Maximum Thinning Rate Prediction of Friction Heat Single-Point Incremental Forming for AZ31B Magnesium Alloy Based on BP Neural Network

Abstract

The purpose of this project is to build an accurate prediction model for the maximum thinning rate of the truncated cone made of magnesium alloy AZ31B by single-point incremental forming. A truncated cone’s thickness distribution is analyzed using ABAQUS. Orthogonal experiments and supplementary data were performed as training samples within the maximum refinement rate obtained from the finite element (FE) results. Back-propagation (BP) neural network is established by taking into account tool diameter, initial sheet thickness, and forming angle as input responses, and maximum thinning rate as output responses, and nine hidden layers are involved. The results showed that the incidence of process parameters on the maximum thinning rate of the truncated cone is in the order of forming angle, initial thickness of sheet, and diameter of tool from large to small; The correlation coefficient of BP neural network model training [Formula: see text]. The test results and FE results contrast, and the average error rate of the maximum thinning rate is only 0.97%. The results indicate that the model is reliable and accurate for the prediction of incremental forming, and it is a useful reference value.