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

Authors: Bin Chen; Xiao Ru Cheng; Yan Sheng Hu; Yong Ren

Addresses: School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai-200240, China ' School of Materials and Metallurgy, Wuhan University of Science and Technology, Wuhan-430081, China ' School of Materials and Metallurgy, Wuhan University of Science and Technology, Wuhan-430081, China ' School of Materials and Metallurgy, Wuhan University of Science and Technology, Wuhan-430081, China

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.

Keywords: back-propagation neural networks; front end bending; plate rolling; temperature; entry thickness; deformation zone.

DOI: 10.1504/IJMPT.2013.056298

International Journal of Materials and Product Technology, 2013 Vol.46 No.2/3, pp.166 - 176

Received: 09 Jul 2012
Accepted: 31 Mar 2013
Published online: 21 Jun 2014 *

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