement of

Final rupture region for untreated and heat treated specimens is documented in Fig. 24. Fracture path is from micrographic aspect thus mostly transgranular and the appearance of the fracture surface is more flat. The fracture of the α-dendritic network is always ductile but particularly depends on morphology of eutectic Si and quantity of brittle intermetallic

The fracture surface of the as-cast samples revealed, in general, a ductile rupture mode with

Fracture surface of heat treated samples consists almost exclusively of small dimples, with morphology and size that traced morphologhy of eutectic silicon (solution treatment resulted spheroidisation of eutectic silicon), such as those seen in Fig. 24b and Fig. 24c.

initiation's places of fatigue damage.

brittle Fe-rich intermetallic phases Al15(FeMn)3Si2.

brittle nature of unmodified eutectic silicon platelets (Fig. 24a).

phases (e.g. Al15(FeMn)3Si2 or Al5FeSi).

Fig. 22. Detail of cast defect

solidification of Al-Si alloys.

stage (I), progressive crack growth across the specimen (II) and final sudden static fracture of the remaining cross section of the specimen (III) (Bokuvka et al., 2002; Palcek et al., 2003; Novy et al., 2007; Tillova & Chalupova, 2009; Moreira & Fuoco, 2006).

a) σ = 88 MPa, Nf = 11 560 cycles b) σ = 54 MPa, Nf = 5.106 cycles Fig. 20. Complete fracture surfaces, SEM

Stage I and II is so-called fatigue region. The three stages are directly related to the macrographic aspects of the fatigue fractures (Fig. 20). Within the bounds of fatigue tests was established that, high stress amplitude caused small fatigue region (Fig. 20a) and large region of final static rupture. With the decreasing of stress amplitude increases the fatigue region of stable propagating of cracks (Fig. 20b) and the initiation places are more focused to one point simultaneously.

a) detail of one initiation' site on the surface b) detail of more initiation' sites on the

surface

Fig. 21. Fatigue crack nucleation - overview of a fracture surface

stage (I), progressive crack growth across the specimen (II) and final sudden static fracture of the remaining cross section of the specimen (III) (Bokuvka et al., 2002; Palcek et al., 2003;

a) σ = 88 MPa, Nf = 11 560 cycles b) σ = 54 MPa, Nf = 5.106 cycles

Stage I and II is so-called fatigue region. The three stages are directly related to the macrographic aspects of the fatigue fractures (Fig. 20). Within the bounds of fatigue tests was established that, high stress amplitude caused small fatigue region (Fig. 20a) and large region of final static rupture. With the decreasing of stress amplitude increases the fatigue region of stable propagating of cracks (Fig. 20b) and the initiation places are more focused to

a) detail of one initiation' site on the surface b) detail of more initiation' sites on the

Fig. 21. Fatigue crack nucleation - overview of a fracture surface

surface

Novy et al., 2007; Tillova & Chalupova, 2009; Moreira & Fuoco, 2006).

Fig. 20. Complete fracture surfaces, SEM

one point simultaneously.

Important to the stress concentration and to fatigue crack nucleation is the presence of casting defects as microporosities, oxide inclusions and shrinkage porosities, since the size of these defects can by much larger than the size of the microstructure particles. It was confirmed, that if are in structure marked cast defects, then behaved preferentially as an initiation's places of fatigue damage.

The cast defects were detected on the surface of test fatigue specimens. Details of the initiations site are shown in Fig. 21. For low stress amplitudes were observed one initiation place (Fig. 21a). For high stress amplitudes existed more initiation places (Fig. 21b). The occurrence of these cast defects (Fig. 22) causes the small solubility of hydrogen during solidification of Al-Si alloys.

The main micrographic characteristics of the fatigue fracture near the initiating site are the tear ridges (Fig. 23a-c) in the direction of the crack propagation and the fatigue striation in a direction perpendicular to the crack propagation. The striations are barely seen (Figure 23d). Fig. 23b illustrates the same fatigue surface as Fig. 23a, near the initiating site, in BSE electron microscopy. The result of BSE observation presents the contrast improvement of brittle Fe-rich intermetallic phases Al15(FeMn)3Si2.

Final rupture region for untreated and heat treated specimens is documented in Fig. 24. Fracture path is from micrographic aspect thus mostly transgranular and the appearance of the fracture surface is more flat. The fracture of the α-dendritic network is always ductile but particularly depends on morphology of eutectic Si and quantity of brittle intermetallic phases (e.g. Al15(FeMn)3Si2 or Al5FeSi).

The fracture surface of the as-cast samples revealed, in general, a ductile rupture mode with brittle nature of unmodified eutectic silicon platelets (Fig. 24a).

Fracture surface of heat treated samples consists almost exclusively of small dimples, with morphology and size that traced morphologhy of eutectic silicon (solution treatment resulted spheroidisation of eutectic silicon), such as those seen in Fig. 24b and Fig. 24c.

Fig. 22. Detail of cast defect

Evolution of Phases in a Recycled Al-Si Cast Alloy During Solution Treatment 435

a) untreated b) 515 °C c) 525 °C

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Fig. 24. Final rupture region - detail

**4. Acknowledgment** 

1364-0461

**5. References** 

a) fatigue fracture surface near the initiating site - fine tear ridges

b) fatigue fracture surface near the initiating site - BSE

d) detail of the typical aspect of fatigue - extremely fine striations

Fig. 23. Typical fatigue fracture surface

Fig. 24. Final rupture region - detail

## **4. Acknowledgment**

The authors acknowledge the financial support of the projects VEGA No1/0249/09; VEGA No1/0841/11 and European Union - the Project "*Systematization of advanced technologies and knowledge transfer between industry and universities (ITMS 26110230004)*".

### **5. References**

434 Scanning Electron Microscopy

b) fatigue fracture surface near the initiating site - BSE

d) detail of the typical aspect of fatigue - extremely fine striations

a) fatigue fracture surface near the initiating site - fine tear ridges

> c) detail of fatigue fracture surface - tear ridges

Fig. 23. Typical fatigue fracture surface


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**1. Introduction** 

**22** 

*Thailand* 

**Strength and Microstructure** 

The soil/ground improvement by cement is an economical and worldwide method for pavement and earth structure works. Stabilization begins by mixing the in-situ soil in a relatively dry state with cement and water specified for compaction. The soil, in the presence of moisture and a cementing agent becomes a modified soil, i.e, particles group together because of physical-chemical interactions among soil, cement and water. Because this occurs at the particle level, it is not possible to get a homogeneous mass with the desired strength. Compaction is needed to make soil particles slip over each other and move into a densely packed state. In this state, the soil particles can be welded by chemical (cementation) bonds and become an engineering material (Horpibulsuk et al., 2006). To reduce the cost of ground improvement, the replacement of the cement by waste materials such as fly ash and biomass ash is one of the best alternative ways. In many countries, the generation of these waste materials is general far in excess of their utilization. A feasibility study of utilizing these ashes (waste materials) to partially replace Type I Portland cement is thus interesting. The effects of some influential factors, i.e., water content, cement content, curing time, and compaction energy on the laboratory engineering characteristics of cement-stabilized soils have been extensively researched (Clough et al., 1981; Kamon & Bergado, 1992; Yin & Lai, 1998; Miura et al., 2001; Horpibulsuk & Miura, 2001; Horpibulsuk et al., 2003, 2004a, 2004b, 2005, 2006, 2011a). The field mixing effect such as installation rate, water/cement ratio and curing condition on the strength development of cemented soil was investigated by Nishida et al. (1996) and Horpibulsuk et al. (2004c, 2006 and 2011b). Based on the available compression and shear test results, many constitutive models were developed to describe the engineering behavior of cemented clay (Gens and Nova, 1993; Kasama et al., 2000; Horpibulsuk et al., 2010a; Suebsuk et al., 2010 and 2011). These investigations have mainly focused on the mechanical behavior that is mainly controlled by the microstructure. The structure is fabric that is the arrangement of the particles, clusters and pore spaces in the soil as well as cementation (Mitchell, 1993). It is thus vital to understand the changes in

engineering properties that result from the changes in the influential factors.

This chapter attempts to illustrate the microstructural changes in cement-stabilized clay to explain the different strength development according to the influential factors, i.e., cement content, clay water content, fly ash content and curing time. The unconfined compressive

**of Cement Stabilized Clay** 

Suksun Horpibulsuk

*Suranaree University of Technology,* 


## **Strength and Microstructure of Cement Stabilized Clay**

Suksun Horpibulsuk

*Suranaree University of Technology, Thailand* 

## **1. Introduction**

438 Scanning Electron Microscopy

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1320

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The soil/ground improvement by cement is an economical and worldwide method for pavement and earth structure works. Stabilization begins by mixing the in-situ soil in a relatively dry state with cement and water specified for compaction. The soil, in the presence of moisture and a cementing agent becomes a modified soil, i.e, particles group together because of physical-chemical interactions among soil, cement and water. Because this occurs at the particle level, it is not possible to get a homogeneous mass with the desired strength. Compaction is needed to make soil particles slip over each other and move into a densely packed state. In this state, the soil particles can be welded by chemical (cementation) bonds and become an engineering material (Horpibulsuk et al., 2006). To reduce the cost of ground improvement, the replacement of the cement by waste materials such as fly ash and biomass ash is one of the best alternative ways. In many countries, the generation of these waste materials is general far in excess of their utilization. A feasibility study of utilizing these ashes (waste materials) to partially replace Type I Portland cement is thus interesting.

The effects of some influential factors, i.e., water content, cement content, curing time, and compaction energy on the laboratory engineering characteristics of cement-stabilized soils have been extensively researched (Clough et al., 1981; Kamon & Bergado, 1992; Yin & Lai, 1998; Miura et al., 2001; Horpibulsuk & Miura, 2001; Horpibulsuk et al., 2003, 2004a, 2004b, 2005, 2006, 2011a). The field mixing effect such as installation rate, water/cement ratio and curing condition on the strength development of cemented soil was investigated by Nishida et al. (1996) and Horpibulsuk et al. (2004c, 2006 and 2011b). Based on the available compression and shear test results, many constitutive models were developed to describe the engineering behavior of cemented clay (Gens and Nova, 1993; Kasama et al., 2000; Horpibulsuk et al., 2010a; Suebsuk et al., 2010 and 2011). These investigations have mainly focused on the mechanical behavior that is mainly controlled by the microstructure. The structure is fabric that is the arrangement of the particles, clusters and pore spaces in the soil as well as cementation (Mitchell, 1993). It is thus vital to understand the changes in engineering properties that result from the changes in the influential factors.

This chapter attempts to illustrate the microstructural changes in cement-stabilized clay to explain the different strength development according to the influential factors, i.e., cement content, clay water content, fly ash content and curing time. The unconfined compressive

Strength and Microstructure of Cement Stabilized Clay 441

decreases the repulsion between successive diffused double layers and increases edge-toface contacts between successive clay sheets. Thus, clay particles flocculate into larger clusters, which increases in the plastic limit with an insignificant change in the liquid limit (*vide* Table 1). As such, the plasticity index of the mixture decreases due to the significant increase in the plastic limit. Because the *OWC* of low swelling clays is mainly controlled by the liquid limit (Horpibulsuk et al., 2008 and 2009), the *OWC*s of the unstabilized and the stabilized samples are almost the same (*vide* Table 1). Figure 2 shows the compaction curve of the fly ash (FA) blended cement stabilized clay for different replacement ratios (ratios of cement to fly ash, C:F) compared with that of the unstabilized clay. Two fly ashes are presented in the figure: original, OFA (*D*50 = 0.03 mm) and classified, CFA (*D*50 = 0.009 mm) fly ashes. It is noted that the compaction curve of the stabilized clay is insignificantly dependent upon replacement ratio and fly ash particles. Maximum dry unit weight of the stabilized clay is higher than that of the unstabilized clay whereas their optimum water

5 10 15 20 25 30 35 40

Water content (%)

Fig. 1. Plots of dry unit weight versus water content of the uncemented and the cemented samples compacted under standard and modified Proctor energies (Horpibulsuk et al.,

LL PL PI Std. Mod. Std. Mod.

0 74.1 27.5 46.6 22.4 17.2 14.6 17.4 3 74.1 45.0 29.1 22.2 17.5 16.2 18.5 5 72.5 45.0 27.5 21.8 17.3 16.2 18.7 10 71.0 44.8 26.2 22.0 17.4 16.4 18.8

OWCst

OWCmod

Cement (%) Atterberg's limits (%) *OWC* (%)

Silty clay (CH) LL = 74%, PL = 28%

= 17.2%

= 22.0%

0% cement

Standard Modified

γ

*dmax* (kN/m3)

3% cement

5% cement 10% cement

content is practically the same.

Dry unit weight (kN/m3

2010b)

)

Table 1. Basic properties of the cemented samples

strength was used as a practical indicator to investigate the strength development. The microstructural analyses were performed using a scanning electron microscope (SEM), mercury intrusion porosimetry (MIP), and thermal gravity (TG) tests. For SEM, the cement stabilized samples were broken from the center into small fragments. The SEM samples were frozen at -195°C by immersion in liquid nitrogen for 5 minutes and evacuated at a pressure of 0.5 Pa at -40°C for 5 days (Miura et al., 1999; and Yamadera, 1999). All samples were coated with gold before SEM (JEOL JSM-6400) analysis.

Measurement on pore size distribution of the samples was carried out using mercury intrusion porosimeter (MIP) with a pressure range from 0 to 288 MPa, capable of measuring pore size diameter down to 5.7 nm (0.0057 micron). The MIP samples were obtained by carefully breaking the stabilized samples with a chisel. The representative samples of 3-6 mm pieces weighing between 1.0-1.5 g were taken from the middle of the cemented samples. Hydration of the samples was stopped by freezing and drying, as prepared in the SEM examination. Mercury porosimetry is expressed by the Washburn equation (Washburn, 1921). A constant contact angle (θ) of 140° and a constant surface tension of mercury (γ) of 480 dynes/cm were used for pore size calculation as suggested by Eq.(1)

$$D = -\left(4\gamma\cos\theta\right)/P\tag{1}$$

where *D* is the pore diameter (micron) and *P* is the applied pressure (MPa).

Thermal gravity (TG) analysis is one of the widely accepted methods for determination of hydration products, which are crystalline Ca(OH)2, CSH, CAH, and CASH, ettringite (Aft phases), and so on (Midgley, 1979). The CSH, CAH, and CASH are regarded as cementitious products. Ca(OH)2 content was determined based on the weight loss between 450 and 580°C (El-Jazairi and Illston, 1977 and 1980; and Wang et al., 2004) and expressed as a percentage by weight of ignited sample. When heating the samples at temperature between 450 and 580°C, Ca(OH)2 is decomposed into calcium oxide (CaO) and water as in Eq. (2).

$$\text{Ca(OH)}\_{2}\text{---} + \text{---} + \text{---} \longrightarrow \text{CaO} + \text{H}\_{2}\text{O} \tag{2}$$

Due to the heat, the water is lost, leading to the decrease in overall weight. The amount of Ca(OH)2 can be approximated from this lost water by Equation (2), which is 4.11 times the amount of lost water (El-Jazairi and Illston, 1977 and 1980). The change of the cementitious products can be expressed by the change of Ca(OH)2 since they are the hydration products.

#### **2. Compaction and strength characteristics of cement stabilized clay**

Compaction characteristics of cement stabilized clay are shown in Figure 1. The clay was collected from the Suranaree University of Technology campus in Nakhon Ratchasima, Thailand. It is composed of 2% sand, 45% silt and 53% clay. Its specific gravity is 2.74. The liquid and plastic limits are approximately 74% and 27%, respectively. Based on the Unified Soil Classification System (USCS), the clay is classified as high plasticity (CH). It is found that the maximum dry unit weight of the stabilized samples is higher than that of the unstabilized samples whereas their optimum water content is practically the same. This characteristic is the same as that of cement stabilized coarse-grained soils as reported by Horpibulsuk et al. (2006). The adsorption of Ca2+ ions onto the clay particle surface

strength was used as a practical indicator to investigate the strength development. The microstructural analyses were performed using a scanning electron microscope (SEM), mercury intrusion porosimetry (MIP), and thermal gravity (TG) tests. For SEM, the cement stabilized samples were broken from the center into small fragments. The SEM samples were frozen at -195°C by immersion in liquid nitrogen for 5 minutes and evacuated at a pressure of 0.5 Pa at -40°C for 5 days (Miura et al., 1999; and Yamadera, 1999). All samples

Measurement on pore size distribution of the samples was carried out using mercury intrusion porosimeter (MIP) with a pressure range from 0 to 288 MPa, capable of measuring pore size diameter down to 5.7 nm (0.0057 micron). The MIP samples were obtained by carefully breaking the stabilized samples with a chisel. The representative samples of 3-6 mm pieces weighing between 1.0-1.5 g were taken from the middle of the cemented samples. Hydration of the samples was stopped by freezing and drying, as prepared in the SEM examination. Mercury porosimetry is expressed by the Washburn equation

θ

*D P* = −( ) 4 cos / γ θ

Thermal gravity (TG) analysis is one of the widely accepted methods for determination of hydration products, which are crystalline Ca(OH)2, CSH, CAH, and CASH, ettringite (Aft phases), and so on (Midgley, 1979). The CSH, CAH, and CASH are regarded as cementitious products. Ca(OH)2 content was determined based on the weight loss between 450 and 580°C (El-Jazairi and Illston, 1977 and 1980; and Wang et al., 2004) and expressed as a percentage by weight of ignited sample. When heating the samples at temperature between 450 and

 Ca(OH)2-----------------------> CaO + H2O (2) Due to the heat, the water is lost, leading to the decrease in overall weight. The amount of Ca(OH)2 can be approximated from this lost water by Equation (2), which is 4.11 times the amount of lost water (El-Jazairi and Illston, 1977 and 1980). The change of the cementitious products can be expressed by the change of Ca(OH)2 since they are the hydration products.

Compaction characteristics of cement stabilized clay are shown in Figure 1. The clay was collected from the Suranaree University of Technology campus in Nakhon Ratchasima, Thailand. It is composed of 2% sand, 45% silt and 53% clay. Its specific gravity is 2.74. The liquid and plastic limits are approximately 74% and 27%, respectively. Based on the Unified Soil Classification System (USCS), the clay is classified as high plasticity (CH). It is found that the maximum dry unit weight of the stabilized samples is higher than that of the unstabilized samples whereas their optimum water content is practically the same. This characteristic is the same as that of cement stabilized coarse-grained soils as reported by Horpibulsuk et al. (2006). The adsorption of Ca2+ ions onto the clay particle surface

where *D* is the pore diameter (micron) and *P* is the applied pressure (MPa).

580°C, Ca(OH)2 is decomposed into calcium oxide (CaO) and water as in Eq. (2).

**2. Compaction and strength characteristics of cement stabilized clay** 

) of 480 dynes/cm were used for pore size calculation as suggested by Eq.(1)

) of 140° and a constant surface tension of

(1)

were coated with gold before SEM (JEOL JSM-6400) analysis.

(Washburn, 1921). A constant contact angle (

mercury (

γ

decreases the repulsion between successive diffused double layers and increases edge-toface contacts between successive clay sheets. Thus, clay particles flocculate into larger clusters, which increases in the plastic limit with an insignificant change in the liquid limit (*vide* Table 1). As such, the plasticity index of the mixture decreases due to the significant increase in the plastic limit. Because the *OWC* of low swelling clays is mainly controlled by the liquid limit (Horpibulsuk et al., 2008 and 2009), the *OWC*s of the unstabilized and the stabilized samples are almost the same (*vide* Table 1). Figure 2 shows the compaction curve of the fly ash (FA) blended cement stabilized clay for different replacement ratios (ratios of cement to fly ash, C:F) compared with that of the unstabilized clay. Two fly ashes are presented in the figure: original, OFA (*D*50 = 0.03 mm) and classified, CFA (*D*50 = 0.009 mm) fly ashes. It is noted that the compaction curve of the stabilized clay is insignificantly dependent upon replacement ratio and fly ash particles. Maximum dry unit weight of the stabilized clay is higher than that of the unstabilized clay whereas their optimum water content is practically the same.

Fig. 1. Plots of dry unit weight versus water content of the uncemented and the cemented samples compacted under standard and modified Proctor energies (Horpibulsuk et al., 2010b)


Table 1. Basic properties of the cemented samples

Strength and Microstructure of Cement Stabilized Clay 443

the same compaction energy, the strength curves follow the same pattern for all curing times, which are almost symmetrical around 1.2*OWC* for the range of the water content tested. Figure 4 shows the strength versus water content relationship of the CFA blended cement stabilized clay at different replacement ratios after 60 days of curing compared with that of the unstabilized clay. The maximum strengths of the stabilized clay are at about 1.2*OWC* whereas the maximum strength of the unstabilized clay is at *OWC* (maximum dry unit weight). This is because engineering properties of unstabilized clay are mainly

Fig. 4. Strength versus water content relationship of the CFA blended cement stabilized clay

Figure 5 shows the strength development with cement content (varied over a wide range) of the stabilized samples compacted under the modified Proctor energy at 1.2 *OWC* (20%) after 7 days of curing. The strength increase can be classified into three zones. As the cement content increases, the cement per grain contact point increases and, upon hardening, imparts a commensurate amount of bonding at the contact points. This zone is designated as the *active zone*. Beyond this zone, the strength development slows down while still gradually increasing. The incremental gradient becomes nearly zero and does not make any further significant improvement. This zone is referred to as the *inert zone* (*C* = 11-30%). The strength

Influence of replacement ratio on the strength development of the blended cement stabilized clay compacted at water content (*w*) of 1.2*OWC* (*w* = 20.9%) for the five curing times is presented in Figure 6. For all curing times, the samples with 20% replacement ratio exhibit almost the same strength as those with 0% replacement ratio. The 30 and 40% replacement samples exhibit lower strength than 0% replacement samples. The samples with 10% replacement ratio exhibit the highest strength since early curing time. The sudden strength

at different replacement ratios and 60 days of curing (Horpibulsuk et al., 2009)

decrease appears when *C* > 30%. This zone is identified as the *deterioration* zone.

dependent upon the densification (packing).

Fig. 2. Compaction curves of the OFA and CFA blended cement stabilized clay and the unstabilized clay (Horpibulsuk et al., 2009)

Fig. 3. Effect of compaction energy and curing time on strength development (Horpibulsuk et al., 2010b)

Typical strength-water content relationships for different curing times and compacton energies of the stabilized samples are shown in Figure 3. The strength of the stabilized samples increases with water content up to 1.2 times the optimum water content and decreases when the water content is on the wet side of optimum. At a particular curing time, the strength curve depends on the compaction energy. As the compaction energy increases, the maximum strength increases and the water content at maximum strength decreases. For

90:10 80:20 70:30 60:40

100:0

Classified

Original

5 10 15 20 25 30

OWC = 17.4%

Water content, *w* (%)

wmod = 20.0%

5 10 15 20 25 30 35

wst = 26.4%

7 days 28 days 60 days

Water content (%)

Fig. 3. Effect of compaction energy and curing time on strength development (Horpibulsuk

Typical strength-water content relationships for different curing times and compacton energies of the stabilized samples are shown in Figure 3. The strength of the stabilized samples increases with water content up to 1.2 times the optimum water content and decreases when the water content is on the wet side of optimum. At a particular curing time, the strength curve depends on the compaction energy. As the compaction energy increases, the maximum strength increases and the water content at maximum strength decreases. For

Fig. 2. Compaction curves of the OFA and CFA blended cement stabilized clay and the

14

unstabilized clay (Horpibulsuk et al., 2009)

0

1000

2000

3000

4000

Unconfined compressive strength (kPa)

et al., 2010b)

5000

6000

7000

8000

15

16

17

Dry unit weight,

γ*d* (kN/m3

)

18

19

20

21

Modified Proctor Silty clay

LL = 74%, PL = 27% Binder content = 10%

No cement

C = 10%

Modified Proctor

> Standard Proctor

22

the same compaction energy, the strength curves follow the same pattern for all curing times, which are almost symmetrical around 1.2*OWC* for the range of the water content tested. Figure 4 shows the strength versus water content relationship of the CFA blended cement stabilized clay at different replacement ratios after 60 days of curing compared with that of the unstabilized clay. The maximum strengths of the stabilized clay are at about 1.2*OWC* whereas the maximum strength of the unstabilized clay is at *OWC* (maximum dry unit weight). This is because engineering properties of unstabilized clay are mainly dependent upon the densification (packing).

Fig. 4. Strength versus water content relationship of the CFA blended cement stabilized clay at different replacement ratios and 60 days of curing (Horpibulsuk et al., 2009)

Figure 5 shows the strength development with cement content (varied over a wide range) of the stabilized samples compacted under the modified Proctor energy at 1.2 *OWC* (20%) after 7 days of curing. The strength increase can be classified into three zones. As the cement content increases, the cement per grain contact point increases and, upon hardening, imparts a commensurate amount of bonding at the contact points. This zone is designated as the *active zone*. Beyond this zone, the strength development slows down while still gradually increasing. The incremental gradient becomes nearly zero and does not make any further significant improvement. This zone is referred to as the *inert zone* (*C* = 11-30%). The strength decrease appears when *C* > 30%. This zone is identified as the *deterioration* zone.

Influence of replacement ratio on the strength development of the blended cement stabilized clay compacted at water content (*w*) of 1.2*OWC* (*w* = 20.9%) for the five curing times is presented in Figure 6. For all curing times, the samples with 20% replacement ratio exhibit almost the same strength as those with 0% replacement ratio. The 30 and 40% replacement samples exhibit lower strength than 0% replacement samples. The samples with 10% replacement ratio exhibit the highest strength since early curing time. The sudden strength

Strength and Microstructure of Cement Stabilized Clay 445

For compacted fine-grained soils, the soil structure mainly controls the strength and resistance to deformation, which is governed by compaction energy and water content. Compaction breaks down the large clay clusters into smaller clusters and reduces the pore space. Figure 7 shows SEM photos of the unstabilized samples compacted under the modified Proctor energy at water contents in the range of 0.8*OWC* to 1.2*OWC*. On the wet side of optimum (*vide* Figure 7c), a dispersed structure is likely to develop because the quantity of pore water is enough to develop a complete double layer of the ions that are attracted to the clay particles. As such, the clay particles and clay clusters easily slide over each other when sheared, which causes low strength and stiffness. On the dry side of optimum (*vide* Figure 7a), there is not sufficient water to develop a complete double-layer; thus, the distance between two clay platelets is small enough for van der Waals type attraction to dominate. Such an attraction leads to flocculation with more surface to edge bonds; thus, more aggregates of platelets lead to compressible flocs, which make up the overall structure. At the *OWC*, the structure results from a combination of these two characteristics. Under this condition, the compacted sample exhibits the highest strength

Fig. 7. SEM photos of the uncemented samples compacted at different molding water

contents under modified Proctor energy (Horpibulsuk et al., 2010b)

**3. Microstructure of cement stabilized clay** 

**3.1 Unstabilized clay**

and stiffness.

development with time is not found for all replacement ratios. This finding is different from concrete technology where the role of fly ash as a pozzolanic material comes into play after a long curing time (generally after 60 days). In other words, the strength of concrete mixed with fly ash is higher than that without fly ash after about 60 days of curing.

Fig. 5. Strength development as a function of cement content (Horpibulsuk et al., 2010b)

Fig. 6. Relationship between strength development and replacement ratio of the CFA blended cement stabilized clay at different curing times (Horpibulsuk et al., 2009)

## **3. Microstructure of cement stabilized clay**

#### **3.1 Unstabilized clay**

444 Scanning Electron Microscopy

development with time is not found for all replacement ratios. This finding is different from concrete technology where the role of fly ash as a pozzolanic material comes into play after a long curing time (generally after 60 days). In other words, the strength of concrete mixed

7 days of curing

0 5 10 15 20 25 30 35 40 45 50

Inert zone Deterioration zone

Standard Proctor energy, w = 26% Modified Proctor energy, w = 20%

> 28 days 60 days 90 days 120 days

7 days

Cement content (%)

C : F

Fig. 6. Relationship between strength development and replacement ratio of the CFA blended cement stabilized clay at different curing times (Horpibulsuk et al., 2009)

100 : 0 90 : 10 80 : 20 70 : 30 60 : 40

Fig. 5. Strength development as a function of cement content (Horpibulsuk et al., 2010b)

Modified Proctor Binder content = 10% Classified fly ash w = 20.9%

with fly ash is higher than that without fly ash after about 60 days of curing.

0

0

2000

4000

6000

Unconfined compressive strength, *q*

*u* (kPa)

8000

10000

12000

500

1000

1500

2000

Unconfined compressive strength (kPa)

2500

3000

3500

Active zone

4000

For compacted fine-grained soils, the soil structure mainly controls the strength and resistance to deformation, which is governed by compaction energy and water content. Compaction breaks down the large clay clusters into smaller clusters and reduces the pore space. Figure 7 shows SEM photos of the unstabilized samples compacted under the modified Proctor energy at water contents in the range of 0.8*OWC* to 1.2*OWC*. On the wet side of optimum (*vide* Figure 7c), a dispersed structure is likely to develop because the quantity of pore water is enough to develop a complete double layer of the ions that are attracted to the clay particles. As such, the clay particles and clay clusters easily slide over each other when sheared, which causes low strength and stiffness. On the dry side of optimum (*vide* Figure 7a), there is not sufficient water to develop a complete double-layer; thus, the distance between two clay platelets is small enough for van der Waals type attraction to dominate. Such an attraction leads to flocculation with more surface to edge bonds; thus, more aggregates of platelets lead to compressible flocs, which make up the overall structure. At the *OWC*, the structure results from a combination of these two characteristics. Under this condition, the compacted sample exhibits the highest strength and stiffness.

Fig. 7. SEM photos of the uncemented samples compacted at different molding water contents under modified Proctor energy (Horpibulsuk et al., 2010b)

Strength and Microstructure of Cement Stabilized Clay 447

Curing time (days) Weight loss (%) Ca(OH)2 (%) 7 1.52 6.25 28 1.65 6.78 60 1.85 7.63

Table 2. Ca(OH)2 of the 10% cement samples compacted at 1.2*OWC* at different curing times

Figures 10 and 11 and Table 3 show the SEM photos, pore size distribution, and the amount of Ca(OH)2 of the stabilized samples compacted at *w* = 20% under the modified Proctor energy for different cement contents after 7 days of curing. Figures 10a-c, 10d-g, and 10h-j show SEM photos of the cemented samples in the active, inert, and deterioration zones, respectively. The SEM photo of the 3% cement sample (Figure 10a) is similar to that of the unstabilized sample because the input of cement is insignificant compared to the soil mass. As the cement content increases in the active zone, hydration products are clearly seen in the pores (*vide* Figures 10b and c) and the cementitious products significantly increase (Table 3). The cementitious products not only enhance the inter-cluster bonding strength but also fill the pore space, as shown in Figure 11: the volume of pores smaller than 0.1 micron is significantly reduced with cement, thus, the reduction in total pore volume. As a result, the strength significantly increases with cement. For the inert zone, the presence of hydration products (Figures 10d to g) and cementitious products (Table 3) is almost the same for 15- 30% cement. This results in an insignificant change in the pore size distribution and, thus, the strength. For the deterioration zone (Figure 10h-j), few hydration products are detected. Both the volumes of the highest pore size interval (1.0-0.1 micron pores) and the total pore tend to increase with cement (Figure 11). This is because the increase in cement content significantly reduces the water content, which decreases the degree of hydration and, thus,

Figures 12 and 13 show SEM photos of the CFA blended cement stabilized clay compacted at *w* = 1.2OWC (*w* = 20.9%) and cured for 28 and 60 days at different replacement ratios. The fly ash particles are clearly shown among clay-cement clusters especially for 30% replacement ratio (C:F = 70:30) for both curing times (Figures 12a and 13a). It is noted that the hydration products growing from the cement grains connect fly ash particles and claycement clusters together. Some of the surfaces of fly ash particles are coated with layers of amounts of hydration products. However, they are still smooth with different curing times. This finding is different from concrete technology where the precipitation in the pozzolanic reaction is indicated by the etching on fly ash surface (Fraay et al., 1989; Berry et al., 1994; Xu and Sarker, 1994; and Chindapasirt et al., 2005). This is because the input of cement in concrete is high enough to produce a relatively high amount of Ca(OH)2 to be consumed for pozzolanic reaction. Its water to binder ratio (*W*/*B*) is generally about 0.2-0.5, providing strength higher than 30 MPa (30,000 kPa) at 28 days of curing, whereas for ground improvement, the *W*/*B* is much lower. From this observation, it is thus possible to conclude that the pozzolanic reaction is minimal for strength development in the blended cement

under modified Proctor energy (Horpibulsuk et al., 2010b)

**3.2.2 Effect of cement content** 

cementitious products (Table 3).

**3.2.3 Effect of fly ash** 

stabilized clay.

## **3.2 Stabilized clay**

## **3.2.1 Effect of curing time**

Figure 8 shows SEM photos of the 10% cement samples compacted at *w* = 20% (1.2*OWC*) under the modified Proctor energy and cured for different curing times. After 4 hours of curing, the soil clusters and the pores are covered and filled by the cement gel (hydrated cement) (*vide* Figure 8a). Over time, the hydration products in the pores are clearly seen and the soil-cement clusters tend to be larger (*vide* Figures 8b through d) because of the growth of cementitious products over time (*vide* Table 2).

The effect of curing time on the pore size distribution of the stabilized samples is illustrated in Figure 9. It is found that, during the early stage of hydration (fewer than 7 days of curing), the volume of pores smaller than 0.1 micron significantly decreases while the volume of pores larger than 0.1 micron slightly increases. This result shows that during 7 days of curing, the cementitious products fill pores smaller than 0.1 micron and the coarse particles (unhydrated cement particles) cause large soil-cement clusters and large pore space. After 7 days of curing, the volume of pores larger than 0.1 micron tends to decrease while the volume of pores smaller than 0.1 micron tends to increase possibly because the cementitious products fill the large pores (larger than 0.1 micron). As a result, the volume of small pores (smaller than 0.1 micron) increases, and the total pore volume decreases.

Fig. 8. SEM photos of the 10% cement samples compacted at 1.2*OWC* under modified Proctor energy at different curing times (Horpibulsuk et al., 2010b)


Table 2. Ca(OH)2 of the 10% cement samples compacted at 1.2*OWC* at different curing times under modified Proctor energy (Horpibulsuk et al., 2010b)

#### **3.2.2 Effect of cement content**

446 Scanning Electron Microscopy

Figure 8 shows SEM photos of the 10% cement samples compacted at *w* = 20% (1.2*OWC*) under the modified Proctor energy and cured for different curing times. After 4 hours of curing, the soil clusters and the pores are covered and filled by the cement gel (hydrated cement) (*vide* Figure 8a). Over time, the hydration products in the pores are clearly seen and the soil-cement clusters tend to be larger (*vide* Figures 8b through d) because of the growth

The effect of curing time on the pore size distribution of the stabilized samples is illustrated in Figure 9. It is found that, during the early stage of hydration (fewer than 7 days of curing), the volume of pores smaller than 0.1 micron significantly decreases while the volume of pores larger than 0.1 micron slightly increases. This result shows that during 7 days of curing, the cementitious products fill pores smaller than 0.1 micron and the coarse particles (unhydrated cement particles) cause large soil-cement clusters and large pore space. After 7 days of curing, the volume of pores larger than 0.1 micron tends to decrease while the volume of pores smaller than 0.1 micron tends to increase possibly because the cementitious products fill the large pores (larger than 0.1 micron). As a result, the volume of

small pores (smaller than 0.1 micron) increases, and the total pore volume decreases.

Fig. 8. SEM photos of the 10% cement samples compacted at 1.2*OWC* under modified

Proctor energy at different curing times (Horpibulsuk et al., 2010b)

**3.2 Stabilized clay** 

**3.2.1 Effect of curing time** 

of cementitious products over time (*vide* Table 2).

Figures 10 and 11 and Table 3 show the SEM photos, pore size distribution, and the amount of Ca(OH)2 of the stabilized samples compacted at *w* = 20% under the modified Proctor energy for different cement contents after 7 days of curing. Figures 10a-c, 10d-g, and 10h-j show SEM photos of the cemented samples in the active, inert, and deterioration zones, respectively. The SEM photo of the 3% cement sample (Figure 10a) is similar to that of the unstabilized sample because the input of cement is insignificant compared to the soil mass. As the cement content increases in the active zone, hydration products are clearly seen in the pores (*vide* Figures 10b and c) and the cementitious products significantly increase (Table 3).

The cementitious products not only enhance the inter-cluster bonding strength but also fill the pore space, as shown in Figure 11: the volume of pores smaller than 0.1 micron is significantly reduced with cement, thus, the reduction in total pore volume. As a result, the strength significantly increases with cement. For the inert zone, the presence of hydration products (Figures 10d to g) and cementitious products (Table 3) is almost the same for 15- 30% cement. This results in an insignificant change in the pore size distribution and, thus, the strength. For the deterioration zone (Figure 10h-j), few hydration products are detected. Both the volumes of the highest pore size interval (1.0-0.1 micron pores) and the total pore tend to increase with cement (Figure 11). This is because the increase in cement content significantly reduces the water content, which decreases the degree of hydration and, thus, cementitious products (Table 3).

### **3.2.3 Effect of fly ash**

Figures 12 and 13 show SEM photos of the CFA blended cement stabilized clay compacted at *w* = 1.2OWC (*w* = 20.9%) and cured for 28 and 60 days at different replacement ratios. The fly ash particles are clearly shown among clay-cement clusters especially for 30% replacement ratio (C:F = 70:30) for both curing times (Figures 12a and 13a). It is noted that the hydration products growing from the cement grains connect fly ash particles and claycement clusters together. Some of the surfaces of fly ash particles are coated with layers of amounts of hydration products. However, they are still smooth with different curing times. This finding is different from concrete technology where the precipitation in the pozzolanic reaction is indicated by the etching on fly ash surface (Fraay et al., 1989; Berry et al., 1994; Xu and Sarker, 1994; and Chindapasirt et al., 2005). This is because the input of cement in concrete is high enough to produce a relatively high amount of Ca(OH)2 to be consumed for pozzolanic reaction. Its water to binder ratio (*W*/*B*) is generally about 0.2-0.5, providing strength higher than 30 MPa (30,000 kPa) at 28 days of curing, whereas for ground improvement, the *W*/*B* is much lower. From this observation, it is thus possible to conclude that the pozzolanic reaction is minimal for strength development in the blended cement stabilized clay.

Strength and Microstructure of Cement Stabilized Clay 449

Fig. 10. SEM photos of the cemented samples compacted at different cement contents under

modified Proctor energy after 7 days of curing (Horpibulsuk et al., 2010b)

Fig. 9. Pore size distribution of the 10% cement samples compacted at different curing times under modified Proctor energy after 7 days of curing (Horpibulsuk et al., 2010b)

Modified Proctor w = 20%, C = 10%

0.001 0.01 0.1 1 10 100 1000

Pore diameter (micron)

28 days of curing

60 days of curing

7 days of curing 4 hours of curing

C = 0

12345

Fig. 9. Pore size distribution of the 10% cement samples compacted at different curing times

under modified Proctor energy after 7 days of curing (Horpibulsuk et al., 2010b)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0

0.03

0.06

0.09

Cummulative pore volume (cc/g)

0.12

0.15

Fig. 10. SEM photos of the cemented samples compacted at different cement contents under modified Proctor energy after 7 days of curing (Horpibulsuk et al., 2010b)

Strength and Microstructure of Cement Stabilized Clay 451

0.001 0.01 0.1 1 10 100 1000

Pore diameter (micron)

C = 0% C = 3% C = 15% C = 30% C = 45%

12345

contents and under modified Proctor energy after 7 days of curing time (Horpibulsuk et al.,

Fig. 11. Pore size distribution of the cemented samples compacted at different water

0

2010b)

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0

0.03

0.06

0.09

Cummulative pore volume (cc/g)

0.12

Modified Proctor w = 20% 7 days of curing

0.15

Fig. 10. *(Continued*)

Fig. 10. *(Continued*)

Fig. 11. Pore size distribution of the cemented samples compacted at different water contents and under modified Proctor energy after 7 days of curing time (Horpibulsuk et al., 2010b)

Strength and Microstructure of Cement Stabilized Clay 453

Fig. 13. SEM photos of the blended cement stabilized clay at different replacement ratios

Figure 14 shows the pore size distribution of the CFA blended cement stabilized clay at different curing times and replacement ratios. The pore size distribution for all replacement ratios is almost identical since the grain size distribution and *D*50 of PC and CFA are practically the same. This implies that the strength of blended cement stabilized clay is not directly dependent upon only pore size distribution. However, it might control permeability and durability. With time, the total pore and large pore (>0.1 micron) volumes decrease while the small pore (<0.1 micron) volume increases. This is due to the growth of

Table 4 shows Ca(OH)2 of the blended cement stabilized clay at *w* = 1.2*OWC* for different curing times. For a particular water content and curing time, Ca(OH)2 for the CFA blended cement stabilized clay decreases with replacement ratio only when the replacement ratio is in excess of a certain value. This finding is different from concrete technology in which Ca(OH)2 decreases significantly with the increase in fineness and replacement ratio (Berry et al., 1989; Sybertz and Wiens, 1991; and Harris et al., 1987; and Chindapasirt et al., 2005 and 2006; and others) due to pozzolanic reaction. The highest Ca(OH)2 is at 10% replacement ratio (C:F = 90:10) for all curing times. For replacement ratios higher than 10%, Ca(OH)2 decreases with replacement ratio. Ca(OH)2 at 20% replacement ratio is almost the same as that at 0% replacement ratio. This finding is associated with the strength test results that the 10% replacement ratio gives the highest strength and the strengths for 0% and 20% replacement ratios are practically the same for all curing times. It is thus concluded that

after 60 days of curing (Horpibulsuk et al., 2010b)

cementitious products filling up pores.


Table 3. Ca(OH)2 of the cemented samples compacted at different cement contents under modified Proctor energy after 7 days of curing.

Fig. 12. SEM photos of the blended cement stabilized clay at different replacement ratios after 28 days of curing (Horpibulsuk et al., 2009)

Improvement zones Cement (%) Weight loss (%) Ca(OH)2 (%)

Table 3. Ca(OH)2 of the cemented samples compacted at different cement contents under

Fig. 12. SEM photos of the blended cement stabilized clay at different replacement ratios

3 1.34 5.51 7 1.50 6.17 11 1.60 6.58

15 1.62 6.66 20 1.65 6.78 30 1.68 6.90

35 1.54 6.33 40 1.48 6.08 45 1.37 5.63

Active

Inert

Deterioration

modified Proctor energy after 7 days of curing.

after 28 days of curing (Horpibulsuk et al., 2009)

Fig. 13. SEM photos of the blended cement stabilized clay at different replacement ratios after 60 days of curing (Horpibulsuk et al., 2010b)

Figure 14 shows the pore size distribution of the CFA blended cement stabilized clay at different curing times and replacement ratios. The pore size distribution for all replacement ratios is almost identical since the grain size distribution and *D*50 of PC and CFA are practically the same. This implies that the strength of blended cement stabilized clay is not directly dependent upon only pore size distribution. However, it might control permeability and durability. With time, the total pore and large pore (>0.1 micron) volumes decrease while the small pore (<0.1 micron) volume increases. This is due to the growth of cementitious products filling up pores.

Table 4 shows Ca(OH)2 of the blended cement stabilized clay at *w* = 1.2*OWC* for different curing times. For a particular water content and curing time, Ca(OH)2 for the CFA blended cement stabilized clay decreases with replacement ratio only when the replacement ratio is in excess of a certain value. This finding is different from concrete technology in which Ca(OH)2 decreases significantly with the increase in fineness and replacement ratio (Berry et al., 1989; Sybertz and Wiens, 1991; and Harris et al., 1987; and Chindapasirt et al., 2005 and 2006; and others) due to pozzolanic reaction. The highest Ca(OH)2 is at 10% replacement ratio (C:F = 90:10) for all curing times. For replacement ratios higher than 10%, Ca(OH)2 decreases with replacement ratio. Ca(OH)2 at 20% replacement ratio is almost the same as that at 0% replacement ratio. This finding is associated with the strength test results that the 10% replacement ratio gives the highest strength and the strengths for 0% and 20% replacement ratios are practically the same for all curing times. It is thus concluded that

Strength and Microstructure of Cement Stabilized Clay 455

7 100:0 - 6.67 6.67 0.00 90:10 CFA 6.97 6.00 0.97 80:20 CFA 6.79 5.34 1.45 70:30 CFA 6.39 4.67 1.72

28 100:0 - 6.79 6.79 0.00 90:10 CFA 6.96 6.11 0.85 80:20 CFA 6.81 5.43 1.38 70:30 CFA 6.57 4.75 1.82

60 100:0 - 6.82 6.82 0.00 90:10 CFA 7.16 6.14 1.02 80:20 CFA 6.92 5.46 1.46 70:30 CFA 6.68 4.77 1.91

90 100:0 - 7.07 7.07 0.00 90:10 CFA 7.28 6.36 0.91 80:20 CFA 6.94 5.66 1.28 70:30 CFA 6.67 4.95 1.72

120 100:0 - 7.08 7.08 0.00 90:10 CFA 7.29 6.37 0.92 80:20 CFA 6.96 5.66 1.30 70:30 CFA 6.70 4.96 1.74

Table 4. Ca(OH)2 of the blended cement stabilized clay at different replacement ratios and

From SEM and MIP observation, it is notable that the small pore (<0.1 micron) volumes of the blended cement stabilized clay are higher than those of the cement stabilized clay. This implies that a number of large clay-cement clusters possessing large pore space reduce

Fly ash Ca(OH)2 (%)

Hydration Induced

(dispersion effect)

Test (Combined effect)

Curing time (days)

curing times.

Replacement ratio C : F

cementitious products mainly control the strength development. In other words, the strengths of the blended cement stabilized clay having different mixing condition (binder content, replacement ratios, and curing time) could be identical as long as cementitious products are the same.

(a) 7 days


Fig. 14. Pore size distribution of the blended cement stabilized clay at different replacement ratios and curing times (Horpibulsuk et al., 2010b)

cementitious products mainly control the strength development. In other words, the strengths of the blended cement stabilized clay having different mixing condition (binder content, replacement ratios, and curing time) could be identical as long as cementitious

1234

(a) 7 days

1 23 4

(b) 28 days

Fig. 14. Pore size distribution of the blended cement stabilized clay at different replacement

products are the same.

0

0

ratios and curing times (Horpibulsuk et al., 2010b)

0.02

0.04

0.06

0.08

0.1

0.02

0.04

0.06

0.08

0.1


Table 4. Ca(OH)2 of the blended cement stabilized clay at different replacement ratios and curing times.

From SEM and MIP observation, it is notable that the small pore (<0.1 micron) volumes of the blended cement stabilized clay are higher than those of the cement stabilized clay. This implies that a number of large clay-cement clusters possessing large pore space reduce

Strength and Microstructure of Cement Stabilized Clay 457

4. From the microstructural investigation, it is concluded that the role of fly ash as a noninteracting material is to disperse the cement-clay clusters with large pore space into smaller clusters with smaller pore space. The dispersing effect by fly ash increases the reactive surfaces, and hence the increase in degree of hydration as clearly illustrated by

5. The increase in cementitious products with time is observed from the scanning electron microscope, mercury intrusion porosimetry and thermal gravity test. With time, the large pore (>0.1 micron) and total pore volumes decrease while the small pore (<0.1 micron) volumes increase. This shows the growth of the cementitious products filling

This work was a part of the author's researches conducted in the Suranaree University of Technology. The authors would like to acknowledge the financial support provided by the Higher Education Research Promotion and National Research University Project of Thailand, Office of Higher Education Commission, the Thailand Research Fund (TRF), and the Suranaree University of Technology. The author is indebted to Dr. Theerawat Sinsiri, School of Civil Engineering, Suranaree University of Technology for his technical advice in cement and concrete technology. The author is grateful to Mr. Yutthana Raksachon, ex-

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*Fume, Slag, and Natural Pozzolans in Concrete* (SP-114), Detroit, pp.241-273. Chindaprasirt, P., Jaturapitakkul, C. & Sinsiri, T. (2005). Effect of Fly Ash Fineness on

Clough, G.W., Sitar, N., Bachus, R.C. & Rad, N.S. (1981). Cemented Sands under Static

El-Jazairi, B. & Illston, J.M. (1977). A Simultaneous Semi-Isothermal Method of

El-Jazairi, B. & Illston, J.M. (1980). The Hydration of Cement Plate Using the Semi-

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reaction.

up the large pores.

master's student for his assistance.

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**5. Acknowledgment** 

**6. References** 

different from the application of fly ash as a pozzolanic material in concrete structure in which Ca(OH)2 from hydration is much enough to be consumed for pozzolanic

when fly ashes are utilized. In other words, the fly ashes disperse large clay-cement clusters into small clusters, resulting in the increase in small pore volume. The higher the replacement ratio, the better the dispersion. Consequently, the reactive surfaces increase, resulting in the increase in cementitious products as illustrated by dispersion induced Ca(OH)2 (*vide* Table 4). It is the difference in Ca(OH)2 of the blended cement stabilized clay due to the combined effect (hydration and dispersion) and due to hydration. Ca(OH)2 due to combined effect is directly obtained from TG test on the blended cement stabilized sample. Ca(OH)2 due to hydration is also obtained from TG test on the cement stabilized sample having the same cement content as the blended cement stabilized sample. For simplicity, Ca(OH)2 due to hydration at any cement content can be estimated from known Ca(OH)2 of cement stabilized clay at a specific cement content by assuming that the change in the cementitious products is directly proportional to the input of cement (Sinsiri et al., 2006). Thus, Ca(OH)2 due to hydration (*H*) for any replacement ratio at a particular curing time is approximated in the form.

$$H = T \times \left(1 - F \,/\, 100\right) \tag{3}$$

where *T* is known Ca(OH)2 of the cement stabilized clay (0% replacement ratio) obtained from TG test, and *F* is the replacement ratio expressed in percentage. Sinsiri et al. (2006) have shown that Ca(OH)2 of the cement paste with fly ash is always lower than Ca(OH)2 of the cement paste without fly ash, resulted from Ca(OH)2 consumption for pozzolanic reaction. The same is not for the blended cement stabilized clay. It is found that Ca(OH)2 due to combined effect is higher than that due to hydration for all replacement ratios and curing times. The dispersion induced Ca(OH)2 increases with the replacement ratio for all curing times.

#### **4. Conclusions**

This chapter presents the role of curing time, cement content and fly ash content on the strength and microstructure development in the cement stabilized clay. The following conclusions can be advanced:


different from the application of fly ash as a pozzolanic material in concrete structure in which Ca(OH)2 from hydration is much enough to be consumed for pozzolanic reaction.


## **5. Acknowledgment**

456 Scanning Electron Microscopy

when fly ashes are utilized. In other words, the fly ashes disperse large clay-cement clusters into small clusters, resulting in the increase in small pore volume. The higher the replacement ratio, the better the dispersion. Consequently, the reactive surfaces increase, resulting in the increase in cementitious products as illustrated by dispersion induced Ca(OH)2 (*vide* Table 4). It is the difference in Ca(OH)2 of the blended cement stabilized clay due to the combined effect (hydration and dispersion) and due to hydration. Ca(OH)2 due to combined effect is directly obtained from TG test on the blended cement stabilized sample. Ca(OH)2 due to hydration is also obtained from TG test on the cement stabilized sample having the same cement content as the blended cement stabilized sample. For simplicity, Ca(OH)2 due to hydration at any cement content can be estimated from known Ca(OH)2 of cement stabilized clay at a specific cement content by assuming that the change in the cementitious products is directly proportional to the input of cement (Sinsiri et al., 2006). Thus, Ca(OH)2 due to hydration (*H*) for any replacement ratio at a particular curing time is

where *T* is known Ca(OH)2 of the cement stabilized clay (0% replacement ratio) obtained from TG test, and *F* is the replacement ratio expressed in percentage. Sinsiri et al. (2006) have shown that Ca(OH)2 of the cement paste with fly ash is always lower than Ca(OH)2 of the cement paste without fly ash, resulted from Ca(OH)2 consumption for pozzolanic reaction. The same is not for the blended cement stabilized clay. It is found that Ca(OH)2 due to combined effect is higher than that due to hydration for all replacement ratios and curing times. The dispersion induced Ca(OH)2 increases with the replacement ratio for all

This chapter presents the role of curing time, cement content and fly ash content on the strength and microstructure development in the cement stabilized clay. The following

1. The strength development with cement content for a specific water content is classified into three zones: active, inert and deterioration. In the active zone, the volume of pores smaller than 0.1 micron significantly decreases with the addition of cement because of the increase in cementitious products. In the inert zone, both pore size distribution and cementitious products change insignificantly with increasing cement; thus, there is a slight change in strength. In the deterioration zone, the water is not adequate for hydration because of the excess of cement input. Consequently, as cement content

2. The flocculation of clay particles due to the cation exchange process is controlled by cement content, regardless of fly ash content. It results in the increase in dry unit weight with insignificant change in liquid limit. Hence, *OWC*s of stabilized and unstabilized

3. The surfaces of fly ash in the blended cement stabilized clay are still smooth for different curing times and fineness, suggesting that pozzolanic reaction is minimal. Fly ash is considered as a dispersing material in the blended cement stabilized clay. This is

increases, the cementitious products and strength decreases.

silty clay (low swelling clay) are practically the same.

*HT F* =× − ( ) 1 /100 (3)

approximated in the form.

curing times.

**4. Conclusions** 

conclusions can be advanced:

This work was a part of the author's researches conducted in the Suranaree University of Technology. The authors would like to acknowledge the financial support provided by the Higher Education Research Promotion and National Research University Project of Thailand, Office of Higher Education Commission, the Thailand Research Fund (TRF), and the Suranaree University of Technology. The author is indebted to Dr. Theerawat Sinsiri, School of Civil Engineering, Suranaree University of Technology for his technical advice in cement and concrete technology. The author is grateful to Mr. Yutthana Raksachon, exmaster's student for his assistance.

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