A Thermal–Elastic–Plastic Constitutive Model using the Radial Basis Function Neural Network and Application for an Energy Efficient Warm Forming Process

Abstract

This work presents a thermal–elastic–plastic constitutive equation based on the radial basis function (RBF) artificial neural network and application with the finite element (FE) analysis. In order to capture the stress data in the coupled temperature-strain doamin, a constitutive equation was defined based on the RBF model, and the trained model was validated by test data that were not used in the training. The RBF based constitutive model was then combined with the stress integration and tangent modulus formulation of FE analysis to apply the new model to a warm V-bending process that includes elastic–plastic deformation and elastic recovery under non-isothermal conditions. The heating method was the infrared (IR) local heating method. The results show that the RBF constitutive model can provide good agreement with the experimental data for V-bending in the non-isothermal conditions. The effects of the parameters of the basis function are also discussed in this work.