Application of finite element method and artificial neural networks to predict the rolling force in hot rolling of Mg alloy plates

Abstract

A computational model combining a finite element method (FEM) with an artificial neural network (ANN) was developed to predict the rolling force in the hot rolling of Mg alloy plates. FEM results were compared with experimental data to verify the accuracy of the finite element model. Numerous thermomechanical finite element simulations were carried out to obtain a database for training and validation of the network. The input variables were initial thickness, thickness reduction, initial temperature of the plate, friction coefficient in the contact area, and rolling speed. The optimal ANN model was obtained after repeated training and studying of the samples. The trained network gave satisfactory results when comparing the ANN predictions and FEM simulation results. A comprehensive validation of the prediction model is presented. The resulting ANN model was found to be suitable for online control and rolling schedule optimization in the hot rolling process of Mg alloy plate.