Open access peer-reviewed chapter

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

Written By

Rogfel Thompson Martinez and Sadek Crisóstomo Absi Alfaro

Submitted: August 26th, 2019 Reviewed: January 14th, 2020 Published: March 10th, 2020

DOI: 10.5772/intechopen.91184

From the Edited Volume


Edited by Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek

Chapter metrics overview

1,046 Chapter Downloads

View Full Metrics


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.


  • 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.

Figure 1.

Some technical problems in IWMT [3].

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, 5, 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, 9, 10, 11, 12], data mining process [13, 14, 15, 16, 17], and control process [18, 19, 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, 25, 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 analyzed with this point of view.

Figure 2.

Origin diagram of the new data analysis techniques.

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]).

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:

  1. Welding process—understanding of welding processes being analyzed.

  2. Sensors—analysis of some principal sensors in welding process.

  3. Data processing—analysis of technique to transform sensors information to welding process dataset.

  4. Modeling welding process—analysis of some modeling techniques in welding process.

  5. Intelligent control of welding process—analysis of some intelligent control techniques in welding process.

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


2. Welding process

American Welding Society (AWS) definition for a welding process is:

a materials joining process which produces coalescence of materials by heating them to suitable temperatures with or without the application of pressure or by the application of pressure alone and with or without the use of filler material” [36].

AWS defines groups of welding techniques depending on the energy transfer mode. The processes analyzed in this chapter are grouped as shown in Table 1.

GroupWelding process
Arc weldingGas metal arc welding (GMAW)
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)
Girth welds
Resistance weldingResistance spot welding (RSW)
Large scale RSW (LSRSW)
Other welding processesLaser beam welding (LBW)

Table 1.

Welding processes group.

These groups present different parameters and characteristics that were analyzed in the articles presented in this chapter.

2.1 Arc welding

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

2.2 Resistance welding

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 being welded to the flow of a localized electric current, based on Joule’s law. The quality of the joint in RSW is influenced by the welding parameters. These parameters mainly include welding current, welding time, electrode force, and electrode geometry [38]. Large scale resistance spot welding (LSRSW), as mentioned in Table 1, is generally adopted in the automotive industry. It is an automotive structure that includes thousands of spot welds. It presents the same parameters and complexity as RSW; only the parameters related and influenced by its scalability are increased [39].

2.3 Other welding processes

In this group, AWS presents various welding processes. Laser welding is the only one belonging to this group, which is found in the analyzed articles.

Laser beam welding is one of the most technically advanced welding processes. Laser welding is in general a keyhole fusion welding technique which is achieved with the very high power density obtained by focusing a beam of laser light to a very fine spot [40]. This light ray heats metals up quickly so that the two pieces fuse together into one unit. The light beam is very small and focused, so the metal weld also cools very quickly. Laser welding operates in two fundamentally different modes: conduction limited welding and keyhole welding. The mode in which the laser beam will interact with the material is welding; it will depend on the power density of the focused laser spot on the work piece [41].

Other parameters that are present in these processes are those of final welding geometry, which behave differently in different processes and under different conditions. The parameters of the respective sources generate their influence on the final result of each welding process.

Welding is a complex process, so it requires more intelligent techniques in its analysis, monitoring, and production quality improvement. The use of sensors allows the acquisition of process parameters. The new artificial intelligence techniques will allow a better study, modeling, and control of these processes.


3. Sensors

Several sensors have been applied in the welding process for monitoring. The weld bead and the weld-pool indirect sensing technologies can be classified like exposed in [42] and in Figure 5.

Figure 5.

Some indirect monitoring technologies in welding process [42].

Infrared vision techniques have been widely applied in the welding process [43, 44, 45, 46, 47, 48, 49, 50]. One of the problems of this technique is that the environment where it is applied can interfere in the precision of the data obtained from a process. This may be due to the own heat emission of the technologies utilized.

3.1 Sound sensor

Sound may indicate conditions that generate weld defects. Acoustic information plays a relevant role for expert welders, as described in [51]. Sound signature produced by GMAW contains information about arc column behavior, the molten metal, and the metal transfer mode. High-speed data acquisition and computer-aided analysis of sound signature may indicate conditions that generate weld defects [52, 53]. Di Wu, in 2016 [54], tried to monitor penetration and keyhole with acoustic signals and image analysis. Lv et al. [55], proposed a recognition model to analyze the relationship between penetration state and arc sound. In 2017, Lv et al. [56] again presented a welding quality control in pulse gas tungsten arc welding (P-GTAW). The welding acoustic signal was used to analyze the design of an automated welding penetration control system.

In welding, it is easy to capture sound, but it is very difficult to analyze the noises and differences of intensities that are sometimes generated. This is not a problem to sound deep learning technique like present [32, 57]. To understand the welding sound analysis with deep learning techniques, it is necessary make an image arc correlation to know what happens in welding arc.

3.2 Vision sensor

Vision sensor is largely utilized in welding process to analyze weld-pool process [58, 59], arc-welding process [60, 61], and weld bead geometry [62, 63]. The more light generated by arc can be difficult for the image obtention. Some techniques are utilized. One of them was utilized by Chen in 2010 [64].

He made monitoring and control of the hybrid laser-gas metal arc welding process with an economical sensor system, and a coaxial vision system, which was integrated from a relatively inexpensive industrial vision system and a personal computer (PC). Another visualization technique is Shadowgraphy, applied in Esdras Ramos investigation, in 2013 [65, 66]. This is based on process shadow arc with laser source.

In [60], a laser illumination was utilized. To reduce the arc light, a narrow band interference filter was applied. For precise measurements, an image-analysis technique was used. This technique can be used to obtain high quality images but only it can be used in processes without material transfer.

Chen et al. [67] utilized a visual double-sided sensing system. In one frame, the weld-pool geometry parameters in GTAW process were determined.

With high speed illumination laser in [68], great quality images are obtained. This technique is more recent one but it needs a laser with more potentiality than Shadowgraphy technique. This technique is more expensive too.


4. Data processing

Some papers define their own image processing technologies, like Hong Yue in 2009 [69], where the weld image processing adopts the classic techniques such as Laplacian, Gaussian, neighborhood mean filters, and threshold segmentation. Yanling Xu, in 2014 [70], proposed the Canny edge detection algorithm for detecting edges and extracting pool and seam characteristic parameters. Qian-Qian Wu in [71] researched to find out the optimal algorithm to filter. He made a comparison of Wiener filter, Gaussian filter, and Median filter on welding seam image. In the classic image processing, it is very difficult to generalize a filter or algorithm, because it depends on the conditions and characteristics of camera parameters and light.

Another problem with these algorithms mentioned above is that the real-time analysis has an insufficient response time to be utilized in a process control despite recent developments in computational resources.

Deep learning techniques have efficient result in real-time executions [28] and classifications [24, 25] despite classifications on new images. One example applied in welding process is [62, 63]. It utilizes autoencoder deep learning technique to extract features of images process in laser welding. Another example of recent application of deep learning technique is [72]. It presents a method based on deep learning aims to extract information from photographs on spot welding. This monitoring system on the spot welding productive line shown better performance than the previous images analysis.

Not focused on welding arc analysis, but with good results, the work [73] proposed an automatic detection for weld defects in X-ray images. A classification model on deep neural network was developed. The accuracy rate of the proposed model was 91.84%. This was one more example of the potential of these techniques in welding area on images processing.


5. Modeling welding process

Today’s manufacturing environments has a rapid advancement on demand for quality products. Many techniques and methods are applied to correlate between process parameters and bead geometry. One of them is response surface methodology (RSM). It was applied by Sen in 2015 [74]. He made to evaluate the correlations between process parameters and weld bead geometry in double-pulsed gas metal arc welding (DP-GMAW). Santhana Babu [75] with the same technique got good results for predicting and controlling the weld bead quality in GTAW process. The problem of this method is that the researcher can find the equation, called response surface, by test and error. This can be very difficult. Many theoretical models have been defined to determine the process that occurs in the welding arc, including [76]. The main problems of these models were that they lose precision because it was very difficult to obtain a formula that contains all the complexity of these processes, as well as affirmed by Hang Dong in [77]. Mathematical models, based on machine learning techniques, have better results in problems as complex as this one. In the same paper, Hang Dong expressed the potential of these models.

One of the well-known and utilized regression algorithms is the least squares method. It was utilized in [78] to predict the seam position under strong arc light influence. Other work is [79] a LR model that is utilized to analyze the pool image centroid deviation and weld based on visual weld deviation measurement in GTAW process. The other technique is Gaussian process (GP) regression (GP), which was utilized in [77] to predict better performance in arc welding process of GTAW process.

An interesting method, utilized in [80], was Mahalanobis Distance Measurement (MDM). It was employed to determine welding faults occurrences. The same method was utilized in 2017 by Khairul Muzaka [81] on GMAW process to optimize welding current on a vertical-position welding. One problem of this method is that only correlate in function one input.

Bai and Lubecki [82] proposed a Localized Minimum and Maximum (LMM) analysis method in real time for welding monitoring system. The problem of LMM is that it exposes a simple function to measure the quality than not defining the complexity of the system. That is why, this work is limited only to the short-circuit transfer mode.

In 2017 by Junheung Park [83], a SVM was proposed with bootstrap aggregating that reduced the noisy on RSW data with computational efficiency. In this framework, other techniques as Generalized Regressive Neural Networks (GRNN) and Genetic algorithms for optimization were joined. This article demonstrates an increase in more complex computer science techniques for better analysis of welding processes. But the only way to know if all this was necessary is comparing with other techniques.

5.1 Artificial neural network models

Some researchers already had this reference of advantages of these algorithms. Bo Chen in 2009 [84] utilized ANN to training the experimental obtaining data. The good result of ANN prediction was validated by D-S evidence theory information fusion. They have also been utilized for different purposes and in different welding processes such as in SAW process [85] and GMAW cold metal transfer (CMT) process [86], for predicting weld bead geometry; in GTAW process, for predicting the angular distortion considering the bead geometry [87]; in girth welded pipes process, for predicting residual stresses [88]; and in underwater wet welding process, for predicting the weld seams, geometric parameters [89].

For better results, ANNs have been mixed with other techniques. One example is [90], where ANN and Support Vector Machine (SVM) are utilized for welded defect detecting and monitoring on a laser welding process. The other technique is by Bo Chen and Shanben Chen [91] for predicting the penetration in GTAW process. But they used different ANNs to process information from different sensors, and finally, they used the predictive fuzzy integral method.

Another example is [92], for predicting bead height and width in GMAW process using ANN Fuzzy ARTMAP, like monitoring task.

The increase in computational resources has allowed an increase in the complexity of ANN architectures. These are called Deep Neural Networks (DNN). They, bit by bit, begin to be applied in the welding process. One of them utilized was in [93]. The model is based on a DNN architecture to make a study of the estimation of weld bead parameters. This article mixed data from different welding processes. This is a risk for results analysis since different processes can have different outcomes with the same input parameters.

Rao et al. [94] utilized Generalized Regressive Neural Networks (GRNN) technique for estimating and optimizing the vibratory assisted welding parameters to produce quality welded joints. But in this case, it does not have comparison with other algorithms.

Di Wu, in 2017 [95], wrote a paper that addresses to perform Variable Polarity Plasma Arc Welding (VPPAW) process. Deep Belief Network (DBN), DNN variant, and t-Stochastic Neighbor Embedding (t-SNE) were studied for monitoring and identifying the penetration values. Experimental comparisons and verifications expose better performance for DBN, 97.62% exactly. This reaffirms the good results offered by the learning models developed with these algorithms. This work does not take the advantage of DNN algorithms to analyze both images and sound in real time.

Figure 6 shows a summary of articles analyzed. It shows that ANNs are one of the most used techniques, but they do not always offer the best result. This demonstrates the need to make comparisons between various modeling techniques in order to define the best result, in terms of efficiency and computational cost.

Figure 6.

Comparison between ANNs and ANN variations.

5.2 Comparison of different models

As it has been expressed in the previous sections, there are new techniques to analyze very complex systems. But they require expensive computational resources for their construction and sometimes for their execution. A comparison between models will allow to know which model has better results and which model can be the most effective to be utilized. This effectivity is measured in function of problem necessity, like the one shown in data mining (DM) methodologies and processes [16, 17].

An interesting comparison is Support Vector Machine (SVM) and ANN model, to identify weld groove state and weld deviation extraction in rotating arc narrow gap MAG welding (RANGMW) [96]. It presented SVM models with better results than ANN model.

One comparison with focus on time optimized was [97]. It utilized an ANN and ANN with differential evolutionary algorithm (DEA) separately. The results obtained by ANN using DEA were closer to ANN, but the computational time of ANN using DEA was shorter.

In the article [98], Response Surface Methodology (RSM) was compared with linear isotonic regression, regression (LR), regression trees, ANN, GP, and SVM, to evaluate mechanical properties in GMAW process. The results present that the DM models have poorer generalization on this research, because DM techniques require, to obtain acceptable results, a large amount dataset.

Sumesh in 2015 [99] compared Decision Trees (DT), ANN, Fuzzy Logic, SVM, and Random forest technique Weld Quality Monitoring in SMAW. The most efficient technique was Random forest. This shows that not always the most complex techniques offer the best results.

One of the few comparative analysis algorithms is Kumar’s paper in 2016 [100]. This paper explores Self-Organizing Maps (SOM) using as a mechanism for performing unsupervised learning, for comparing performance characteristics of various welding parameters which include welding power supplies and welders. Results obtained using SOM has been compared with the Probability Density Distributions (PDDs) obtained during statistical analysis. Voltage and current data analyzed using the SOM technique can also be utilized to evaluate the arc welding process. These studies demonstrate that there are other potential algorithms for welding process analysis. For that reason, it is necessary to evaluate and compare several of them to be agreed upon in a real-time process.

Other comparison in 2016 by Di Wu is [54]. The article compared a prediction model for Plasma Arc Welding based on Extreme Learning Machine (ELM) with ANN and SVM techniques. The ELM model had better generalization performance and was faster than others. This potentiality was established too by Nandhitha in 2016 [106]. He utilized GRNN and Radial Basis Networks (RBN) for torch current prediction in GTAW process. The torch current deviation was 98.95 % accuracy for the best result of GRNN.

In 2016 too, Kyoung-Yun Kim [107] discusses that in Resistance Spot Welding (RSW) process. He examined the prediction performance with GRNN and k-Nearest Neighbor (kNN) algorithms. The results indicate that with smaller k of kNN, the prediction performance measured by mean acceptable error has increased.

Other quality welding article was Xiaodong Wan in 2017 [102]. It proposed a Probabilistic Neural Network (PNN) model for quality prediction in large scale RSW process. In this case, the PNN model was more appropriate in quality level classification than the Back Propagation Neural Network.

The one of the last articles with direct DM techniques and welding relation is of Yiming Huang in 2017 [103]. This is an investigation of porosity on pulsed gas tungsten arc welding (P-GTAW) with an X-ray image analysis. To detect, an Empirical Mode Decomposition (EMD) and Spectral Analyses were made based on DM.

In 2017, Petković [104] predicted the laser welding quality by training data for the computational intelligence methodologies and support vector regression (SVR). SVR is a novel variant of SVM for regression task. This article made a comparison between SVR, ANN, and GP. It is another example that in certain problems, less complex algorithms can offer better results.

Table 2 presents a series of articles that were based on the monitoring and quality of the welding processes. The column Preparation defines the technique of processing the data obtained by the sensors; Classic for processes that do not use the latest techniques of image processing and DL for the use of deep learning; Online defines if the model was executed in real time; Compare, if in the research carried out in the article, a comparison is made between several algorithms; and Modeling defines the algorithms used in specific article. When a comparison exits, the first model before coma was the best quality result. As Tables 24, the best algorithm does not always match.

AuthorYearWelding processSensorsData preparationsModelingOnlineCompare
Saini [52]1998GMAWSoundClassicNoYesNo
Yue [69]2009Pipeline weldingVisualClassicTheoretical modelNoNo
Chen [64]2010LBW/GMAWVisualClassicYesNo
Horvat [53]2011GMAWSoundClassicNoYesNo
Gao [78]2011GTAWVisualClassicLR-ANNNoNo
Feng [80]2012GMAWStandardClassicMDMYesNo
Fidali [45]2013GMAWInfraredClassicStatistical analysisYesNo
Sreedhar [48]2013GTAWInfraredClassicStatistical analysisYesNo
Kalaichelvi [101]2013GMAWStandardClassicGA-FuzzyYesNo
Kumar [97]2014GMAWVisualClassicANN, ANN-DEAYesYes
Deyong You [90]2015Laser weldingPhotodiode, spectrometerWPD-PCAFFANN-SVMYesNo
Sumesh [99]2015SMAWSoundClassicSome DM (RF)YesYes
Kumar [100]2016SMAWStandardClassicPDDs, SOMNoYes
Muzaka [81]2016GMAWStandardClassicMDMYesNo
Bai [82]2016GMAWStandardClassicLMMYesNo
Park [83]2017RSWStandardClassicGRNN-SVMYesNo
Wan [102]2017LSRSWStandardClassicANN (BP), ANN (Prob)YesYes
Huang [103]2017P-GTAWVisualClassicDM, EMDNoYes
Petković [104]2017Laser weldingMultiplesClassicSVM, ANN, GPYesYes
Muniategui [72]2017RSWvisualDL, classicFuzzyYesYes
Wan [105]2017GTAWvisualClassicANN and fuzzyYesNo

Table 2.

Table articles with quality objective.

AuthorYearWelding processSensorsData preparationsModelingOnlineCompare
Bo Chen [84]2009GTAWMultiplesClassicANN-DSNoNo
Bo Chen [91]2010GTAWMultiplesClassicANN-fuzzyNoNo
Seyyedian [108]2012GTAWStandardClassicANNYesNo
Li [79]2014GTAWVisualClassicLRNoNo
Bo Chen [89]2014UWWVisualClassicANNYesNo
Li [96]2014RANGMWVisualClassicSVM, ANNYesYes
Escribano-García [98]2014GMAWStandardClassicRSM, some DMYesYes
Sen [74]2015DP-GMAWStandardClassicTaguchi-RSMNoNo
Keshmiri [93]2015SAW, GMAW, GTAWStandardClassicDNNYesNo
Wu [54]2016VPPAWSoundClassicELM, ANN, SVMYesYes
Lv [55]2016GTAWSoundClassicBP-AdaboostYesYes
Dong [77]2016GTAWStandardClassicGPRYesNo
Sarkar [85]2016SAWStandardClassicMRA and ANNYesYes
Rong [87]2016GTAWStandardClassicANNYesNo
Rios-Cabrera [92]2016GMAWVisualClassicANN fuzzy ARTMAPYesNo
Nandhitha [106]2016GTAWThermographyClassicELM, RBN, GRNNYesYes
Kim [107]2016RSWStandardClassickNN, GRNNYesYes
Aviles-Viñas [109, 110]2016GMAWVisualClassicANN-fuzzyYesNo
Pavan Kumar [86]2017GMAW CMTStandardClassicANNYesNo
Mathew [88]2017Girth weldsStandardClassicANNYesNo
Di Wu [95]2017VP-PAWVisual, soundClassict-SNE and DBNNoNo

Table 3.

Table articles with prediction objective.

AuthorYearWelding processSensorsData preparationsModelingOnlineCompare
Chen [66]2000P-GTAWDouble-visualClassicANN-learning controlYesYes
Chen [111]2009GTAWVisualClassicANN-fuzzyYesNo
Malviya [112]2011GMAWStandardClassicANN-PSOYesNo
Hailin [105]2012GMAWVisualClassicANN and fuzzyYesNo
Cruz [113]2015GMAWVisualClassicANN and fuzzyYesNo
Günther [63]2016Laser weldingVisualDLDL-RLYesNo
Santhana [75]2016GTAWStandardClassicRSMYesNo
Sharma [114]2016SAWStandardClassicRSM and fuzzyYesNo
Moghaddam [115]2016GMAWVisualClassicANN-PSOYesNo
Lv [56]2017GTAWSoundClassicANNYesNo
Rao [94]2017Vibratory WeldingStandardClassicGRNNYesNo
Pengfei Hu [116]2017GMAWStandardClassicMath-model—fuzzyYesNo

Table 4.

Table articles with control objective.

Defining which of the techniques is more effective for our problem also helps in the effectiveness of a future process of intelligent control.


6. Intelligent control of welding process

The intelligent control approach offers interesting perspectives since it is able to provide methodologies that allow to perform automatically some of the tasks typically performed by humans [117]. This combines with data mining models.

One intelligent control tendency utilized is a fuzzy method with ANN model. Example of this was [111] on GTAW process for predicting the dynamic of the weld pool; and in [105] for GMAW pipe-line welding, to improve the welding quality.

Another example was [113], on GMAW process, for modeling and control of weld bead width. Other example of fuzzy methods but different model techniques was [114]. It was applied for better control purpose of bead geometry parameters in submerged arc welding (SAW) process. This article proposed the response of a fuzzy logic approach with surface methodology (RSM). Demonstrating that any model obtained from a welding process can be integrated into a control system. As long as it meets time demands.

Conventional and intelligent control methods were investigated by [67] in P-GTAW process. This work made a comparison with PID control, fuzzy control, and neuron self-learning PSD control. It had better performance. This article highlights the advantage of learning-based control.

Other optimization based in learning was [115]. It proposed ANN model with a Particle Swarm Optimization (PSO) algorithm to optimize weld bead geometry characteristics on the GMAW process. The ANN-PSO model obtained an efficient optimization and multi-criteria modeling.

An emerging learning-based control system was used by Günther in [62, 63] for laser welding control. This technique is called reinforcement learning (RL). It is a machine learning branch. It is focused on decision-making by learning process [118]. Control learning can be an optimization-based method like Q-learning algorithm. It can be used to solve optimal control problems like expressed in [119].

Günther’s study [63] is one of the few RL studies for laser welding system. This makes this work an important contribution to welding process engineering. RL is a new technique open now in welding process with noble success in other areas like appearing in [120, 121, 122, 123].


7. Future perspective

These techniques of data analysis based on learning, as appearing in this article, is not yet widespread in welding process area. A bibliometric analysis among the authors studied in this research, presents a very little relationship between them. Figure 7 exposes this. The small dimensions of the authors’ clouds (articles with welding process and new data analysis techniques) and their relationships (joint publications) show little maturity in the interrelation of these areas.

Figure 7.

Bibliometric analysis: authors’ interrelationship.

Some of the works demonstrate a small approximation between the areas, fulfilling the interdisciplinarity that Industry 4.0 advocates. Achieving this interdisciplinarity implies new study processes, defining new methodologies that unify the potential of these two areas. The needs of the modern world are going to make this happen in a short time. The new data analysis conception in welding processes area will be an acceleration in obtaining new and better models, more efficient predictions, and controls.


8. Conclusions

Several articles about the welding process were analyzed. These allowed to determine for each data mining stage how it is possible to optimize the results to obtain a good result of process analysis. Several analysis algorithms of the welding process were shown, and it was demonstrated that the comparison between them can make the process analysis more efficient and less expensive. The potential of learning-based techniques was described, because computational resources are becoming cheaper, and more quality information of welding process can be obtained. All these premises aligned with the so-called Industry 4.0, where a set of technologies that allow a fusion of physical and digital world, create a more intelligent and dynamic system.



The authors would like to acknowledge IntechOpen, Brasilia University, CNPq, CAPES, and PPMEC-UnB, and also to professors Alysson Martin Silva, and Guillermo Albarez Bestard.


  1. 1. Villani P, Modenesi PJ, Bracarense AQ. Soldagem: Fundamentos e Tecnologia. Brasil: Elsevier; 2016
  2. 2. Zhang YM. Institute of Materials, Real-time Weld Process Monitoring. Woodhead Pub. and Maney Pub. on behalf of the Institute of Materials, Minerals and Mining; 2008. Available from:
  3. 3. Chen SB, Lv N. Research evolution on intelligentized technologies for arc welding process. Journal of Manufacturing Processes. 2014;16(1):109-122
  4. 4. Haffner O, Kucera E, Kozak S, Stark E. Proposal of system for automatic weld evaluation. In: 2017 21st International Conference on Process Control (PC). IEEE; 2017. pp. 440-445. Available from:
  5. 5. Jiang C, Zhang F, Wang Z. Image processing of aluminum alloy weld pool for robotic VPPAW based on visual sensing. IEEE Access. 2017;5:21567-21573. Available from:
  6. 6. Chong L, Ramakrishna S, Singh S. A review of digital manufacturing-based hybrid additive manufacturing processes. The International Journal of Advanced Manufacturing Technology. 2018;95(5-8):2281-2300. Available from:
  7. 7. Tuominen V. The measurement-aided welding cellgiving sight to the blind. The International Journal of Advanced Manufacturing Technology. 2016;86(1-4):371-386. Available from:
  8. 8. Hernandez Orallo J, Ramirez Quintana MJ, Ferri Ramirez C. Introduccion a la Mineria de Datos. NJ, USA: Pearson Prentice Hall; 2004
  9. 9. Marsland S. Machine Learning, An Algorithmic Perspective. USA: CRC Press; 2015
  10. 10. Bell J. Machine Learning: Hands-On for Developers and Technical Professionals. Indianapolis, IN, USA: John Wiley & Sons, Inc.; 2015
  11. 11. Casalino G. [INVITED] Computational intelligence for smart laser materials processing. Optics & Laser Technology. 2018;100:165-175. Available from:
  12. 12. Yu D, Deng L. Deep learning and its applications to signal and information processing [exploratory DSP]. IEEE Signal Processing Magazine. 2011;28(1):145-154. Available from:
  13. 13. Hirji KK. Discovering data mining: From concept to implementation. SIGKDD Explorations Newsletter. 1999;1(1):44-45. Available from:
  14. 14. Norton MJ. Knowledge discovery in databases. Library Trends. 1999;48(1):9-21. Available from:
  15. 15. Olson DL, Delen D. Advanced Data Mining Techniques. 1st ed. NY, USA: Springer Publishing Company, Incorporated; 2008
  16. 16. Piatetsky G. CRISP-DM, still the top methodology for analytics, data mining, or data science projects. 2014. [Online]. Available from: [Accessed: 27 July 2017]
  17. 17. Chambers M, Doig C, Stokes-Rees I. Breaking Data Science Open. 1st ed. CA, USA: O’Reilly Media, Inc; 2017
  18. 18. Huang Z, Xu X, He H, Tan J, Sun Z. Parameterized batch reinforcement learning for longitudinal control of autonomous land vehicles. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019;49(4):730-741
  19. 19. Chi R, Hou Z, Jin S, Huang B. An improved data-driven point-to-point ilc using additional on-line control inputs with experimental verification. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019;49(4):687-696
  20. 20. Woods AC, La HM. A novel potential field controller for use on aerial robots. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019;49(4):665-676
  21. 21. Shalev-Shwartz S, Ben-David S. Understanding Machine Learning: From Theory to Algorithms. New York, USA: Cambridge University Press; 2014
  22. 22. Alpaydin E. Introduction to Machine Learning. USA: Massachusetts Institute of Technology; 2010
  23. 23. Mesnil G, He X, Deng L, Bengio Y. Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding Iterspeech. In: Bimbot F, Cerisara C, Fougeron C, Gravier G, Lamel L, Pellegrino F, et al. ISCA. 2013. pp. 3771-3775
  24. 24. Zhu Z, Luo P, Wang X, Tang X. Multi-View Perceptron: A Deep Model for Learning Face Identity and View Representations. 2014. pp. 217-225
  25. 25. Pachitariu M, Packer AM, Pettit N, Dalgleish H, Hausser M, Sahani M. Extracting regions of interest from biological images with convolutional sparse block coding. 2013. pp. 1745-1753
  26. 26. Yang J, Price B, Cohen S, Lee H, Yang M-H. Object contour detection with a fully convolutional encoder-decoder network. Cvpr 2016. 2016. Available from:
  27. 27. Pachauri D, Kondor R, Sargur G, Singh V. Permutation Diffusion Maps (PDM) with Application to Image Association Problem in Computer Vision. 2014. pp. 541-549
  28. 28. Redmon J, Divvala S, Girshick R, Farhadi A. You only look once: Unified, real-time object detection. Cvpr 2016. 2016. pp. 779-788
  29. 29. Vondrick C, Pirsiavash H, Torralba A. Anticipating visual representations from unlabeled video. In: IEEE Conference on Computer Vision and Pattern Recognition. 2015. Available from:
  30. 30. Zheng S, Dongang W, Shih-Fu C. Temporal action localization in untrimmed videos via multi-stage CNNs. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2016. pp. 1049-1058
  31. 31. Luo S, Zhu L, Althoefer K, Liu H. Knock-Knock: Acoustic object recognition by using stacked denoising autoencoders. Neurocomputing. 2017;267:18-24. Available from:
  32. 32. McLoughlin I, Zhang H, Xie Z, Song Y, Xiao W, Phan H. Continuous robust sound event classification using time-frequency features and deep learning. PLoS ONE. 2017;12(9):e0182309. Available from:
  33. 33. Zhou Z-H. Three perspectives of data mining. Artificial Intelligence. 2003;143(1):139-146. Available from:
  34. 34. Marbán Ó, Mariscal G, Segovia J. A data mining & knowledge discovery process model. Data Mining and Knowledge. 2009;(February):1-17. Available from:
  35. 35. C. Analytics. Web of Science. 2018. Available from:
  36. 36. AWS. Welding Inspection Handbook 3rd Edition. 2000
  37. 37. Modenesi PJ. Introdução à Física do Arco Elétrico e sua Aplicação na Soldagem dos Metais. Dep. Eng. Met. e Mater. Univ. Fed. Minas Gerais—UFMG. 2004. p. 159
  38. 38. Abdullahi I, Hamza MF. A review on the application of resistance spot welding of automotive sheets. 2015;(December)
  39. 39. Ouisse M, Cogan S. Robust design of spot welds in automotive structures: A decision-making methodology. Mechanical Systems and Signal Processing. 2010;24(4):1172-1190
  40. 40. Dawes CT. Laser Welding: A Practical Guide. 1992
  41. 41. Mazmudar CP, Patel K. Effect of laser welding process parameters on mechanical properties of stainless steel-316. 2014;1(5):1-11
  42. 42. Alvarez Bestard G. Sensor fusion and embedded devices to estimate and control the depth and width of the weld bead in real time [Ph.D. thesis, Ph.D. dissertation]. 2017. Available from:
  43. 43. Nagarajan S, Nagarajan S, Banerjee P, Banerjee P, Chen W, Chen W, et al. Control of the welding process using infrared sensors. Society. 1992;8(1):86-93
  44. 44. Mota CP, Machado MVR, Finzi Neto RM, Vilarinho LO. Sistema de visão por infravermelho próximo para monitoramento de processos de soldagem a arco. Soldagem & Inspeção. 2013;18(1):19-30
  45. 45. Fidali M, Jamrozik W. Diagnostic method of welding process based on fused infrared and vision images. Infrared Physics & Technology. 2013;61:241-253
  46. 46. Bagavathiappan S, Lahiri BB, Saravanan T, Philip J, Jayakumar T. Infrared thermography for condition monitoring—A review. Infrared Physics & Technology. 2013;60:35-55
  47. 47. Vilarinho LO, Mota CP, Machado MVR, Finzi Neto RM. Near-infrared vision system for arc-welding monitoring. In: DebRoy T, David SA, JN DP, Koseki T, Bhadeshia HK, editors. Trends in Welding Research: Proceedings of the 9th International Conference. Proceedings Paper. ASM Int. 9503 Kinsman Rd, Materials Park, OH 44073 USA: ASM International; 2013. pp. 1029-1037
  48. 48. Sreedhar U, Krishnamurthy CV, Balasubramaniam K, Raghupathy VD, Ravisankar S. Automatic defect identification using thermal image analysis for online weld quality monitoring. Journal of Materials Processing Technology. 2012;212(7):1557-1566
  49. 49. Vasudevan M, Chandrasekhar N, Maduraimuthu V, Bhaduri AK, Raj B. Real-time monitoring of wield pool during gtaw using infra-red thermography and analysis of infra-red thermal images. Welding in the World. 2011;55(7-8):83-89
  50. 50. Benoit A, Paillard P, Baudin T, Klosek V, Mottin JB. Comparison of four arc welding processes used for aluminium alloy cladding. Science and Technology of Welding and Joining. 2015;20(1):75-81
  51. 51. Tarn J, Huissoon J. Developing psycho-acoustic experiments in gas metal arc welding. IEEE International Conference Mechatronics and Automation. 2005, 2014;2(January):1112-1117. Available from:
  52. 52. Saini BYD. An Investigation of Gas Metal Arc Welding Sound Signature for On-Line Quality Control. 1998. pp. 172-179. Available from:
  53. 53. Horvat J, Prezelj J, Polajnar I, Čudina M. Monitoring gas metal arc welding process by using audible sound signal. Strojniški Vestnik Journal of Mechanical Engineering. 2011;2011(03):267-278
  54. 54. Wu D, Chen H, He Y, Song S, Lin T, Chen S. A prediction model for keyhole geometry and acoustic signatures during variable polarity plasma arc welding based on extreme learning machine. Sensor Review. 2016;36(3):257-266
  55. 55. Lv N, Xu YL, Fang G, Yu XW, Chen SB. Research on welding penetration state recognition based on BP-Adaboost model for pulse GTAW welding dynamic process. In: Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO. Vol. 2016. IEEE; 2016. pp. 100-105. Available from:
  56. 56. Lv N, Xu Y, Li S, Yu X, Chen S. Automated control of welding penetration based on audio sensing technology. Journal of Materials Processing Technology. 2017;250:81-98. Available from:
  57. 57. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. Available from:
  58. 58. Xu Y, Yu H, Zhong J, Lin T, Chen S. Real-time image capturing and processing of seam and pool during robotic welding process. Industrial Robot—An International Journal. 2012;39(5):513-523
  59. 59. Liu Y-K, Huang N, Zhang Y-M. Modeling of human welder response against 3D weld pool surface using machine-human cooperative virtualized welding platform. In: Tarn TJ, Chen SB, Chen XQ, editors. Robotic Welding, Intelligence and Automation, RWIA’2014, Ser. Advances in Intelligent Systems and Computing. Proceedings Paper. Vol. 363. Heidelberger Platz 3, D-14197 Berlin, Germany: Springerverlag Berlin; 2015. pp. 451-457
  60. 60. Ogawa Y. High speed imaging technique. Part 1—High speed imaging of arc welding phenomena. Science and Technology of Welding and Joining. 2011;16(1):33-43
  61. 61. Gao F, Chen Q, Guo L. Study on arc welding robot weld seam touch sensing location method for structural parts of hull. In: 2015 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE; 2015. pp. 42-46
  62. 62. Günther J, Pilarski PM, Helfrich G, Shen H, Diepold K. First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technology. 2014;15:474-483
  63. 63. Günther J. Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning. Mechatronics. 2016;34:1-11. Available from:
  64. 64. Chen JZ, Farson DF. Hybrid welds coaxial vision monitoring of LBW/GMAW hybrid welding process. Materials Evaluation. 2010;68(12):1318-1328
  65. 65. Ramos EG, de Carvalho GC, Absi Alfaro SC. Analysis of weld pool oscillation in P-GMAW by means of shadowgraphy image processing. Soldagem & Inspeção. 2013;18(1):39-49
  66. 66. Siewert E, Wilhelm G, Haessler M, Schein J, Hanson T, Schnick M, et al. Visualization of gas flows in welding arcs by the Schlieren measuring technique. Welding Journal. 2014;93(January):1-5
  67. 67. Chen SB, Lou YJ, Wu L, Zhao DB. Intelligent methodology for sensing, modeling and control of pulsed GTAW: Part I—Bead-on-plate welding. Welding Journal. 2000;79(6):151s-163s
  68. 68. Ma G, Li L, Chen Y. Effects of beam configurations on wire melting and transfer behaviors in dual beam laser welding with filler wire. Optics and Laser Technology. 2017;91(April):138-148. DOI: 10.1016/j.optlastec.2016.12.019
  69. 69. Yue H, Li K, Zhao HW, Zhang Y. Vision-based pipeline girth-welding robot and image processing of weld seam. Industrial Robot—An International Journal. 2009;36(3):284-289. Available from:
  70. 70. Xu Y, Fang G, Chen S, Zou JJ, Ye Z. Real-time image processing for vision-based weld seam tracking in robotic GMAW. International Journal of Advanced Manufacturing Technology. 2014;73(9-12):1413-1425
  71. 71. Wu Q-Q, Lee J-P, Park M-H, Park C-K, Kim I-S. A study on development of optimal noise filter algorithm for laser vision system in GMA welding. In: Xavior MA, PKDV Y, editors. 12th Global Congress on Manufacturing and Management (GCMM—2014), ser. Procedia Engineering. Proceedings Paper. Vol. 97. VIT Univ, Sch Mech & Bldg Sci; Queensland Univ Technol. Sara Burgerhartstraat 25, PO BOX 211, 1000 AE Amsterdam, Netherlands: Elsevier Science BV; 2014. pp. 819-827
  72. 72. Muniategui A, Hériz B, Eciolaza L, Ayuso M, Iturrioz A, Quintana I, et al. Spot welding monitoring system based on fuzzy classification and deep learning. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE; 2017. pp. 1-6. Available from:
  73. 73. Hou W, Wei Y, Guo J, Jin Y, Zhu C. Automatic detection of welding defects using deep neural network. Journal of Physics: Conference Series. 2018;933:012006
  74. 74. Sen M, Mukherjee M, Pal TK. Evaluation of correlations between DP-GMAW process parameters and bead geometry. Welding Journal. 2015;(July):265-279
  75. 75. Santhana Babu AV, Giridharan PK, Ramesh Narayanan P, Narayana Murty SVS. Prediction of bead geometry for flux bounded TIG welding of AA 2219-T87 aluminum alloy. Journal of Advanced Manufacturing Systems. 2016;15(02):69-84. Available from:
  76. 76. Boutaghane A, Bouhadef K, Valensi F, Pellerin S, Benkedda Y. Theoretical model and experimental investigation of current density boundary condition for welding arc study. European Physical Journal-Applied Physics. 2011;54(1):13
  77. 77. Dong H, Cong M, Liu Y, Zhang Y, Chen H. Predicting characteristic performance for arc welding process. In: 2016 IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems (CYBER). IEEE; 2016. pp. 7-12
  78. 78. Gao X, Ding D, Bai T, Katayama S. Weld-pool image centroid algorithm for seam-tracking vision model in arc-welding process. IET Image Processing. 2011;5(5):410-419
  79. 79. Li Z, Gao X. Study on regression model of measuring weld position. In: Choi SB, Yarlagadda P, AbdullahAlWadud M, editors. Sensors, Mechatronics and Automation, Ser. Applied Mechanics and Materials. Proceedings Paper. Vol. 511-512. Laublsrutistr 24, CH-8717 Stafa-Zurich, Switzerland: Trans Tech Publications Ltd; 2014. pp. 514-517
  80. 80. Feng S, Lin G, Ma B, Hu S. A novel measurement and qualification method of GMAW welding fault based on digital signals. In: Chen WZ, Xu XP, Dai PQ, Chen YL, editors. Advanced Manufacturing Technology, Pts 1-4, Ser. Advanced Materials Research. Proceedings Paper. Vol. 472-475. Fujian Univ Technol; Xiamen Univ; Fuzhou Univ; Huaqiao Univ; Univ Wollongong; Fujian Mech Engn Soc; Hong Kong Ind Technol Res Ctr. Laublsrutistr 24, CH-8717 Stafa-Zurich, Switzerland: Trans Tech Publications Ltd; 2012. pp. 1201-1205
  81. 81. Muzaka K, Park MH, Lee JP, Jin BJ, Lee BR, Kim WYIS. A study on prediction of welding quality using mahalanobis distance method by optimizing welding current for a vertical-position welding. Procedia Engineering. 2017;174:60-67. Available from:
  82. 82. Bai F, Lubecki TM. Robotic arc welding with on-line process monitoring based on the LMM analysis of the welding process stability. In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). 2016. pp. 566-571. Available from:
  83. 83. Park J, Kim K-Y. Prediction modeling framework with bootstrap aggregating for noisy resistance spot welding data. Journal of Manufacturing Science and Engineering. 2017;139(10):101003
  84. 84. Chen B, Wang J, Chen S. Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. International Journal of Advanced Manufacturing Technology. 2010;48(1-4):83-94
  85. 85. Sarkar A, Dey P, Rai R, Saha S. A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding. Sadhana—Academy Proceedings in Engineering Sciences. 2016;41(5):549-559
  86. 86. Pavan Kumar N, Devarajan PK, Arungalai Vendan S, Shanmugam N. Prediction of bead geometry in cold metal transfer welding using back propagation neural network. The International Journal of Advanced Manufacturing Technology. 2017;93(1-4):385-392. Available from:
  87. 87. Rong Y, Huang Y, Zhang G, Chang Y, Shao X. Prediction of angular distortion in no gap butt joint using BPNN and inherent strain considering the actual bead geometry. International Journal of Advanced Manufacturing Technology. 2016;86(1-4):59-69. Available from:
  88. 88. Mathew J, Moat R, Paddea S, Fitzpatrick M, Bouchard P. Prediction of residual stresses in girth welded pipes using an artificial neural network approach. International Journal of Pressure Vessels and Piping. 2017;150:89-95
  89. 89. Chen B, Feng J. Modeling of underwater wet welding process based on visual and arc sensor. Industrial Robot—An International Journal. 2014;41(3):311-317
  90. 90. You D, Gao X, Katayama S. WPD-PCA-based laser welding process monitoring and defects diagnosis by using FNN and SVM. IEEE Transactions on Industrial Electronics. 2015;62(1):628-636
  91. 91. Chen B, Chen S. Multi-sensor information fusion in pulsed GTAW based on fuzzy measure and fuzzy integral. Assembly Automation. 2010;30(3):276-285
  92. 92. Rios-Cabrera R, Morales-Diaz AB, Aviles-Viñas JF, Lopez-Juarez I. Robotic GMAW online learning: Issues and experiments. International Journal of Advanced Manufacturing Technology. 2016;87(5-8):2113-2134
  93. 93. Keshmiri S, Zheng X, Feng LW, Pang CK, Chew CM. Application of deep neural network in estimation of the weld bead parameters. In: IEEE International Conference on Intelligent Robots and Systems. Vol. 2015. 2015. pp. 3518-3523. Available from:
  94. 94. Rao PG, Srinivasa Rao P, Deepak BB. GRNN-immune based strategy for estimating and optimizing the vibratory assisted welding parameters to produce quality welded joints. Engineering Journal. 2017;21(3):251-267
  95. 95. Wu D, Huang Y, Chen H, He Y, Chen S. VP-PAW penetration monitoring based on fusion of visual and acoustic signals using t-SNE and DBN model. Materials and Design. 2017;123:1-14. Available from:
  96. 96. Li W, Gao K, Wu J, Hu T, Wang J. SVM-based information fusion for weld deviation extraction and weld groove state identification in rotating arc narrow gap MAG welding. International Journal of Advanced Manufacturing Technology. 2014;74(9-12):1355-1364
  97. 97. Kumar GS, Natarajan U, Veerarajan T, Ananthan SS. Quality level assessment for imperfections in GMAW. Welding Journal. 2014;93(3):85S-97S
  98. 98. Escribano-García R, Lostado-Lorza R, Fernández-Martínez R, Villanueva-Roldán P, Mac Donald BJ. Improvement in manufacturing welded products through multiple response surface methodology and data mining techniques. Advances in Intelligent Systems and Computing. 2014;299:301-310
  99. 99. Sumesh A, Rameshkumar K, Mohandas K, Babu RS. Use of machine learning algorithms for weld quality monitoring using acoustic signature. Procedia Computer Science. 2015;50:316-322. Available from:
  100. 100. Kumar V, Albert SK, Chandrasekhar N, Jayapandian J, Venkatesan MV. Performance analysis of arc welding parameters using self organizing maps and probability density distributions. In: 2016 IEEE First International Conference on Control, Measurement and Instrumentation (CMI). IEEE; 2016. pp. 196-200
  101. 101. Kalaichelvi V, Karthikeyan R, Sivakumar D. Analysis of gas metal arc welding process using GA tuned fuzzy rule based system. Journal of Intelligent & Fuzzy Systems. 2013;25(2):429-440
  102. 102. Wan X, Wang Y, Zhao D, Huang Y. A comparison of two types of neural network for weld quality prediction in small scale resistance spot welding. Mechanical Systems and Signal Processing. 2017;93:634-644
  103. 103. Huang Y, Wu D, Lv N, Chen H, Chen S. Investigation of porosity in pulsed GTAW of aluminum alloys based on spectral and X-ray image analyses. Journal of Materials Processing Technology. 2017;243:365-373
  104. 104. Petković D. Prediction of laser welding quality by computational intelligence approaches. Optik. 2017;140:597-600. Available from:
  105. 105. Hailin H, Jing L, Fang L, Wei Z, Heqiang P. Neural-fuzzy variable gap control method for GMAW pipe-line welding with CCD camera. In: Zhao H, editor. Mechanical and Electronics Engineering III, Pts 1-5, Ser. Applied Mechanics and Materials. Proceedings Paper. Vol. 130-134. Hefei UnivTechnol. Laublsrutistr 24, CH-8717 Stafa-Zurich, Switzerland: Trans Tech Publications Ltd; 2012. pp. 2358-2363
  106. 106. Nandhitha NM. Artificial Neural Network Based Prediction Techniques for Torch Current Deviation to Produce Defect-Free Welds in GTAW Using IR Thermography. 2016. pp. 137-142. Available from:
  107. 107. Kim KY, Park J, Sohmshetty R. Prediction measurement with mean acceptable error for proper inconsistency in noisy weldability prediction data. Robotics and Computer-Integrated Manufacturing. 2017;43:18-29
  108. 108. Seyyedian Choobi M, Haghpanahi M, Sedighi M. Prediction of welding-induced angular distortions in thin butt-welded plates using artificial neural networks. Computational Materials Science. 2012;62:152-159
  109. 109. Aviles-Viñas JF, Rios-Cabrera R, Lopez-Juarez I. On-line learning of welding bead geometry in industrial robots. International Journal of Advanced Manufacturing Technology. 2016;83(1-4):217-231
  110. 110. Wan X, Wang Y, Zhao D, Huang YA, Yin Z. Weld quality monitoring research in small scale resistance spot welding by dynamic resistance and neural network. Measurement: Journal of the International Measurement Confederation. 2017;99:120-127
  111. 111. Chen SB, Wang WY, Ma HB. Intelligent control of arc welding dynamics during robotic welding process. In: Chandra T, Wanderka N, Reimers W, Ionescu M, editors. Thermec 2009, PTS 1-4, Ser. Materials Science Forum. Proceedings Paper. Vol. 638-642. Minerals, Met & Mat Soc. Laublsrutistr 24, CH-8717 Stafa-Zurich, Switzerland: Trans Tech Publications Ltd; 2010. pp. 3751-3756
  112. 112. Malviya R, Pratihar DK. Tuning of neural networks using particle swarm optimization to model MIG welding process. Swarm and Evolutionary Computation. 2011;1(4):223-235. Available from:
  113. 113. Cruz JG, Torres EM, Alfaro SCA. A methodology for modeling and control of weld bead width in the GMAW process. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2015;37(5):1529-1541
  114. 114. Sharma SK, Maheshwari S, Rathee S. Multi-objective optimization of bead geometry for submerged arc welding of pipeline steel using RSM-fuzzy approach. Journal for Manufacturing Science and Production. 2016;16(3):141-151
  115. 115. Azadi Moghaddam M, Golmezergi R, Kolahan F. Multivariable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNNPSO approach. Measurement. 2016;92:279-287
  116. 116. Wang Z. Monitoring of GMAW weld pool from the reflected laser lines for real-time control. IEEE Transactions on industrial informatics. 2014;10(4):2073-2083
  117. 117. Santos M. Un enfoque aplicado del control inteligente. RIAI—Revista Iberoamericana de Automatica e Informatica Industrial. 2011;8(4):283-296. Available from:
  118. 118. Sutton R, Barto A. Reinforcement learning: An introduction. Trends in Cognitive Sciences. 1999;3(9):360
  119. 119. Li J, Chai T, Lewis FL, Fan J, Ding Z, Ding J. Off-policy Q-learning: Set-point design for optimizing dual-rate rougher flotation operational processes. IEEE Transactions on Industrial Electronics. 2018;65(5):4092-4102
  120. 120. Chincoli M, Liotta A. Self-learning power control in wireless sensor networks. Sensors. 2018;18(2):375. Available from:
  121. 121. Ramanathan P, Mangla KK, Satpathy S. Smart controller for conical tank system using reinforcement learning algorithm. Measurement: Journal of the International Measurement Confederation. 2018;116:422-428
  122. 122. Yin L, Yu T, Zhou L. Design of a novel smart generation controller based on deep Q learning for large-scale interconnected power system. Journal of Energy Engineering. 2018;144(3):04018033
  123. 123. Hu P, Huang J, Zeng M. Application of fuzzy control method in gas metal arc welding. The International Journal of Advanced Manufacturing Technology. 2017;92(5-8):1769-1775. Available from:

Written By

Rogfel Thompson Martinez and Sadek Crisóstomo Absi Alfaro

Submitted: August 26th, 2019 Reviewed: January 14th, 2020 Published: March 10th, 2020