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.
Highlights
Manufacturers in the automotive industry face an increasingly competitive environment [1]
The number of resistance spot welding (RSW) joints per vehicle is very high, and there can be significant variability in the quality of each of them due to the fact that RSW is a complex process [6], [7]; more precisely, the heat generated by an electrical current has to be substantial enough to promote local melting and the formation of a weld nugget at the faying interface [8], [9], while at the same time the amount of heat is influenced by the electrical conductivity
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]
Summary
Manufacturers in the automotive industry face an increasingly competitive environment [1]. The number of RSW joints per vehicle is very high (around 5000 according to Xia et al [5]), and there can be significant variability in the quality of each of them due to the fact that RSW is a complex process [6], [7]; more precisely, the heat generated by an electrical current has to be substantial enough to promote local melting and the formation of a weld nugget at the faying interface [8], [9], while at the same time the amount of heat is influenced by the electrical conductivity. 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].
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