Glass-box modeling for quality assessment of resistance spot welding joints in industrial applications

Abstract

Resistance spot welding (RSW) is one of the most relevant industrial processes in different sectors. Key issues in RSW are process control and ex-ante and ex-post evaluation of the quality level of RSW joints. Multiple-input–single-output methods are commonly used to create predictive models of the process from the welding parameters. However, until now, the choice of a particular model has typically involved a tradeoff between accuracy and interpretability. In this work, such dichotomy is overcome by using the explainable boosting machine algorithm, which obtains accuracy levels in both classification and prediction of the welded joint tensile shear load bearing capacity statistically as good or even better than the best algorithms in the literature, while maintaining high levels of interpretability. These characteristics allow (i) a simple diagnosis of the overall behavior of the process, and, for each individual prediction, (ii) the attribution to each of the control variables—and/or to their potential interactions—of the result obtained. These distinctive characteristics have important implications for the optimization and control of welding processes, establishing the explainable boosting machine as one of the reference algorithms for their modeling.