2024-03-29T00:10:59Zhttps://riubu.ubu.es/oai/requestoai: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
Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets
Martín, Óscar
Ahedo García, Virginia
Santos Martín, José Ignacio
Tiedra, Pilar de
Galán Ordax, José Manuel
Resistance spot welding
AISI 304 stainless steel
Tensile shear load bearing capacity
Quality assessment
Elastic nets
Smoothing splines
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.
2022-01-25T13:15:38Z
2022-01-25T13:15:38Z
2016-10
info:eu-repo/semantics/article
0921-5093
http://hdl.handle.net/10259/6358
10.1016/j.msea.2016.08.112
eng
Materials Science and Engineering: A. 2016, V. 676, p. 173-181
https://doi.org/10.1016/j.msea.2016.08.112
info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2008-2011/CSD2010-00034/ES/Social and environmental transitions: simulating the past to understand human behaviour
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Elsevier