Multi-objective optimization of the resistance spot welding process using a hybrid approach

Abstract

This study proposed an approach to optimize the process parameters using the entropy weight method combining regression analysis in the resistance spot welding process. Based on the central composite experimental design, tests were carried out with three levels of process parameters for spot-welded titanium alloy sheets. Multiple quality characteristics, namely nugget diameter, maximum displacement, tensile shear load, and failure energy, were converted into a comprehensive welding quality index. The weight for each quality index to obtain the comprehensive welding quality index was determined based on the grey entropy method. The welding heat input for each welding joints was calculated based on the dynamic power signal in the welding process. The mathematical model correlating process parameters and the comprehensive welding quality index was established on the basis of regression analysis. The relationship between the welding process parameters and welding heat was also quantified using a regression model. The effects of welding process parameters on welding quality and welding heat were also discussed. To optimize multi-performance characteristics, the desirability function was employed. The verification test results proved that the method proposed in this paper effectively optimized the welding parameters and kept the welding heat input as low as possible at the same time. Welding current is the most significant parameter affecting the welding quality followed by welding time. This can be owing to its direct influence on the amount of heat supplied to the welding zone during the welding process. The method proposed in this study can serve as a guidance and recommendation for resistance spot welding welders to guarantee welding quality and meet the needs of high production and effective energy saving.