Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling

In this paper, an Artificial Neural Network (ANN) is used to investigate the influence of rolling parameters such as thickness reduction, inter-strand tension, rolling speed and friction on the rolling force, rolling power, and slip of tandem cold rolling. For this reason, the rolling power was derived for 195 various experiments through a series of observation tests. The network is trained and tested using real data collected from a practical tandem rolling line. The best topology of the ANN is determined by Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm and error, and nine neurons in the hidden layer had the best performance. The average of the training, testing, and validating correlation coefficients data sets are mentioned 0.947, 0.924, and 0.943, respectively. The obtained results show MSE value 4.2 x 10-4 for predicting slip. In addition, the effect of friction and angular velocity condition on the cold rolling critical slip phenomena are investigated. The results show that ANNs can accurately predict the cold rolling parameters considered in this study. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper uses an Artificial Neural Network (ANN) to investigate the influence of rolling parameters on the rolling force, rolling power, and slip of tandem cold rolling. Real data collected from a practical tandem rolling line is used to train and test the network. The best topology of the ANN is determined, and the results show accurate prediction of the cold rolling parameters considered in this study.