Error Prediction of Springback in Bending Process Based on GA-BPNN for Accurate Forming Angle

The issue of springback in sheet metal bending has always been of great concern in industrial applications. To reduce its influence on forming angle in bending process., a prediction model based on GA-BPNN (Genetic Algorithm-Backpropagation Neural Network) was proposed and optimized. To train the network., the finite element method was introduced to construct the dataset. Then the parameters in the neural network were determined., and a 5-dimensional input improved model considering material classification was adopted to further improve the prediction accuracy. The experimental results showed that the maximum errors of forming angle reduced from $2.45^{\circ}$ to $0.4^{\circ}$ , and $0.15^{\circ}$ by applying different models.