TY - JOUR ID - 4648 TI - Resistance Spot Welding Process of AISI 304 Steel: Application of Sensitivity Analysis and ANFIS-GWO Methods JO - Journal of Stress Analysis JA - JRSTAN LA - en SN - 2588-2597 AU - Safari, M. AU - Rabiee, A.H. AU - Tahmasbi, V. AD - Department of Mechanical Engineering, Arak University of Technology, Arak, Iran. Y1 - 2022 PY - 2022 VL - 6 IS - 2 SP - 21 EP - 29 KW - Resistance spot welding KW - Adaptive neural-fuzzy inference system KW - Gray wolf optimization algorithm KW - Sobol sensitivity analysis method KW - AISI 304 steel DO - 10.22084/jrstan.2022.25065.1195 N2 -  For the Resistance Spot Welding (RSW) process, the effects of Welding Current (WC), Electrode Force (EF), Welding Cycle (WCY), and Cooling Cycle (CCY) on the Tensile-Shear Strength (TSS) of the joints have been experimentally investigated. An Adaptive Neural-Fuzzy Inference System (ANFIS) based on data taken from the test results were developed for modelling and predicting of TSS of welds. Optimal parameters of ANFIS system were extracted by Gray Wolf Optimization (GWO) algorithm. The results show that ANFIS network can successfully predict the TSS of RSW welded joints. It can be concluded that the coefficient of determination and mean absolute percentage error for the test section data is 0.97 and 2.45% respectively, which indicates the high accuracy of the final model in approximating the desired outputs of the process. After modeling with ANFIS-GWO, the effect of each input parameter on TSS of the joints was quantitatively measured using Sobol sensitivity analysis method. The results show that increasing in WC, WCY, EF, and CCY leads to an increase in TSS of joints.   UR - https://jrstan.basu.ac.ir/article_4648.html L1 - https://jrstan.basu.ac.ir/article_4648_95bc17c3b2ffa5e2d03880015c0d850b.pdf ER -