Optimization of laser spiral welding using Response surface methodology and genetic algorithms

Abstract

In the laser spiral welding (LSW) process, the welding parameters have a significant impact on the weld quality. In this paper, experiments were conducted and experimental data were collected on galvanized steel sheets using the LSW process, and mathematical models were developed using response surface methodology (RSM) and genetic algorithm (GA) to verify the specific effects of each process parameter on the weld and to perform process optimization. Laser power, welding speed, gap and focal length were selected as the influencing factors, and melt depth, melt width and concave as the output results. In the RSM model we found that the laser power was positively correlated with the weld depth and width, while the welding speed was inversely correlated with the weld depth and width, the gap was positively correlated with the amount of concave, and the focal length had no significant effect on the weld. In the GA model we use a large amount of experimental data for BP neural network training and iterative optimization using a genetic algorithm. Validation experiments were conducted on two models, and the results indicated that the two models had higher accuracy in predicting the welding depth and width compared to predicting the concave. The GA model had an 8% increase in tensile strength and a 25% increase in plasticity of the weld joint obtained from the optimal process compared to the RSM model. The GA model has higher accuracy in optimizing the LSW process.