Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis

Pereda, María; Santos, José Ignacio; Martín, Óscar; Galán, José Manuel

doi:10.1179/1362171815y.0000000052

Cited by 24 publications

(14 citation statements)

References 34 publications

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“…RSW holds a promising optimization potential as a result of the balance that it establishes between cost and performance; remarkably, the tendency in the automotive industry is to reduce the number of RSW joints per vehicle, which makes the accuracy of the tools to assist in the quality control of RSW joints [10] more critical, as the fewer the RSW joints per vehicle, the stronger the requirements for each of them [6].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Classification Algorithms for Quality Assessment of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs

Martín¹,

Ahedo

Santos

et al. 2022

IEEE Access

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Resistance spot welding (RSW) is a widespread manufacturing process in the automotive industry. There are different approaches for assessing the quality level of RSW joints. Multi-input-singleoutput methods, which take as inputs either the intrinsic parameters of the welding process or ultrasonic nondestructive testing variables, are commonly used. This work demonstrates that the combined use of both types of inputs can significantly improve the already competitive approach based exclusively on ultrasonic analyses. The use of stacking of tree ensemble models as classifiers dominates the classification results in terms of accuracy, F-measure and area under the receiver operating characteristic curve metrics. Through variable importance analyses, the results show that although the welding process parameters are less relevant than the ultrasonic testing variables, some of the former provide marginal information not fully captured by the latter.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Classification Algorithms for Quality Assessment of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs

Martín¹,

Ahedo

Santos

et al. 2022

IEEE Access

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“…Most of the methods used for machine learning modelling can be classified as classical machine learning methods (LaCasse et al 2019), like Linear Regression (LR) (Cho and Rhee 2002;Martín et al 2009;Panchakshari and Kadam 2013), Polynomial Regression (PolyR) (Pashazadeh et al 2016;, or Generalised Linear Models (GLM) Gavidel et al (2019), k-Nearest Neighbours (kNN) (Haapalainen et al 2005;Koskimaki et al 2007;Boersch et al 2016), Decision Trees (DT) (Zhang et al 2015;Kim and Ahmed 2018), Random Forests (RF) (Pereda et al 2015;Boersch et al 2016), Support Vector Machines (SVM), etc. Statistic methods like Linear or Quadratic Discriminate Analysis (LDA and QDA) are also used for classification.…”

Section: Modellingmentioning

confidence: 99%

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

et al. 2022

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Digitalisation trends of Industry 4.0 and Internet of Things led to an unprecedented growth of manufacturing data. This opens new horizons for data-driven methods, such as Machine Learning (ML), in monitoring of manufacturing processes. In this work, we propose ML pipelines for quality monitoring in Resistance Spot Welding. Previous approaches mostly focused on estimating quality of welding based on data collected from laboratory or experimental settings. Then, they mostly treated welding operations as independent events while welding is a continuous process with a systematic dynamics and production cycles caused by maintenance. Besides, model interpretation based on engineering know-how, which is an important and common practice in manufacturing industry, has mostly been ignored. In this work, we address these three issues by developing a novel feature-engineering based ML approach. Our method was developed on top of real production data. It allows to analyse sequences of welding instances collected from running manufacturing lines. By capturing dependencies across sequences of welding instances, our method allows to predict quality of upcoming welding operations before they happen. Furthermore, in our work we strive to combine the view of engineering and data science by discussing characteristics of welding data that have been little discussed in the literature, by designing sophisticated feature engineering strategies with support of domain knowledge, and by interpreting the results of ML analysis intensively to provide insights for engineering. We developed 12 ML pipelines in two dimensions: settings of feature engineering and ML methods, where we considered 4 feature settings and 3 ML methods (linear regression, multi-layer perception and support vector regression). We extensively evaluated our ML pipelines on data from two running industrial production lines of 27 welding machines with promising results.

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“…Comparatively, the control improvements about double-wire GMAW process were a bit limited when compared to other forms of GMAW process. By means of serious researching relative improvements used in other forms of GMAW process, the future improvements about this process will be more and more improved, especially can employ artificial intelligent tools [56], [57] for process analysis and control, or technique innovations, such as bypass coupling technology, and so on.…”

Section: Improvement Of Process Control Of the Double-wire Gmaw Processmentioning

confidence: 99%

Review of the Recent Trends of Process Monitoring and Control for Double- Wire GMAW Process

Zhou

Yao

2019

IEEE Access

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Recently, double-wire gas metal arc welding (GMAW) process is more and more prevalently employed in industrial applications and attracted many academic researches. This paper reviewed the recent trends of researches and applications of this process. Three main aspects were included in this paper. The first was the operational process analysis of the process, and the reviewing work was divided into two catalogues: twin-wire GMAW process and tandem GMAW process, which were two main energy delivery and control formations of the process. This part focused on some negative phenomena, such as arc interruption, during the welding process and their effects on the products quality and process stability. Some works about using different current waveforms combinations and their effects on the welding process were discussed. Also, the works of numerical simulation for this process were mentioned in this part. The second part was about process stability monitoring, and corresponding works focused on establishing quantitative process stability estimation model. The last was the improvement of process control method, and the works mainly focused on the adjustment and modulation of the current waveforms, in order to eliminate negative phenomena, improve the process stability and obtain the products with high quality. Finally, some suggestions about future works were presented. This paper can provide references and enlightens for current academic researches or actual industrial applications in the double-wire GMAW process relative areas.INDEX TERMS Double-wire GMAW process, arc interruption, process stability, current waveform.

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Direct quality prediction in resistance spot welding process: Sensitivity, specificity and predictive accuracy comparative analysis

Cited by 24 publications

References 34 publications

Comparative Study of Classification Algorithms for Quality Assessment of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs

Comparative Study of Classification Algorithms for Quality Assessment of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding

Review of the Recent Trends of Process Monitoring and Control for Double- Wire GMAW Process

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