**Abstract**

Information contributes to the improvement of decision-making, process improvement, error detection, and prevention. The new requirements of the coming Industry 4.0 will make these new information technologies help in the improvement and decision-making of industrial processes. In case of the welding processes, several techniques have been used. Welding processes can be analyzed as a stochastic system with several inputs and outputs. This allows a study with a data analysis perspective. Data mining processes, machine learning, deep learning, and reinforcement learning techniques have had good results in the analysis and control of systems as complex as the welding process. The increase of information acquisition and information quality by sensors developed at present, allows a large volume of data that benefits the analysis of these techniques. This research aims to make a bibliographic analysis of the techniques used in the welding area, the advantages that these new techniques can provide, and how some researchers are already using them. The chapter is organized according to some stages of the data mining process. This was defined with the objective of highlighting evolution and potential for each stage for welding processes.

**Keywords:** data mining, deep learning, welding process, machine learning

## **1. Introduction**

One of the most important processes of joining metals is welding process, like the one that appears in [1]. It is used in simple structures fabrication, nuclear and petroleum industries, as well as chemical components.

In a typical fusion welding process of metals, such as resistance welding, arc welding, electron beam welding, laser welding, a heat source is applied locally to the interfaces of the two metals to be joined. The interface can be metals'surfaces, where faces of each other are joined by a nugget (e.g., spot or resistance welding). In arc welding, the interface will be the weld seam. However, complex physical phenomena and processes occur due to the heating/melting and cooling/solidifying. This may produce adverse effects on weld properties and base metal properties [2]. In order to reduce adverse effects and obtain desired results, many studies have been developed to monitor, predict, or control welding processes. All these studies

are based on the optimal welding parameters' adjustment, but all of these are adjustable.

All adjustable welding parameters, such as current or current waveform, heat input, wire feed speed, travel speed, and arc voltage, may be used as system inputs and be designed to assure the required outputs. For that reason and the interrelations-parameters complexity, welding process can be analyzed like a stochastic system, which has input and output parameters and several disturbances [2]. Chen's article [3] was related to the need to improve the information acquired from these welding parameters and identify characteristics in order to improve and control the welding process results. Chen defined new objectives of modern welding manufacturing technology to show the way for better welding processes. It exposes some problems of the intelligentized welding manufacturing technology (IWMT), which are shown in **Figure 1**.

Other science areas present the potential to solve these problems. Computer science areas have had great results with new technique applications of data analysis, learning models, and intelligent control. Data analysis objective indicates nontrivial features on a large amount of data. Due to the increase and complexity of data, more efficient data analysis techniques have been developed. Welding process can be analyzed with this point of view. So, the welding process analysis with new techniques is nothing more than a continuity in the development of welding analysis processes. This interdisciplinarity is one of the necessary contributions proclaimed by the so-called Industry 4.0, like the one shown in [4–6].

The fourth industrial revolution refers to the next manufacturing generation, where automation technology will be improved by self-optimization and intelligent feedback [7]. For this reason, the application of the most recent data analysis techniques and processes can contribute to a better control and monitoring of welding processes. These techniques can be joined in machine learning techniques [8–12], data mining process [13–17], and control process [18–20]. The interrelation of these areas and their origins are presented in **Figure 2**. Machine learning is a growing area in computer science, with far-reaching applications, for data analysis [21]. Machine learning uses computer theory and statistics for building mathematical models with the goal of making inference from a sample [22]. One branch in machine learning with fast growing is deep learning. These methods are an essential part of the research on speech recognition in the state of the art [23], image recognition [24–26], object detection [27, 28], videos [29, 30], and sound [31, 32] analysis. Interesting patterns come out from such a machine learning techniques. One important process is data mining. Data mining puts strong emphasis on different aspects, like efficiency, effectiveness, and validity of process [33]. Data mining

processes define several stages and methodologies to achieve these objectives, as exposed by Marbán in [34]. An important objective of data analysis is to reveal and indicate diverse, nontrivial features in data. For this reason, welding process can be

*Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art*

welding process, growth is almost imperceptible, as appearing in **Figure 4**.

A search conducted in the Web of Science from 2011 to October 3, 2018, shows the growing trend of these new data analysis techniques and processes in welding process researches **Figure 3**, but when comparing with the investigations on models

analyzed with this point of view.

*Cited per year on welding (Web of Science [35]).*

*Origin diagram of the new data analysis techniques.*

*DOI: http://dx.doi.org/10.5772/intechopen.91184*

**Figure 2.**

**Figure 3.**

**29**

**Figure 1.** *Some technical problems in IWMT [3].*

*Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art DOI: http://dx.doi.org/10.5772/intechopen.91184*

**Figure 2.** *Origin diagram of the new data analysis techniques.*

processes define several stages and methodologies to achieve these objectives, as exposed by Marbán in [34]. An important objective of data analysis is to reveal and indicate diverse, nontrivial features in data. For this reason, welding process can be analyzed with this point of view.

A search conducted in the Web of Science from 2011 to October 3, 2018, shows the growing trend of these new data analysis techniques and processes in welding process researches **Figure 3**, but when comparing with the investigations on models welding process, growth is almost imperceptible, as appearing in **Figure 4**.

**Figure 3.** *Cited per year on welding (Web of Science [35]).*

are based on the optimal welding parameters' adjustment, but all of these are

and be designed to assure the required outputs. For that reason and the

All adjustable welding parameters, such as current or current waveform, heat input, wire feed speed, travel speed, and arc voltage, may be used as system inputs

interrelations-parameters complexity, welding process can be analyzed like a stochastic system, which has input and output parameters and several disturbances [2]. Chen's article [3] was related to the need to improve the information acquired from these welding parameters and identify characteristics in order to improve and

welding manufacturing technology to show the way for better welding processes. It exposes some problems of the intelligentized welding manufacturing technology

Other science areas present the potential to solve these problems. Computer science areas have had great results with new technique applications of data analysis, learning models, and intelligent control. Data analysis objective indicates nontrivial features on a large amount of data. Due to the increase and complexity of data, more efficient data analysis techniques have been developed. Welding process can be analyzed with this point of view. So, the welding process analysis with new techniques is nothing more than a continuity in the development of welding analy-

The fourth industrial revolution refers to the next manufacturing generation, where automation technology will be improved by self-optimization and intelligent feedback [7]. For this reason, the application of the most recent data analysis techniques and processes can contribute to a better control and monitoring of welding processes. These techniques can be joined in machine learning techniques [8–12], data mining process [13–17], and control process [18–20]. The interrelation of these areas and their origins are presented in **Figure 2**. Machine learning is a growing area in computer science, with far-reaching applications, for data analysis [21]. Machine learning uses computer theory and statistics for building mathematical models with the goal of making inference from a sample [22]. One branch in machine learning with fast growing is deep learning. These methods are an essential

control the welding process results. Chen defined new objectives of modern

sis processes. This interdisciplinarity is one of the necessary contributions proclaimed by the so-called Industry 4.0, like the one shown in [4–6].

part of the research on speech recognition in the state of the art [23], image recognition [24–26], object detection [27, 28], videos [29, 30], and sound [31, 32] analysis. Interesting patterns come out from such a machine learning techniques. One important process is data mining. Data mining puts strong emphasis on different aspects, like efficiency, effectiveness, and validity of process [33]. Data mining

(IWMT), which are shown in **Figure 1**.

adjustable.

*Welding - Modern Topics*

**Figure 1.**

**28**

*Some technical problems in IWMT [3].*

**Figure 4.** *Cited per year on welding (Web of Science [35]).*

These demonstrate the need for this review to show these techniques, the advantages in their applications, and the increasing trend of their utilization. This review can be resumed in following stages:

**2.1 Arc welding**

*Welding processes group.*

**Table 1.**

**2.2 Resistance welding**

**31**

The group arc welding is characterized with electric arc. The electric arc is the heat source most commonly used in fusion welding of metallic materials. The welding arc comprises a relatively small region of space characterized by high temperatures (similar to or even higher than the sun's surface), strong generation of light and ultraviolet radiation, intense flow of matter, and large gradients of physical properties. It has an adequate concentration of energy for localized base metal fusion, ease of control, low relative cost of equipment, and an acceptable level of health risks to its operators. The study of the arc is of special interest in areas such as astrophysics and the electrical and nuclear industries [37]. The electric arc generates a complex interrelation of thermal, electrical, and magnetic parameters. These are hampering much of their studies based on definite theoretical formulations. Despite many studies, the electric arc is quite complex and the knowledge so

Girth welds

Large scale RSW (LSRSW)

Resistance welding is the joining of metals by applying pressure and passing current for a length of time through the metal area that is to be joined. Its principal advantage is no other materials are needed to create the bond; this reason makes this process extremely cost effective. Resistance welding is applied in a wide range of automotive, aerospace, and industrial applications. Among the main parameters are welding time, welding force, contact resistance, materials properties [1]. Resistance spot welding, like all resistance welding processes, creates welds using heat generated by resistance to the flow of welding current between the faying surfaces, as well as force to push the workpieces together, applied over a defined period of time. Resistance spot welding uses the electrode face geometries to focus the welding current at the desired location, and apply force to the workpieces. Once sufficient resistance is generated, the materials set down and combine, and a weld nugget is formed [36]. The process is fast and effective, and it is also complicated due to complex interactions between electrical, mechanical, thermal, and metallurgical processes. The heat generation in RSW is due to the resistance of the parts

far allows a partial understanding of the phenomenon [1].

**Group Welding process**

*DOI: http://dx.doi.org/10.5772/intechopen.91184*

Arc welding Gas metal arc welding (GMAW)

*Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art*

Resistance welding Resistance spot welding (RSW)

Other welding processes Laser beam welding (LBW)

Gas tungsten arc welding (GTAW) Plasma arc welding (PAW)

Shielded metal arc welding (SMAW) Submerged arc welding (SAW)

Variable polarity plasma arc welding (VPPAW) Rotating arc narrow gap MAG welding (RANGMW)


These stages has a close relationship with data mining processes as a sample [34].
