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.
