Optimization of welding parameters in resistance spot welding of AZ61 Mg alloy

The main purpose of this study is optimization of resistance spot welding of AZ61 Mg alloy to achieve maximum nugget size and minimum tensile residual stresses in welding area. Since the stability and strength of a welded structure is strongly dependent on the nugget size and the residual stresses, an integrated artificial neural networks and genetic algorithm is utilized to optimize the welding parameters. In this study, the resistance spot welding process is simulated by a 2D fully coupled structural-electrical-thermal finite element model. The finite element model is developed to predict of the nugget size and the residual stresses in the welding area. In order to validate the FE model, the results are compared with the obtained results from the experimental tests. The results show that the FE model has a good agreement with the experimental tests. The input parameters for optimization are: electrical current, welding time and electrode force. The results show that the integrated optimization algorithm is successful in determining the optimized welding parameters. Based on the optimization results, the maximum nugget size 6.33 mm and the minimum tensile residual stress 228 MPa are achievable by using 16kA, 16 Cycles and 848 N as current, welding time and electrode force respectively.