Research on stamping forming prediction of aluminum alloy sheet based on RBF neural network

Abstract

In order to accurately predict and reduce the possible defects in the stamping process of an aluminum alloy sheet, the simulation data of the sheet thickness for the 6016 aluminum alloy in the stamping process were obtained by the Hill’48 yield criterion based on finite element ABAQUS/Explicit solver. Taking blank holder force, friction coefficient, stamping speed, and die clearance as input parameters, the radial basis function (RBF) network model for predicting the maximum thinning rate of the stamping aluminum alloy sheet was established. The results show that the RBF network model constructed in this paper has high precision and can reflect the complex relationship between the stamping process parameters and the maximum thinning rate well by comparing the finite element simulation and neural network prediction results. It is of great significance to improve the optimization efficiency of the stamping process of the aluminum alloy sheet and reduce the actual experimental cost.