Paper Title:
A Reverse Approach in Optimizing Pass Parameters
  Abstract

A method combines a back propagation neural networks (BPNN) with the data obtained using finite element method (FEM) is introduced in this paper as an approach to solve reverse problems. This paper presents the feasibility of this approach. FEM results are used to train the BPNN. Inputs of the network are associated with dimension deviation values of the steel pipe, and outputs correspond to its pass parameters. Training of the network ensures low error and good convergence of the learning process. At last, a group of optimal pass parameters are obtained, and reliability and accuracy of the parameters are verified by FEM simulation.

  Info
Periodical
Advanced Materials Research (Volumes 113-116)
Edited by
Zhenyu Du and X.B Sun
Pages
1707-1711
DOI
10.4028/www.scientific.net/AMR.113-116.1707
Citation
J. H. Hu, Y. H. Shuang, "A Reverse Approach in Optimizing Pass Parameters", Advanced Materials Research, Vols. 113-116, pp. 1707-1711, 2010
Online since
June 2010
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Price
$32.00
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