Modeling Hot Rolling Based on RBF Neural Networks and Thermo-Mechanical Coupling FEM

Abstract:

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In the modeling of rolling process, the result from FEM is influenced by the accuracy of the resistance of deformation model of material which is often from the experiment using isothermal compression at elevated temperatures. But the state under high temperature is unstable in lab. And the flow stress model under the lab condition is not consistent to the hot rolling field condition. This paper will employ the RBF artificial neural networks to train the random sample from the field data and get the relation between the rolling force and the parameters of hot rolling. The array of the rolling force and temperature under special condition can be then obtained and the resistance of deformation model will be built by applying the S.EKLUND formula. Finally, the thermo-mechanical coupling FEM with the resistance of deformation model got from RBF method is used to simulate the hot rolling process. The simulated experiment shows the modeling data is fit to the field data.

Info:

Periodical:

Advanced Materials Research (Volumes 443-444)

Edited by:

Li Jian

Pages:

9-14

DOI:

10.4028/www.scientific.net/AMR.443-444.9

Citation:

Z. Q. Xiong et al., "Modeling Hot Rolling Based on RBF Neural Networks and Thermo-Mechanical Coupling FEM", Advanced Materials Research, Vols. 443-444, pp. 9-14, 2012

Online since:

January 2012

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Price:

$35.00

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