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

J S Xia; Mohamad Khaje Khabaz; Indrajit Patra; Imran Khalid; José Ricardo Nuñez Alvarez; Alireza Rahmanian; S Ali Eftekhari; Davood Toghraie

doi:10.1016/j.isatra.2022.06.009

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

ISA Trans. 2023 Jan:132:353-363. doi: 10.1016/j.isatra.2022.06.009. Epub 2022 Jun 17.

Authors

J S Xia¹, Mohamad Khaje Khabaz², Indrajit Patra³, Imran Khalid⁴, José Ricardo Nuñez Alvarez⁵, Alireza Rahmanian⁶, S Ali Eftekhari⁷, Davood Toghraie⁸

Affiliations

¹ College of Mechanical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, 224051, China. Electronic address: jasonxia88@yeah.net.
² Young Researchers and Elite Club, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran. Electronic address: khajekhabaz.m@gmail.com.
³ An Independent Researcher, PhD from NIT Durgapur, West Bengal, India.
⁴ Department of Agricultural Extension, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
⁵ Department of Energy, Universidad de la Costa, Barranquilla, Colombia.
⁶ Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
⁷ Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran.
⁸ Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Iran. Electronic address: Toghraee@iaukhsh.ac.ir.

PMID: 35773061
DOI: 10.1016/j.isatra.2022.06.009

Abstract

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 × 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.

Keywords: Perceptron feed-forward ANN; Rolling power and slip prediction; Tandem cold rolling.