Fatigue Life Prediction for SAE9254 Steel Treated by Shot Peening Based on Finite Element Simulation Combining with Neural Network

A stochastic multiple shot peening model based on Python scripts was established by ABAQUS finite element software. The residual stress distribution and surface roughness of SAE9254 steel for suspension springs after shot peening under different shot diameters, different shot velocities, and different shot peening coverage rates were predicted and compared with test results. Based on the finite element simulation results and neural network model, the fatigue life of the test steel was predicted, and experimental verification was carried out. The results show that the simulated curves of residual stress along the depth direction of SAE9254 steel were in good agreement with the test results, the relative error of the maximum residual compressive stress was about 14.77%, and the relative error of surface roughness was about 3.18%, which indicated that the established stochastic multiple shot peening model could accurately predict the residual stress distribution and surface roughness of SAE9254 steel after shot peening. The average relative error between the fatigue life prediction values obtained by the method combining finite element simulation and neural network and the experimental values was 6.85%, indicating that this method could accurately predict the fatigue life of SAE9254 steel.