2026-07-15T13:42:30Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/63582022-11-30T11:20:09Zcom_10259_6336com_10259_6335com_10259.4_106com_10259_2604com_10259_3830com_10259_5086col_10259_6337col_10259_3832

00925njm 22002777a 4500 dc Martín, Óscar author Ahedo García, Virginia author Santos Martín, José Ignacio author Tiedra, Pilar de author Galán Ordax, José Manuel author 2016-10 In this work, the quality of resistance spot welding (RSW) joints of 304 austenitic stainless steel (SS) is assessed from its tensile shear load bearing capacity (TSLBC). A predictive model using a polynomial expansion of the relevant welding parameters, i.e. welding current (WC), welding time (WT) and electrode force (EF) and elastic net regularization is proposed. The predictive power of the elastic net approach has been compared to artificial neural networks (ANNs), previously used to predict TSLBC, and smoothing splines in the framework of a generalized additive model. The results show that the predictive and classification error of the elastic net model are statistically comparable to benchmarks of the best pattern recognition tools whereas it overcomes correlation problems and performs variable selection at the same time, resulting in a simpler and more interpretable model. These features make the elastic net model amenable to be used in the design of welding conditions and in the control of manufacturing processes. 0921-5093 http://hdl.handle.net/10259/6358 10.1016/j.msea.2016.08.112 Resistance spot welding AISI 304 stainless steel Tensile shear load bearing capacity Quality assessment Elastic nets Smoothing splines Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets