Abstract

The current research work is focused on fabrication of Aluminium Alloy 8011 with 4% fly ash composite (AA8011-4% FA) by using the stir casting method. Wear behaviour and description of the composite are evaluated in different process parameters by using a pin-on-disc at room temperature. Fly ash (FA) in the range of (4 wt. %, average micron size 10–30 μm) is included into the matrix, and its sensitivity analysis is investigated. Three level of Central Composite Design model is developed by using Response Surface Methodology equation with different process parameters via load, time and sliding velocity are separate in the range of (5–15 N), (5–15 min) and (1.5–4.5 m/s) respectively. The surface plot shows that wear rate increases with increasing load, time and sliding velocity. A sensitivity analysis is also carried out and compared with the relative impact of input parameters on wear behaviour in order to verify the measurement errors on the values of the uncertainty in estimated parameters of three inputs such as normal load, time and sliding velocity on wear rate (WR) and coefficient of friction (COF). The result shows that normal load is more sensitive than the other parameters. The variation of load causes more changes in wear rate.

Keywords: aluminium alloy 8011 (AA8011), fly ash (FA), response surface methodology (RSM), wear rate (WR), coefficient of friction (COF), sensitivity analysis (SA)

## 1. Introduction

Metal matrix composites (MMCs) occur as an essential category of material used in space and transportation industries. There is an inclusive in dropping the wear in demand to decrease the tradition of material properties and expenditure of energy. This controlling of wear should be considered cautiously from the idea of choosing the alloy composition, reinforcement and additionally the process techniques. The incorporation of hard reinforcement segments, particulates, fibres and whiskers has been capable of these composites through smart tribological characteristics [2, 8, 12–18].

These reinforcements will either be value-added ex-situ or created as in-situ composites within the dissolved. It is glowing well-known that in-situ supports stay of the many smart benefits appreciate wholesome boundary and extraordinary affection power through the medium, homogenously circulated minor parts within the matrix, extraordinary mechanical assets and low cost. In-situ ceramic mixtures appreciate Al2O3 [23], TiB2 [1] and TiC [24] widely occupied as reinforcements in aluminium fabricated composites.

analysis shows that bead width, dilution, area of penetration and coefficient of internal shape are mostly affected by the change of process parameters [20].

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity…

to voltage and speed variations than bead height and penetration [21].

RPM increases compared with welding RPM and axial force [22].

are compared and verified with the experimental result.

DOI: http://dx.doi.org/10.5772/intechopen.89554

S.No. Description Details

2. Normal load range 5–15 N 3. Disc speed 100–1000 rpm 4. Pre-set timer range 5–15 min

6. Wear disc track diameter 90 mm 7. Specimen pin diameter 10 mm 8. Pin length 50 mm 9. ASTM standard G99

10. Measurement and real-time data acquisition

Specification of wear testing equipment.

Table 1.

121

1. Make & Model DUCOM, TR-20LE-PHM-400

5. Wear disc thickness 8 mm (EN 31 steel (C, 1.00%; Si, 0.20%; Mn, 0.50%; Cr,

1.40%; hardness of 60 HRC)

Wear, friction, COF and temperature.

2. Dry sliding wear test

Table 1.

We investigated the sensitivity analysis of weld bead parameters such as bead width, bead height and penetration to variations in current, voltage and speed in the submerged arc welding process. The result shows that bead width is more sensitive

Sensitivity analysis to predict the tensile strength of friction stir welded alloy AA7039 aluminium. Based on the result they concluded, it was found that the sensitivities of rotational speed cause large changes in tensile strength when the

In the present work, an attempt is made to develop a mathematical model to calculate the wear rate of (AA8011-4% FA) composite and analyse the impact of normal load (N), time (min) and sliding velocity (m/sec) on wear rate and coefficient of friction. Interestingly, mathematical models are conducted consistent with central composite rotatable utilised. Mathematical models are established to predict the impact of method constraints on the responses. Finally, the sensitivity results

The tribological properties of the samples were assessed using a dry sliding wear test for different number of specimens by using a pin-on-disc machine as shown in

Figure 1 reveals that the surface of each pin and disc is clean with a soft paper soaked in resolving before actual testing. The fabric loss from the composite surface is measured employing exactness electronic balancing machine with an accuracy of 0.0001 g. Wear rate is unique of the maximum significant criteria on behalf of formative the pin-on-disc operation. Higher wear rate is always preferred in such operations. Wear rate was measured from the weight loss. Each trial was repeated twice and the average weight loss was taken for the analysis of wear rate. In this study, the machining performance and the mathematical model were evaluated

Aluminium ash composites can be synthesised through the liquid metal stir casting, compo casting (semi-solid processing), changed compo casting and squeeze casting techniques. The stir casting route product is well distributed, moderately agglomerate and consistent free fly ash particle composites [1–4].

AA2011 matrix reinforced with SiC reinforcement (5 & 10%) produced by liquid metallurgy route. SiC abrasive wear rate is increased with increasing applied load, sliding distance and element size compared for Al2O3emery paper is used means the wear rate increase of element size, applied load decrease with increasing the sliding distance and at the same time, the abrasive size was more effective for both matrix and composite [5].

AA6061 with 1% of CNT reinforcement is produced by ball milling and spark plasma process and it is reported that the mild wear rate and friction coefficient are low compared to the monolithic 6061 alloy [6].

The wear rate in a concession of weight loss per unit sliding distance, coefficient of friction and volume loss continues to achieve for the metal matrix composites. The results of the composite show higher wear resistance than matrix metal [7].

AA7075/graphite composite materials with (5–20) wt.% are fabricated by a liquid casting technique and pin on disc method to calculate the wear rate. The coefficient of friction is compact with the calculation of graphite content and stretched a minimum at 5 wt. % graphite content [8].

Develops the mathematical typical is settled by mistreatment regression analysis methodology for the expectation of damage performance of the MMC besides sufficiency of the prototypical has been valid expending analysis of variance (ANOVA) methods. Finally, the inflation of parameter has in addition been done using style trained software package. The results ensure unprotected that response surface methodology (RSM) may be a smart tool for expectation of wear behaviour below combined sliding and rolling action [9].

Develops a regression prototypical is valid by normal mathematics code SYSTAT 12 and normal arithmetic tools equivalent to analysis of variance (ANOVA) and student's t-check. It has been found that establishing the regression prototypical is also effectively wished to calculate the wear rate at 95% confidence level and expected trends are mentioned with the assistance of worn surface morphologies. The results of the composite show higher wear of the metal matrix [10].

Develops a mathematical model to predict the wear rate of AA6061/ (010%) ZrB2 in-situ composites. The factors thought of area unit sliding speed, sliding distance, traditional load and mass fraction of ZrB2 particles. The impact of those factors on the damage proportion of the made-up composite is examined and additionally, the expected trends are mentioned by perceptive the injury surface morphologies [11].

Sensitivity analysis of the process parameters of gas metal arc welding process of welding speed, voltage and current. Based on the result, we represent the success of the processing parameters and showed that the change of process parameters influences the bead width and bead height with further strong penetration [19].

The effect of welding on flux cored arc welding material of 317 L on a structural steel plate. The process parameters used in the experiment were welding current, speed and nozzle to plate distance. Sensitivity analysis has been applied to find out the process parameters with the most influence on the bead geometry. Sensitivity

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity… DOI: http://dx.doi.org/10.5772/intechopen.89554

analysis shows that bead width, dilution, area of penetration and coefficient of internal shape are mostly affected by the change of process parameters [20].

We investigated the sensitivity analysis of weld bead parameters such as bead width, bead height and penetration to variations in current, voltage and speed in the submerged arc welding process. The result shows that bead width is more sensitive to voltage and speed variations than bead height and penetration [21].

Sensitivity analysis to predict the tensile strength of friction stir welded alloy AA7039 aluminium. Based on the result they concluded, it was found that the sensitivities of rotational speed cause large changes in tensile strength when the RPM increases compared with welding RPM and axial force [22].

In the present work, an attempt is made to develop a mathematical model to calculate the wear rate of (AA8011-4% FA) composite and analyse the impact of normal load (N), time (min) and sliding velocity (m/sec) on wear rate and coefficient of friction. Interestingly, mathematical models are conducted consistent with central composite rotatable utilised. Mathematical models are established to predict the impact of method constraints on the responses. Finally, the sensitivity results are compared and verified with the experimental result.

#### 2. Dry sliding wear test

of the many smart benefits appreciate wholesome boundary and extraordinary affection power through the medium, homogenously circulated minor parts within the matrix, extraordinary mechanical assets and low cost. In-situ ceramic mixtures appreciate Al2O3 [23], TiB2 [1] and TiC [24] widely occupied as reinforcements in

Aluminium ash composites can be synthesised through the liquid metal stir casting, compo casting (semi-solid processing), changed compo casting and squeeze casting techniques. The stir casting route product is well distributed, moderately

AA2011 matrix reinforced with SiC reinforcement (5 & 10%) produced by liquid metallurgy route. SiC abrasive wear rate is increased with increasing applied load, sliding distance and element size compared for Al2O3emery paper is used means the wear rate increase of element size, applied load decrease with increasing the sliding distance and at the same time, the abrasive size was more effective for

AA6061 with 1% of CNT reinforcement is produced by ball milling and spark plasma process and it is reported that the mild wear rate and friction coefficient are

The wear rate in a concession of weight loss per unit sliding distance, coefficient of friction and volume loss continues to achieve for the metal matrix composites. The results of the composite show higher wear resistance than matrix metal [7]. AA7075/graphite composite materials with (5–20) wt.% are fabricated by a liquid casting technique and pin on disc method to calculate the wear rate. The coefficient of friction is compact with the calculation of graphite content and

Develops the mathematical typical is settled by mistreatment regression analysis

Develops a regression prototypical is valid by normal mathematics code SYSTAT 12 and normal arithmetic tools equivalent to analysis of variance (ANOVA) and student's t-check. It has been found that establishing the regression prototypical is also effectively wished to calculate the wear rate at 95% confidence level and expected trends are mentioned with the assistance of worn surface morphologies.

Develops a mathematical model to predict the wear rate of AA6061/ (010%) ZrB2 in-situ composites. The factors thought of area unit sliding speed, sliding distance, traditional load and mass fraction of ZrB2 particles. The impact of those factors on the damage proportion of the made-up composite is examined and additionally, the expected trends are mentioned by perceptive the injury surface

Sensitivity analysis of the process parameters of gas metal arc welding process of welding speed, voltage and current. Based on the result, we represent the success of the processing parameters and showed that the change of process parameters influ-

The effect of welding on flux cored arc welding material of 317 L on a structural steel plate. The process parameters used in the experiment were welding current, speed and nozzle to plate distance. Sensitivity analysis has been applied to find out the process parameters with the most influence on the bead geometry. Sensitivity

methodology for the expectation of damage performance of the MMC besides sufficiency of the prototypical has been valid expending analysis of variance (ANOVA) methods. Finally, the inflation of parameter has in addition been done using style trained software package. The results ensure unprotected that response surface methodology (RSM) may be a smart tool for expectation of wear behaviour

The results of the composite show higher wear of the metal matrix [10].

ences the bead width and bead height with further strong penetration [19].

agglomerate and consistent free fly ash particle composites [1–4].

aluminium fabricated composites.

Aluminium Alloys and Composites

both matrix and composite [5].

low compared to the monolithic 6061 alloy [6].

stretched a minimum at 5 wt. % graphite content [8].

below combined sliding and rolling action [9].

morphologies [11].

120

The tribological properties of the samples were assessed using a dry sliding wear test for different number of specimens by using a pin-on-disc machine as shown in Table 1.

Figure 1 reveals that the surface of each pin and disc is clean with a soft paper soaked in resolving before actual testing. The fabric loss from the composite surface is measured employing exactness electronic balancing machine with an accuracy of 0.0001 g. Wear rate is unique of the maximum significant criteria on behalf of formative the pin-on-disc operation. Higher wear rate is always preferred in such operations. Wear rate was measured from the weight loss. Each trial was repeated twice and the average weight loss was taken for the analysis of wear rate. In this study, the machining performance and the mathematical model were evaluated


#### Table 1.

Specification of wear testing equipment.

Figure 1. Pin-on –disc wear experimental setup.

using Eqs. (1) and (2). Tables 2 and 3 show that the setup of the experiment was designed on the idea of the central composite design (CCD) technique.

$$Wear\ rate\ (\text{g/min}) = \frac{(metal\text{removed})}{(part)}\tag{1}$$

The factorial portion of CCD is a full factorial design with all mixtures of the factors at three levels (+1, 0, �1) and composed of the eight-star points and six central points (coded level 0), which were the centre between the high and the low levels. The star points were at the face-centre of the cube portion that corresponds to an α value of 1 and this sort of design was usually known as the "face-centred CCD." The tests were conducted using stipulated conditions in keeping with the face-centred CCD with 20 experimental observations at three independent input variables [15–18].

$$\mathbf{Y}\_{\mathbf{u}} = \mathbf{b}\_{\mathbf{o}} + \sum\_{i=1}^{k} \mathbf{b}\_{i}\mathbf{x}\_{i} + \sum\_{i=1}^{k} \mathbf{b}\_{i\mathbf{i}}\mathbf{x}^{2}\_{i} + \sum\_{j>1}^{k} \mathbf{b}\_{i\mathbf{j}}\mathbf{x}\_{i}\mathbf{x}\_{j} \tag{2}$$

2.1 Design of experiment

method parameters is obtained as follows:

Design of experiment matrix and wear characteristics.

DOI: http://dx.doi.org/10.5772/intechopen.89554

results.

123

Table 3.

In this study, a second-order polynomial was selected to develop empirical equations to represent responses (wear rate and COF) in terms of controllable variables such as normal load (A), time (B) and sliding velocity (C). The final response equations were developed for wear rate and COF using the experimental

Run Load (N) Time (min) Sliding velocity (m/sec) WR (g/min) � <sup>10</sup>�<sup>5</sup> COF (μ) � <sup>10</sup>�<sup>2</sup> 1 5 5 1.5 342 55.9 2 15 5 1.5 434 37.2 3 5 15 1.5 372 49.7 4 15 15 1.5 484 54.2 5 5 5 4.5 428 36.9 6 15 5 4.5 534 33.4 7 5 15 4.5 454 26.6 8 15 15 4.5 585 45.2 9 5 10 3.0 422 52.3 10 15 10 3.0 535 52.3 11 10 5 3.0 398 34.4 12 10 15 3.0 434 37.5 13 10 10 1.5 380 38.3 14 10 10 4.5 466 24.9 15 10 10 3.0 430 38.2 16 10 10 3.0 435 38.0 17 10 10 3.0 434 38.4 18 10 10 3.0 430 38.6 19 10 10 3.0 432 38.4 20 10 10 3.0 432 38.2

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity…

This mathematical prototypical has been achieved to reproduce the independent, quadratic and interactive effects of some machining parameters on the machined for wear rate (WR) and coefficient of friction (COF). The empirical relationship for correlating the WR and COF the thought of dry sliding wear

WR gð Þ� <sup>=</sup> min <sup>10</sup>�<sup>5</sup> <sup>¼</sup> <sup>317</sup>:<sup>31</sup> � <sup>30</sup>:17 A <sup>þ</sup> <sup>14</sup>:41 B <sup>þ</sup> <sup>48</sup>:85 C <sup>þ</sup> <sup>1</sup>:8673 A2

COF ð Þ� <sup>μ</sup> <sup>10</sup>�<sup>2</sup> <sup>¼</sup> <sup>97</sup>:<sup>512</sup> � <sup>14</sup>:661 A <sup>þ</sup> <sup>0</sup>:639 B <sup>þ</sup> <sup>10</sup>:816 C <sup>þ</sup> <sup>0</sup>:54745 A2

� 0:050 B � C

� 0:1550 B � C

� <sup>0</sup>:6327 B<sup>2</sup> � <sup>3</sup>:919 C3 <sup>þ</sup> <sup>0</sup>:2250 A � <sup>B</sup> <sup>þ</sup> <sup>0</sup>:550 A � <sup>C</sup>

� <sup>0</sup>:10655 B<sup>2</sup> � <sup>3</sup>:1172 C2 <sup>þ</sup> <sup>0</sup>:22650 A � <sup>B</sup> <sup>þ</sup> <sup>0</sup>:4883 A � <sup>C</sup>

(3)

(4)

YU is the response.

xi (1, 2 … k) is the coded levels of k numerical variables.


bij—interaction term.


Table 2. Process parameters and their levels.


Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity… DOI: http://dx.doi.org/10.5772/intechopen.89554

#### Table 3.

using Eqs. (1) and (2). Tables 2 and 3 show that the setup of the experiment was

The factorial portion of CCD is a full factorial design with all mixtures of the factors at three levels (+1, 0, �1) and composed of the eight-star points and six central points (coded level 0), which were the centre between the high and the low levels. The star points were at the face-centre of the cube portion that corresponds to an α value of 1 and this sort of design was usually known as the "face-centred CCD." The tests were conducted using stipulated conditions in keeping with the face-centred CCD with 20 experimental observations at three independent input

> bixi <sup>þ</sup><sup>X</sup> k

> > i¼1

Parameter �10 1 Load (N) 5 10 15 Time (min) 5 10 15 Sliding velocity(m/sec) 1.5 3 4.5

biix2

<sup>i</sup> <sup>þ</sup><sup>X</sup> k

j>1

bijxixj (2)

metalremoved ð Þ part ð Þ Timeofmachining

(1)

designed on the idea of the central composite design (CCD) technique.

Wear rate gð Þ¼ = min

Yu <sup>¼</sup> bo <sup>þ</sup><sup>X</sup>

k

i¼1

xi (1, 2 … k) is the coded levels of k numerical variables.

variables [15–18].

Figure 1.

Pin-on –disc wear experimental setup.

Aluminium Alloys and Composites

YU is the response.

b0—endless term. bi—linear term. bii—quadratic term. bij—interaction term.

Process parameters and their levels.

Table 2.

122

Design of experiment matrix and wear characteristics.

### 2.1 Design of experiment

In this study, a second-order polynomial was selected to develop empirical equations to represent responses (wear rate and COF) in terms of controllable variables such as normal load (A), time (B) and sliding velocity (C). The final response equations were developed for wear rate and COF using the experimental results.

This mathematical prototypical has been achieved to reproduce the independent, quadratic and interactive effects of some machining parameters on the machined for wear rate (WR) and coefficient of friction (COF). The empirical relationship for correlating the WR and COF the thought of dry sliding wear method parameters is obtained as follows:

$$\begin{aligned} \text{WR} \left( \text{g/min} \right) \times 10^{-5} &= \text{317.31} - \text{30.17 A} + \text{14.41 B} + \text{48.85 C} + \text{1.8673 A}^2 \\ &- 0.6327 \,\text{B}^2 - \text{3.919 C}^3 + \text{0.2250 A} \times \text{B} + \text{0.550 A} \times \text{C} \\ &- 0.050 \,\text{B} \times \text{C} \end{aligned} \tag{3}$$

$$\begin{array}{c} \text{COF } (\mu) \times 10^{-2} = 97.512 - 14.661 \text{ A} + 0.639 \text{ B} + 10.816 \text{ C} + 0.54745 \text{ A}^2 \\ \quad - 0.10655 \text{ B}^2 - 3.1172 \text{ C}^2 + 0.22650 \text{ A} \times \text{B} + 0.4883 \text{ A} \times \text{C} \\ \quad - 0.1550 \text{ B} \times \text{C} \end{array}$$

(4)

Eq. (3) and (4) develop empirical models based on the composite desirability optimisation technique. Figures 2 and 3 show that the experimental values and predicted values from the mathematical model are scattered on both sides and close

Source DF Adj SS Adj MS F-value P-value Model 9 1400.34 15.559 2051.03 0.000 Linear 3 490.29 163.429 2154.31 0.000 Square 3 535.42 178.475 2352.64 0.000 2-Way interaction 3 374.63 124.878 1646.13 0.000

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity…

Lack-of-fit 5 0.54 0.108 2.45 0.174

Total 19 1401.10 R2 = 99.95%

The normal percentage point of F-ratio dispersal for 95% confidence limit is 5.05 as shown in Tables 4 and 5. The F -values (1.27 and 2.45) aimed at deficiency of adequate are lesser than the normal value. Thus WR and COF the prototypically

After eliminating the non-significant terms in Eq. (3) and (4), the final response

From the developed mathematical Eqs. (5) and (6) to be used for the estimation of wear rate and coefficient of friction, the sensitivity equations are obtained by differentiating Eqs. (5) and (6) with respect to process parameters of load (A), time

� <sup>0</sup>:6327 B<sup>2</sup> � <sup>3</sup>:919 C<sup>2</sup> <sup>þ</sup> <sup>0</sup>:2250 A � <sup>B</sup> <sup>þ</sup> <sup>0</sup>:550 A � <sup>C</sup>

� <sup>0</sup>:10655 B<sup>2</sup> � <sup>3</sup>:1172 C2 <sup>þ</sup> <sup>0</sup>:22650 A � <sup>B</sup> <sup>þ</sup> <sup>0</sup>:4883 A � <sup>C</sup>

dA ¼ �30:<sup>17</sup> <sup>þ</sup> <sup>1</sup>:<sup>8673</sup> <sup>∗</sup> 2A <sup>þ</sup> <sup>0</sup>:2250 B <sup>þ</sup> <sup>0</sup>:550 C (7)

dA ¼ �14:<sup>661</sup> <sup>þ</sup> <sup>0</sup>:<sup>5475</sup> <sup>∗</sup> 2A <sup>þ</sup> <sup>0</sup>:2265 B <sup>þ</sup> <sup>0</sup>:4883 C (8)

dB <sup>¼</sup> <sup>0</sup>:<sup>639</sup> � <sup>0</sup>:<sup>1065</sup> <sup>∗</sup> 2 B <sup>þ</sup> <sup>0</sup>:2265 A � <sup>0</sup>:1550 C (10)

dB <sup>¼</sup> <sup>14</sup>:<sup>41</sup> � <sup>0</sup>:<sup>6327</sup> <sup>∗</sup> 2 B <sup>þ</sup> <sup>0</sup>:2250 A (9)

(5)

(6)

WR gð Þ� <sup>=</sup> min <sup>10</sup>�<sup>5</sup> <sup>¼</sup> <sup>317</sup>:<sup>31</sup> � <sup>30</sup>:17 A <sup>þ</sup> <sup>14</sup>:41 B <sup>þ</sup> <sup>48</sup>:85 C <sup>þ</sup> <sup>1</sup>:8673 A2

COF ð Þ� <sup>μ</sup> <sup>10</sup>�<sup>2</sup> <sup>¼</sup> <sup>97</sup>:<sup>512</sup> � <sup>14</sup>:661 A <sup>þ</sup> <sup>0</sup>:639 B <sup>þ</sup> <sup>10</sup>:816 C <sup>þ</sup> <sup>0</sup>:54745 A2

to 45° line, which further proves the adequacy of the model.

Error 10 0.76 0.076

DOI: http://dx.doi.org/10.5772/intechopen.89554

Pure error 5 0.22 0.044

equations for wear rate and coefficient of friction are given below.

� 0:1550 B � C

(B), and sliding velocity (C) as given below.

dWR

dWR

dCOF

dCOF

125

stand adequate.

Table 5.

3. Sensitivity analysis

Analysis of variance table for COF.

Figure 2. Standard probability plot for wear rate.

Figure 3. Standard probability plot for COF.


#### Table 4.

Analysis of variance table for wear rate.

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity… DOI: http://dx.doi.org/10.5772/intechopen.89554


#### Table 5.

Figure 2.

Figure 3.

Table 4.

124

Standard probability plot for COF.

Analysis of variance table for wear rate.

Source DF Adj SS Adj MS F-value P-value Model 9 61981.7 6886.86 1458.03 0.000 Linear 3 3875.2 1291.72 273.47 0.000 Square 3 6472.3 2157.45 456.76 0.000 2-Way interaction 3 390.4 130.12 27.55 0.000

Lack-of-fit 5 26.4 5.28 1.27 0.401

Total 19 62029.0 R2 = 99.92%

Error 10 47.2 4.72

Pure error 5 20.8 4.17

Standard probability plot for wear rate.

Aluminium Alloys and Composites

Analysis of variance table for COF.

Eq. (3) and (4) develop empirical models based on the composite desirability optimisation technique. Figures 2 and 3 show that the experimental values and predicted values from the mathematical model are scattered on both sides and close to 45° line, which further proves the adequacy of the model.

The normal percentage point of F-ratio dispersal for 95% confidence limit is 5.05 as shown in Tables 4 and 5. The F -values (1.27 and 2.45) aimed at deficiency of adequate are lesser than the normal value. Thus WR and COF the prototypically stand adequate.

#### 3. Sensitivity analysis

After eliminating the non-significant terms in Eq. (3) and (4), the final response equations for wear rate and coefficient of friction are given below.

$$\begin{aligned} \text{WR} \left( \text{g/min} \right) \times 10^{-5} &= 317.31 - 30.17 \text{ A} + 14.41 \text{ B} + 48.85 \text{ C} + 1.8673 \text{ A}^2 \\ &- 0.6327 \text{ B}^2 - 3.919 \text{ C}^2 + 0.2250 \text{ A} \times \text{B} + 0.550 \text{ A} \times \text{C} \\ &\text{(5)} \\ \text{COF } \left( \mu \right) \times 10^{-2} &= 97.512 - 14.661 \text{ A} + 0.639 \text{ B} + 10.816 \text{ C} + 0.54745 \text{ A}^2 \\ &- 0.10655 \text{ B}^2 - 3.1172 \text{ C}^2 + 0.22650 \text{ A} \times \text{B} + 0.4883 \text{ A} \times \text{C} \\ &- 0.1550 \text{ B} \times \text{C} \end{aligned} (5)$$

$$(6)$$

From the developed mathematical Eqs. (5) and (6) to be used for the estimation of wear rate and coefficient of friction, the sensitivity equations are obtained by differentiating Eqs. (5) and (6) with respect to process parameters of load (A), time (B), and sliding velocity (C) as given below.

$$\frac{\text{dWR}}{\text{dA}} = -30.17 + 1.8673 \ast 2\text{A} + 0.2250 \text{ B} + 0.550 \text{ C} \tag{7}$$

$$\frac{\text{dCOF}}{\text{dA}} = -14.661 + 0.5475 \ast 2\text{A} + 0.2265\text{ B} + 0.4883\text{ C}\tag{8}$$

$$\frac{\text{dWR}}{\text{dB}} = 14.41 - 0.6327 \ast 2 \text{ B} + 0.2250 \text{ A} \tag{9}$$

$$\frac{\text{dCOF}}{\text{dB}} = 0.639 - 0.1065 \ast 2 \text{ B} + 0.2265 \text{ A} - 0.1550 \text{ C} \tag{10}$$

$$\frac{\text{dWR}}{\text{dC}} = 48.85 - 3.919 \ast 2 \text{ C} + 0.550 \text{ A} \tag{11}$$

$$\frac{\text{dCOF}}{\text{dC}} = 10.816 - 3.1172 \ast 2 \text{ C} + 0.4883 \text{ A} - 0.1550 \text{ B} \tag{12}$$

Results of WR and COF for sensitivities of load, time, sliding velocity on WR and COF are presented in Tables 6 and 7.


#### Table 6.

Wear rate for sensitivities of process parameters.


Table 7. COF for sensitivities of process parameters.

#### 4. Results and discussion

#### 4.1 Effects of load and time on wear rate

The influence of response surface plot for wear rate is shown in Figure 4. Wear rate increases with the increase of applied load, but the time also increases when wear rate increases. The frictional heat can result in excessive deterioration of the composite transfer from materials to the disc counterpart. The increase of applied load inspiration to disproportionate damage of the composite and thus results shows an increase in wear rate.

Figure 5.

Figure 6.

127

Sensitivity analysis results of load.

Figure 4.

Influence of time and sliding velocity on COF.

Effects of load and time on the wear rate.

DOI: http://dx.doi.org/10.5772/intechopen.89554

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity…

#### 4.2 Effect of time and sliding velocity on COF

In Figure 5 shows that the impression residues, the disc considerable develops worn and guarded material derives currently contact with the pin, scratching of the disc shallow origins the ploughing and later friction coefficient increase with a period of impression. After the period of impression the expansion of irregularity and alternative constraints might influence a particular solid value. Therefore, the standards of friction coefficient endure endless for the repose. This can be described by the mechanism that with the wear occurrence below process, supplementary fly ash particles are made with the effect of sliding velocity, consequently, the being of the fly ash disturbs at the connected area and intersection asset and so subsidises directly to the complex level of friction coefficient.

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity… DOI: http://dx.doi.org/10.5772/intechopen.89554

Figure 4. Effects of load and time on the wear rate.

dWR

dCOF

Aluminium Alloys and Composites

4. Results and discussion

COF for sensitivities of process parameters.

Table 6.

Table 7.

126

an increase in wear rate.

4.1 Effects of load and time on wear rate

4.2 Effect of time and sliding velocity on COF

directly to the complex level of friction coefficient.

and COF are presented in Tables 6 and 7.

Wear rate for sensitivities of process parameters.

dC <sup>¼</sup> <sup>48</sup>:<sup>85</sup> � <sup>3</sup>:<sup>919</sup> <sup>∗</sup> 2 C <sup>þ</sup> <sup>0</sup>:550 A (11)

dWR dB

dCOF dB

dWR dC

dCOF dC

dC <sup>¼</sup> <sup>10</sup>:<sup>816</sup> � <sup>3</sup>:<sup>1172</sup> <sup>∗</sup> 2 C <sup>þ</sup> <sup>0</sup>:4883 A � <sup>0</sup>:1550 B (12)

Results of WR and COF for sensitivities of load, time, sliding velocity on WR

5 �34.6796 15.4504 56.1380 10 �30.1700 14.4100 48.8500 15 �25.6604 13.3696 41.5620

5 �16.4708 0.7805 16.7171 10 �14.6610 0.6390 10.8160 15 �12.8512 0.4975 4.9149

Load (N) B = 10 min, C = 3 m/sec dWR dA

Load (N) B = 10 min, C = 3 m/sec dCOF dA

The influence of response surface plot for wear rate is shown in Figure 4. Wear rate increases with the increase of applied load, but the time also increases when wear rate increases. The frictional heat can result in excessive deterioration of the composite transfer from materials to the disc counterpart. The increase of applied load inspiration to disproportionate damage of the composite and thus results shows

In Figure 5 shows that the impression residues, the disc considerable develops worn and guarded material derives currently contact with the pin, scratching of the disc shallow origins the ploughing and later friction coefficient increase with a period of impression. After the period of impression the expansion of irregularity and alternative constraints might influence a particular solid value. Therefore, the standards of friction coefficient endure endless for the repose. This can be described by the mechanism that with the wear occurrence below process, supplementary fly ash particles are made with the effect of sliding velocity, consequently, the being of the fly ash disturbs at the connected area and intersection asset and so subsidises

Figure 5. Influence of time and sliding velocity on COF.

Figure 6. Sensitivity analysis results of load.

• Wear rate increases as the sliding speed increases due to work hardening of the surface and crushing of the fly ash particles. The WR rises with the raise of the normal load. It is attributed to excessive damage to the composite. Wear rate increases dependably with regular interval due to deduction of the majority of

Wear Behaviour of Aluminium Alloy 8011 with 4% Fly Ash Composites by Using Sensitivity…

coefficient of friction normal load decreases then before rises. The assessment of friction coefficient is raised nearly linear up to 10 minutes of impression and

• Response surface methodology has the potential for more stringent sensitivity

• COF growths through an increase in sliding speed, whereas increase in

analysis and may be used for optimal parameter estimation for other

Department of Mechanical Engineering, K.S.R. College of Engineering,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: magibalan42@gmail.com

the materials when exposed to sliding force.

subsequently rests to constant.

DOI: http://dx.doi.org/10.5772/intechopen.89554

mathematical models.

Author details

129

Subramaniam Magibalan

Tiruchengode, Tamil Nadu, India

provided the original work is properly cited.

Figure 7. Sensitivity analysis results of time.

Figure 8.

Sensitivity analysis results of sliding velocity.

#### 4.3 Sensitivity analysis

Figure 6 highlights the sensitivity analysis of load for WR and COF. Sensitivity analysis of load for WR and COF is negative for lower load values and it is negative as the value of sensitivity of load increases. Figure 7 illustrates the time sensitivity. Time sensitivity for WR is positive for lower load values and it is positive as the value of sensitivity of load increases and COF remains unchanged. Figure 8 reveals sliding velocity sensitivity. Sliding velocity sensitivity for WR is positive and increases with increase in load. COF is positive with increase in load.
