**Abstract**

The online measurement of principal magnitudes in welding processes is important to close the control loop and meet the project requirements. But, it is difficult because of the adverse environmental conditions that exist near the weld pool. Some conventional measurement techniques are used, but under these conditions, indirect sensing techniques are a better option. Sensor fusion algorithms and indirect sensing techniques allow estimate magnitudes that are impossible to measure directly. Sensor fusion is used to describe the static and dynamic behavior of process variables and is based on several areas of knowledge, such as statistics and artificial intelligence. By combining different sensing technologies to take advantage of each one, it is possible to obtain better sensing results. In this chapter selected sensing techniques and estimation algorithms used online for collecting values on the welding process are shown. Special attention is given to sensor fusion techniques. Some real applications and innovative research results are discussed.

**Keywords:** estimation, online measurement, sensor fusion, welding

## **1. Introduction**

The welding process is used by many manufacture companies in a wider range of applications. Many studies have been carried out to improve the quality and to reduce the cost of welded components. Part of the overheads is employed in the final inspection, which begins with a visual inspection, followed by destructive and nondestructive testing techniques. In addition to cost raises, a final inspection is conducted when the part is finished only. When a defect occurs during welding, it can be reflected in the physical phenomena involved: magnetic field, electric field, temperature, sound pressure, radiation emission, and others. Thus, if a sensor monitors one of these phenomena, it is possible to build a system to monitor weld parameters and quality.

For years, much has been done to predict problems in welding to make it a stable process capable of making union parts with minimal human interference. Despite various sensors used in welding processes, there is still no effective option able to identify, directly, the weld bead characteristics obtained during the process. This is a limiting factor in the process control because weld bead characteristics can only be determined after the completion of welding through testing (destructive or not) when no control action can be taken.

In the last decade, measurements of multiple sensors are used to estimate some geometric parameters of the weld bead, such as the weld penetration,

reinforcement, and width, and to classify the process stability. These estimations can be made online<sup>1</sup> and can be used to control the weld bead geometry formation. More recently, research has shown the possibility of estimating some microstructural characteristics and thermally affected zone dimensions, as shown in [1]. These estimations can be used to limit the control actions so as not to affect the desired characteristics of the weld bead. These techniques do not eliminate destructive testing but greatly reduce its use and prevent the defect formation or loss of characteristics.

With continuing advancements in digital and sensor technology, new methods with relatively high accuracy and quick response time for the identification of perturbations during the welding process have become possible. Arc position, part placement variations, surface contaminations, and joint penetration are key variables that must be controlled to ensure satisfactory weld production [4]. The techniques related to welding process optimization are based on experimental methodologies. These techniques are strongly related to experimental tests and seek to establish relations between the welding parameters and welding bead geometry. Researches related to adaptive systems for welding seek the improvement of welding bead geometry with direct (if based on monitoring sensors) or indirect monitoring techniques. The indirect monitoring systems are the more used, looking to link elements such as welding pool vibrations, superficial temperature distribution, and acoustic emissions to size, geometry, or welding pool depth [5]. The most used approaches in welding control are infrared monitoring, acoustic monitoring, welding pool vibrations, and welding pool depression monitoring as shown in [6]. The literature analysis made in [7] shows a similar conclusion, but adds vision

techniques with the same level of use with infrared measurement.

to be introduced in industrial applications.

*Online Measurements in Welding Processes DOI: http://dx.doi.org/10.5772/intechopen.91771*

are discussed.

**79**

The majority of modeling methods used in the past were based on the physics and chemical characteristics of the process components. This method needs great knowledge about welding processes, and, because of its complexity, these magnitudes are difficult to obtain and keep constant throughout the process, causing inaccuracies in the models. With the development of information processing capabilities, black box modeling methods were successfully used, as such the statistics and probabilities, but needs very complex models because of the nonlinearity of the welding process and the correlation between variables. Nowadays, the black box methods based on intelligent artificial algorithms are predominant as shown in [7], where 29% of models analyzed were obtained using intelligent artificial algorithms, 18% used image processing (which can include intelligent artificial algorithms such as deep learning methods), 12% used statistical methods, and only 2% used physics and chemical characteristics. Also, recently, big data and data mining algorithms are

This chapter focuses on the online measurement of main magnitudes in welding processes to close the control loop and allow the welding power source and robot parameters to be adjusted. These actions make it easier to meet the project requirements, reduce the cost of welding production, and increase productivity by reducing the number of parts rejected in final quality inspection. The next sections discuss the sensing and analysis techniques used to measure or estimate important variables in welding processes, with emphasis on the conventional gas metal arc welding (GMAW) process. A summary of its evolution is

also shown, and some examples of algorithms to measure data processing

Also, a novel modeling method, based on a sensor fusion algorithm, is shown. This method uses dynamic information of welding processes to improve the model response, rather than relying on static models used in research found in the literature. The method uses arc welding measurements and thermographic information to estimate the weld bead penetration. The algorithm obtains the thermographic features and supplies information about the amount and spatial distribution of the energy in the workpiece, minimizing the errors when multiple inflection points are found because it does not use the second derivative for the calculation of thermographic width. Besides, volume calculations are performed using the actual thermographic curve instead of the ideal Gaussian curve used in most research as an approximation value and using more complex equations. This approach uses only addition operations, simplifies calculations, and improves model accuracy, allowing

In the welding processes, many variables can be used to control the geometry and quality of the final product. For this, other variables need to be measured or estimated. Some variables can be measured or modified online, directly in the welding power source, such as welding voltage and welding electric current intensity. Others can be measured using noncontact sensing methods, such as width and reinforcement of weld bead, drop volume, and electrical stick-out. But several variables, necessary to close the control loops, are difficult to measure online. This difficulty is due to the extreme conditions in the arc zone, because the electric arc is a powerful radiation emitter, in a long range of the frequency spectrum, including high temperature and visible light, generating steam and droplets of molten metal, coming from the electrode and the base metal. For these latter cases are necessary estimation methods based on models of welding processes that may include the power source and the robotic system used to move the torch or piece.

For the automation and control of complex manufacturing systems, a great deal of progress came up in the last decade, for precision and online documentation (bases for the quality control). With the advent of electrically driven mechanical manipulators and later the whole, relatively new, multidisciplinary mechatronics engineering, the need for information acquisition has increased. The acquisition is, in many cases, distributed through the system, with strong interaction between the robot and its environment. The design objective is to attain flexible and lean production. The requirement of real-time processing of data from multi-sensor systems with robustness, in an industrial environment, shows the need for new concepts on system integration.

Technology advancements seek to meet the demands for quality and performance through product improvements and cost reductions. An important area of research is the optimization of applications related to welding and the resultant cost reduction. The use of nondestructive tests and defect repair are slow processes. To avoid this, online monitoring and control of the welding process can favor the correction and reduction of defects before the solidification of the melted/fused metal, reducing the production time and cost [2].

Developments in microelectronics have led to rapid advances in welding power sources. With the use of fast microelectronic circuits, the speed of welding process control and welding parameter adjustment has been increased tremendously, and dynamic control over the arc and molten metal transfer have become possible. Research and development carried out by manufacturers of welding power sources focus on rapid optimal control of the welding parameters during welding. Modern welding sources are equipped with special control functions of arc and molten metal transfer, focusing on two basic areas: The first area of focus is on welding thin metal sheets (0.53 mm), and the second is on high-productivity thick metal sheet welding (over 5 mm) [3].

<sup>1</sup> When the measurement or control action is made inside the process flow and during the process execution, this task is classified as online. When the measurement or control action is made outside the process flow and before or after the process execution, this task is classified as offline.

#### *Online Measurements in Welding Processes DOI: http://dx.doi.org/10.5772/intechopen.91771*

reinforcement, and width, and to classify the process stability. These estimations can be made online<sup>1</sup> and can be used to control the weld bead geometry formation. More recently, research has shown the possibility of estimating some microstructural characteristics and thermally affected zone dimensions, as shown in [1]. These estimations can be used to limit the control actions so as not to affect the desired characteristics of the weld bead. These techniques do not eliminate destructive testing but greatly reduce its use and prevent the defect formation or loss of

In the welding processes, many variables can be used to control the geometry and quality of the final product. For this, other variables need to be measured or estimated. Some variables can be measured or modified online, directly in the welding power source, such as welding voltage and welding electric current intensity. Others can be measured using noncontact sensing methods, such as width and reinforcement of weld bead, drop volume, and electrical stick-out. But several variables, necessary to close the control loops, are difficult to measure online. This difficulty is due to the extreme conditions in the arc zone, because the electric arc is a powerful radiation emitter, in a long range of the frequency spectrum, including high temperature and visible light, generating steam and droplets of molten metal, coming from the electrode and the base metal. For these latter cases are necessary estimation methods based on models of welding processes that may include the

For the automation and control of complex manufacturing systems, a great deal

of progress came up in the last decade, for precision and online documentation (bases for the quality control). With the advent of electrically driven mechanical manipulators and later the whole, relatively new, multidisciplinary mechatronics engineering, the need for information acquisition has increased. The acquisition is, in many cases, distributed through the system, with strong interaction between the robot and its environment. The design objective is to attain flexible and lean production. The requirement of real-time processing of data from multi-sensor systems with robustness, in an industrial environment, shows the need for new concepts on

Technology advancements seek to meet the demands for quality and performance through product improvements and cost reductions. An important area of research is the optimization of applications related to welding and the resultant cost reduction. The use of nondestructive tests and defect repair are slow processes. To avoid this, online monitoring and control of the welding process can favor the correction and reduction of defects before the solidification of the melted/fused

Developments in microelectronics have led to rapid advances in welding power sources. With the use of fast microelectronic circuits, the speed of welding process control and welding parameter adjustment has been increased tremendously, and dynamic control over the arc and molten metal transfer have become possible. Research and development carried out by manufacturers of welding power sources focus on rapid optimal control of the welding parameters during welding. Modern welding sources are equipped with special control functions of arc and molten metal transfer, focusing on two basic areas: The first area of focus is on welding thin metal sheets (0.53 mm), and the second is on high-productivity thick metal sheet welding

<sup>1</sup> When the measurement or control action is made inside the process flow and during the process execution, this task is classified as online. When the measurement or control action is made outside the

process flow and before or after the process execution, this task is classified as offline.

power source and the robotic system used to move the torch or piece.

characteristics.

*Welding - Modern Topics*

system integration.

(over 5 mm) [3].

**78**

metal, reducing the production time and cost [2].

With continuing advancements in digital and sensor technology, new methods with relatively high accuracy and quick response time for the identification of perturbations during the welding process have become possible. Arc position, part placement variations, surface contaminations, and joint penetration are key variables that must be controlled to ensure satisfactory weld production [4]. The techniques related to welding process optimization are based on experimental methodologies. These techniques are strongly related to experimental tests and seek to establish relations between the welding parameters and welding bead geometry.

Researches related to adaptive systems for welding seek the improvement of welding bead geometry with direct (if based on monitoring sensors) or indirect monitoring techniques. The indirect monitoring systems are the more used, looking to link elements such as welding pool vibrations, superficial temperature distribution, and acoustic emissions to size, geometry, or welding pool depth [5]. The most used approaches in welding control are infrared monitoring, acoustic monitoring, welding pool vibrations, and welding pool depression monitoring as shown in [6]. The literature analysis made in [7] shows a similar conclusion, but adds vision techniques with the same level of use with infrared measurement.

The majority of modeling methods used in the past were based on the physics and chemical characteristics of the process components. This method needs great knowledge about welding processes, and, because of its complexity, these magnitudes are difficult to obtain and keep constant throughout the process, causing inaccuracies in the models. With the development of information processing capabilities, black box modeling methods were successfully used, as such the statistics and probabilities, but needs very complex models because of the nonlinearity of the welding process and the correlation between variables. Nowadays, the black box methods based on intelligent artificial algorithms are predominant as shown in [7], where 29% of models analyzed were obtained using intelligent artificial algorithms, 18% used image processing (which can include intelligent artificial algorithms such as deep learning methods), 12% used statistical methods, and only 2% used physics and chemical characteristics. Also, recently, big data and data mining algorithms are to be introduced in industrial applications.

This chapter focuses on the online measurement of main magnitudes in welding processes to close the control loop and allow the welding power source and robot parameters to be adjusted. These actions make it easier to meet the project requirements, reduce the cost of welding production, and increase productivity by reducing the number of parts rejected in final quality inspection. The next sections discuss the sensing and analysis techniques used to measure or estimate important variables in welding processes, with emphasis on the conventional gas metal arc welding (GMAW) process. A summary of its evolution is also shown, and some examples of algorithms to measure data processing are discussed.

Also, a novel modeling method, based on a sensor fusion algorithm, is shown. This method uses dynamic information of welding processes to improve the model response, rather than relying on static models used in research found in the literature. The method uses arc welding measurements and thermographic information to estimate the weld bead penetration. The algorithm obtains the thermographic features and supplies information about the amount and spatial distribution of the energy in the workpiece, minimizing the errors when multiple inflection points are found because it does not use the second derivative for the calculation of thermographic width. Besides, volume calculations are performed using the actual thermographic curve instead of the ideal Gaussian curve used in most research as an approximation value and using more complex equations. This approach uses only addition operations, simplifies calculations, and improves model accuracy, allowing its implementation in an embedded device. The actual application area is automatic control, arc welding, and sensor fusion research.
