Research of Self-learning of Johnson-Cook Models Parameters based on Genetic Algorithm

DOI

10.2991/ifmca-16.2017.115How to use a DOI?

Keywords

plate, rolling, genetic algorithm, self-learning

Abstract

The Johnson-Cook models parameters of deformation resistance determine the prediction accuracy of rolling force during hot rolling. According to the influencing factors analysis of rolling force calculation error, the genetic algorithm was introduced into the self-learning method of Johnson-Cook models parameters, and searches the models optimal value on the basic of space exploration and optimization ability of genetic algorithm. The decision variable selection, the coding and decoding, the fitness evaluation and the terminal conditions process were implemented during development process of self-learning system. The results show that the optimization accuracy and speed can meet industrial production requirement.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunyu He
AU  - Zhijie Jiao
AU  - Shaojie Wang
AU  - Fuxiang Zhao
PY  - 2017/03
DA  - 2017/03
TI  - Research of Self-learning of Johnson-Cook Models Parameters based on Genetic Algorithm
BT  - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
PB  - Atlantis Press
SP  - 743
EP  - 746
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifmca-16.2017.115
DO  - 10.2991/ifmca-16.2017.115
ID  - He2017/03
ER  -