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

Santos Martín, José Ignacio Martín, Óscar Ahedo García, Virginia Tiedra, Pilar de Galán Ordax, José Manuel 2023-02-06T12:39:01Z 2023-02-06T12:39:01Z 2022-11 0268-3768 http://hdl.handle.net/10259/7401 10.1007/s00170-022-10444-4 1433-3015 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. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors acknowledge fnancial support from the Spanish Ministry of Science, Innovation and Universities (Excellence Network RED2018‐102518‐T), the Spanish State Research Agency (PID2020-118906 GB-I00/AEI/https://doi.org/10. 13039/501100011033), and the Fundación Bancaria Caixa D. Estalvis I Pensions de Barcelona, La Caixa (2020/00062/001). In addition, we acknowledge support from the Santander Supercomputación group (University of Cantabria) that provided access to the Altamira Supercomputer—located at the Institute of Physics of Cantabria (IFCACSIC) and member of the Spanish Supercomputing Network—to perform the diferent simulations/analyses. application/pdf eng Springer Nature The International Journal of Advanced Manufacturing Technology. 2022, V. 123, n. 11-12, p. 4077-4092 https://doi.org/10.1007/s00170-022-10444-4 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018‐102518‐T/ES/SISTEMAS COMPLEJOS SOCIOTECNOLOGICOS/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118906GB-I00/ES/INTERACCIONES DINAMICAS DISTRIBUIDAS: PROTOCOLOS BEST EXPERIENCED PAYOFF Y SEPARACION ENDOGENA/ info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//2020%2F00062%2F001/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Explainable boosting machine Pattern recognition Quality assessment Resistance spot welding AISI 304 austenitic stainless steel Tensile shear load bearing capacity Ingeniería Engineering Glass-box modeling for quality assessment of resistance spot welding joints in industrial applications info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion The International Journal of Advanced Manufacturing Technology 123 11-12 4077 4092