Improved knowledge-based neural network (KBNN) model for predicting spring-back angles in metal sheet bending

Abstract

We develop an efficiently improved knowledge-based neural network (KBNN) associated with optimization algorithms and finite element analysis (FEA) to accurately predict spring-back angles in metal sheet bending. The well-known V and U prevalent processes of bending are considered. The KBNN predictive results are based on the empirical model and artificial neural network (ANN) modeling. The empirical model is constructed from the FEA results using response surface method, while the multilayer perceptron is employed to create the ANN. The trained KBNN can accurately model the relationship between the spring-back angles and process parameters. The obtained results are validated against other existing methods showing a high accuracy.