Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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

Authors
Chunyu He, Zhijie Jiao, Shaojie Wang, Fuxiang Zhao
Corresponding Author
Chunyu He
Available Online March 2017.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)
Series
Advances in Engineering Research
Publication Date
March 2017
ISBN
978-94-6252-307-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/ifmca-16.2017.115How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - https://doi.org/10.2991/ifmca-16.2017.115
ID  - He2017/03
ER  -