Adaptive learning prediction on rolling force in the process of reversible cold rolling mill

Abstract

Rolling force model is a basic model of cold rolling process control system, and the main influential factors on forecasting accuracy of rolling force are the material deformation resistance and friction coefficient. Bland-Ford-Hill model which is the classic rolling force model of cold rolling process is selected to calculate rolling force. Five methods of calculating friction coefficient are explained. Deformation resistance is calculated by the deformation resistance formula derived through Bland-Ford-Hill formula, and its model parameters are acquired through the least squares method. Then the friction coefficient and the deformation resistance are substituted into the rolling force model to calculate rolling force. By comparing with the actual rolling force, the method 4 can make the average error smaller. therefore, method 4 is selected to establish the model library of friction coefficient and deformation resistance of different kinds of steels. Finally, the model parameters adaptive learning method is proposed to improve the prediction precision of rolling force in this paper. © Springer-Verlag Berlin Heidelberg 2012.