Abstract: The residual tensile stress generated during the processing operations of laser welding in 45Mn sheet has adverse effects on their strength,toughness,and fatigue life.Various ultrasonic impact processes were employed to enhance the surface of the sheet and a multi-objective optimization method for the ultrasonic impact process parameters of sheet was proposed. Firstly,a dataset of surface residual stress under different impact process parameters was obtained through finite element impact simulation.Then,based on the simulation dataset,a nonlinear mapping relationship between impact process parameters and surface residual stress was successfully established using the IDBO-BP neural network. Comparing the IDBO-BP neural network with BP,GA-BP,PSO-BP,and DBO-BP neural networks,it was found that the IDBO-BP neural network achieves higher accuracy in predicting surface residual stress of the sheet,with MAE and R² evaluation metrics reaching 0.068 3 and 0.997 4,respectively,indicating the effectiveness of the model in predicting residual stress after ultrasonic impact. Finally,considering the ultrasonic impact process parameters as design variables and aiming for minimal residual stress,minimal impact current,and minimal impact time,a Pareto optimal solution set of residual stress,impact current,and impact time corresponding to the ultrasonic impact process parameters was obtained by combining the IDBO-BP neural network and the PSO algorithm. The results demonstrate that the optimized impact process effectively improves processing efficiency and processing energy efficiency.

Key words: 45Mn, ultrasonic impact treatment, Improved Dung Beetle Optimizer(IDBO), residual stress, multi-objective optimization