A Reverse Approach in Optimizing Pass Parameters

Abstract:

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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 and 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:

$35.00

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