Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints

2019 ◽  
Vol 105 (9) ◽  
pp. 3779-3796 ◽  
Author(s):  
Saeed Zamanzad Gavidel ◽  
Shiyong Lu ◽  
Jeremy L. Rickli
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Resistance Spot ◽  
Welding Joints

scholarly journals Quality Prediction of Resistance Spot Welding Joints From Pre- and Post-Welding Inputs Using Stacking of Tree Ensemble Models

2021 ◽  
Author(s):  
Óscar Martín ◽  
Virginia Ahedo ◽  
José Ignacio Santos ◽  
José Manuel Galán
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Characteristic Curve ◽  
Welding Process ◽  
Quality Level ◽  
Ensemble Models ◽  
Resistance Spot ◽  
Single Output ◽  
Combined Use ◽  
Welding Joints

Abstract 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-single-output 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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