Development and Application of Self-learning Model for HSM
- 10.2991/iccia-17.2017.15How to use a DOI?
- Hot strip rolling, Profile and flatness control, Self-learning.
In recent years, based on maturity of AGC technology, the accuracy of Thickness Control has been improved greatly. The poor profile control is the major factor which restricts the dimensional accuracy. The mathematical model of plate shape-setup is the basis for realizing the process control of strip mill. Because the deviation of the mathematical model, the device and the parameter is inevitable during actual rolling, self-learning is an important mean to reduce the error of the set-up model. On a production line, the self-learning algorithm is not in line with actual production, which results in control chaos. To solve this problem, combined with the actual production process, a new self-learning algorithm has been developed, which considers operator revision, unstable error interference and actual parameters optimization. New algorithms has been applied, it has solved the phenomenon of self-learning disorder, improved the hit rate of profile greatly, and improved the quality of the production.
- © 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/).
Cite this article
TY - CONF AU - Ce Wang AU - Ziying Liu AU - Lijie Dong AU - Changli Zhang AU - Fengqin Wang PY - 2016/07 DA - 2016/07 TI - Development and Application of Self-learning Model for HSM BT - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 99 EP - 103 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.15 DO - 10.2991/iccia-17.2017.15 ID - Wang2016/07 ER -