Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets

MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING

卷:676

Article

Martin, Oscar¹  Ahedo, Virginia²  Ignacio Santos, Jose³  De Tiedra, Pilar⁴  Manuel Galan, Jose³ 

[1] Univ Valladolid, Ingn Proc Fabricac, Escuela Ingn Ind, CMeIM,EGI,ICGF,IM,IPF, Paseo Cauce 59, E-47011 Valladolid, Spain

[2] CSIC, CaSEs Complex & Socioecol Dynam Res Grp, Dept Arqueol & Antropol, Inst Mild & Fontanals, C Egipciaques 15, Barcelona 08001, Spain

[3] Univ Burgos, Escuela Politecn Super, Dept Ingn Civil, INSISOC,Area Org Empresas, Edificio La Milanera,C Villadiego S-N, Burgos 09001, Spain

[4] Univ Valladolid, Ciencia Mat & Ingn Met, Dept CMeIM EGI ICGF IM IPF, Escuela Ingn Ind, Paseo Cauce 59, E-47011 Valladolid, Spain

关键词: Resistance spot welding;    AISI 304 stainless steel;    Tensile shear load bearing capacity;    Quality assessment;    Elastic nets;    Smoothing splines;   

DOI  :  10.1016/j.msea.2016.08.112

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier B.V. All rights reserved.

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