Parameter Optimization in Resistance Spot Welding of AISI 1060 Steel Using Adaptive Neural Fuzzy Inference System and Sensitivity Analysis

Document Type : Research Paper

Authors

Department of Mechanical Engineering, Arak University of Technology, Arak, 38181-8411, Iran

Abstract

Resistance spot welding process of AISI 1060 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 (70% of data) and testing (30% 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 0.96 and 6.02%, 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.

Keywords

References

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Iranian Journal of Materials Forming
Volume 8, Issue 4
October 2021
Pages 33-45

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