Parameter Optimization in Resistance Spot Welding of AISI ۱۰۶۰ Steel Using Adaptive Neural Fuzzy Inference System and Sensitivity Analysis
محل انتشار: مجله شکل دهی مواد، دوره: 8، شماره: 4
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 181
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شناسه ملی سند علمی:
JR_IJMF-8-4_005
تاریخ نمایه سازی: 8 آبان 1400
چکیده مقاله:
Resistance spot welding process of AISI ۱۰۶۰ steel has been experimentally investigated by studying the effects of welding current, electrode force, welding cycle and cooling cycle on tensile-shear strength. Using the response surface methodology, experimental tests are performed. An adaptive neural-fuzzy inference system is applied to model and predict the behavior of tensile-shear strength. Additionally, the optimal parameters of adaptive neural-fuzzy inference systems are obtained by the gray wolf optimization algorithm. For modeling the process behavior, the results of experiments have been employed for training (۷۰% of data) and testing (۳۰% of data) of the inference system. The results show that the applied network has been very successful in predicting the tensile-shear strength and the coefficient of determination and mean absolute percentage error for the test section data are ۰.۹۶ and ۶.۰۲%, respectively. This indicates the considerable accuracy of the employed model in the approximation of the desired outputs. After that, the effect of each input parameter on tensile-shear strength is quantitatively evaluated with the Sobol sensitivity analysis method. The results show that the tensile-shear strength of the joint rises by increasing the welding current and welding cycle and also decreasing the electrode force and cooling cycle.
کلیدواژه ها:
Resistance spot welding ، AISI ۱۰۶۰ steel ، Adaptive neural-fuzzy inference system ، Gray wolf optimization algorithm ، Sobol sensitivity analysis method
نویسندگان
Mehdi Safari
Department of Mechanical Engineering, Arak University of Technology, Arak, ۳۸۱۸۱-۸۴۱۱, Iran
Amir Hossein Rabiee
Department of Mechanical Engineering, Arak University of Technology, Arak, ۳۸۱۸۱-۸۴۱۱, Iran
Vahid Tahmasbi
Department of Mechanical Engineering, Arak University of Technology, Arak, ۳۸۱۸۱-۸۴۱۱, Iran