LAUSR

Shot peening is an important process for the forming in aerospace industries. However, it is difficult to design the process parameters because of the complex nonlinear relationship between shot peening parameters and the deformation response of shot peened components which is affected by various nonlinear factors such as geometric nonlinearity, material nonlinearity, and coupling effects between process parameters. In this paper, a back propagation artificial neural network (BP-ANN) optimized by genetic algorithm (GA) method, called GA-ANN, is presented for the prediction of shot peen forming parameters. GA is applied to optimize the initial parameters of the BP-ANN to avoid falling into local minimum error. The initially optimized BP-ANN is then directly used to simulate the complex nonlinear relationships between the shot peening parameters (e.g. air pressure, shot mass flow, workpiece feeding speed, etc.) and the mechanical responses of the workpiece (e.g. the bending radius of target workpiece). The experimental results show that the shot peen forming process parameters can be effectively predicted by BP-ANN and that the prediction accuracy can be significantly improved when the ANN model is optimized first using a GA algorithm.