Research of Self-learning of Johnson-Cook Models Parameters based on Genetic Algorithm
Chunyu He, Zhijie Jiao, Shaojie Wang, Fuxiang Zhao
Available Online March 2017.
- https://doi.org/10.2991/ifmca-16.2017.115How to use a DOI?
- plate, rolling, genetic algorithm, self-learning
- 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.
- 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 -