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

Z.Y Guo,J.N Sun,F.S Du

doi:10.17159/2411-9717/2016/v116n1a7

Abstract

��-/%*0* 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.

Highlights

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
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

Summary

Conditions of hot rolling experiments employed for the simulation

Q sets of input and output data are given to the neural network. The input variables and the simulation results were used for training samples through varying the FE model parameters. A three-level full factorial design creates 3n training data samples, where n is the number of variables. The first subset of 195 randomly selected data was the training set used to adjust the network weights and biases. In order to increase the training speed and to improve convergent behaviour, the data was normalized before developing the net. One of the most important tasks in ANN studies is to determine the transfer function and the training function In this model, the tangential sigmoidal function was used in the hidden and output layers, which can improve the accuracy and efficiency of the ANN model. The L–M algorithm was selected as the training function as it is the best choice for moderate-sized neural networks and it has the fastest convergence rate (Hagan et al, 1994)

In put variables

Network Transfer function Training function Learning function

Parameters of testing samples