2026-05-06T10:22:15Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/74012023-03-27T06:55:36Zcom_10259_3830com_10259_5086com_10259_2604col_10259_3832

00925njm 22002777a 4500 dc Santos Martín, José Ignacio author Martín, Óscar author Ahedo García, Virginia author Tiedra, Pilar de author Galán Ordax, José Manuel author 2022-11 Resistance spot welding (RSW) is one of the most relevant industrial processes in diferent 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 tradeof between accuracy and interpretability. In this work, such dichotomy is overcome by using the explainable boosting machine algorithm, which obtains accuracy levels in both classifcation 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. 0268-3768 http://hdl.handle.net/10259/7401 10.1007/s00170-022-10444-4 1433-3015 Explainable boosting machine Pattern recognition Quality assessment Resistance spot welding AISI 304 austenitic stainless steel Tensile shear load bearing capacity Glass-box modeling for quality assessment of resistance spot welding joints in industrial applications