Application of back-propagation neural network for controlling the front end bending phenomenon in plate rolling

Abstract

In this paper, a new method of controlling the front end bending in plate rolling is introduced. In this method, the back-propagation neural network with three layers is designed. The input layer has three inputs of temperature, entry thickness, and parameter of deformation zone. The hidden layer has seven neurons. The only value of the output layer is the front end bending value. The optimised rolling schedule at different conditions is obtained after training and calculating. Its application to a plate rolling mill shows that the method solves the problem of the front end bending successfully.