Multi-objective Optimization of Resistance Spot Welding Parameters of BH340 Steel Using Kriging and NSGA-III

Improving efficiency and productivity has become paramount in the manufacturing industry in recent years. To achieve this, manufacturing defects must be minimized, and better materials and processes must be implemented. One of the most popular joining processes in the automotive industry is resistance spot welding (RSW). Evaluating the quality of the spot welds is time-consuming, and much material is wasted. Therefore, the welding process must be optimized to improve weld quality and save time and material. The effects of the critical welding parameters [welding current (WC), welding time (WT) and electrode pressure (EP)] on the weld quality are studied along with the microstructure and microhardness of the galvannealed BH340 steel during the RSW process. The weld quality indicators studied are nugget diameter (ND), tensile shear strength (TSS), peel strength (PS) and mean dynamic contact resistance. It was found that the WC had the highest impact on the weld quality, followed by WT and EP. In combination with Kriging, the non-dominated sorting genetic algorithm III (NSGA-III) has been used for the multi-objective optimization of the RSW process. The results obtained after the optimization have been validated experimentally. When the optimal welding parameters were used, it improved the welded specimen's ND, TSS and PS by 9.21%, 4.95% and 7.69%, respectively. It was also found that the EP needs to be reduced to 2 kN to produce welds with a large ND and high strength. The microstructure of the welded sample revealed the presence of martensite in the fusion zone and the heat-affected zone, which attributes to the high microhardness in these zones.