A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness-Reference-Cited by-同舟云学术

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A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness

Published:2023-10-17 Issue: Volume: Page:
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Wang Pengfei,Deng Jinkun,Li Xu,Hua Changchun,Su Lihong,Deng Guanyu^ORCID

Funder

Australian Research Council

National Natural Science Foundation of China

Central Guiding Local Science and Technology Development Special Fund Project

Natural Science Foundation of Hebei Province

the open fund of the State Key Laboratory of Rolling and Automation

Liao Ning Revitalization Talents Program of Liao Ning Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s10845-023-02204-2.pdf

Reference48 articles.

1. Abbaspour, M., & Saboonchi, A. (2008). Work roll thermal expansion control in hot strip mill. Applied Mathematical Modelling, 32, 2652–2669. https://doi.org/10.1016/j.apm.2007.09.011

2. Abidi, M. H., Alkhalefah, H., & Umer, U. (2021). Fuzzy harmony search based optimal control strategy for wireless cyber physical system with industry 4.0. Journal of Intelligent Manufacturing, 33, 1795–1812. https://doi.org/10.1007/s10845-021-01757-4

3. Agarwal, K., & Shivpuri, R. (2015). On line prediction of surface defects in hot bar rolling based on Bayesian hierarchical modeling. Journal of Intelligent Manufacturing, 26, 785–800. https://doi.org/10.1007/s10845-013-0834-y

4. Cortes, C., & Vapnik, V. (1995). Support-vector networks. LMaching Learning, 20, 273. https://doi.org/10.1007/BF00994018

5. Cui, C., Cao, G., Li, X., Gao, Z., Liu, J., & Liu, Z. (2023). A strategy combining machine learning and physical metallurgical principles to predict mechanical properties for hot rolled Ti micro-alloyed steels. Journal of Materials Processing Technology, 311, 117810. https://doi.org/10.1016/j.jmatprotec.2022.117810

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