A study on on-line learning neural network for prediction for rolling force in hot-rolling mill

Abstract

Steel manufacturers are under pressure to improve their productivity and to optimize their process parameters to maximum efficiency and quality. Indeed, one of the keys to achieve this goal is the automation of the steel-making process using AI (artificial intelligence) techniques. The automation of hot-rolling process requires the developments of several mathematical models for simulation and quantitative description of the industrial operations involved. The mathematical modeling of hot-rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, an on-line learning neural network for both long-term learning and short-term learning was developed in order to improve the prediction of rolling force in hot-rolling mill. This analysis shows that the predicted rolling force agrees with very close to the practical rolling force, and the thickness error of the strip is considerably reduced.