Treffer: A novel multi-time scale collaborative process parameters optimisation framework for hot strip rolling process.

Title:
A novel multi-time scale collaborative process parameters optimisation framework for hot strip rolling process.
Authors:
Xu, Qingquan1 (AUTHOR), Dong, Jie1,2 (AUTHOR) dongjie@ies.ustb.edu.cn, Peng, Kaixiang1,2 (AUTHOR)
Source:
International Journal of Production Research. Dec2025, Vol. 63 Issue 23, p8963-8983. 21p.
Database:
Business Source Premier

Weitere Informationen

It is important that metal materials forming processes are optimised to improve product qualities and properties. However, information island and multi-time scale problems are formed in complex manufacturing processes, such as hot strip rolling process (HSRP), because of their long process, multi-system, and multi-level, etc. To solve the above problems, a multi-time scale collaborative optimisation framework for properties and shape of HSRP is proposed in this paper. First, the mechanism and data of the rolling process are analysed to establish the mechanical properties model in long time scale and the shape model in short time scale, respectively. Second, mechanical properties and shape optimisation models are established with the constraints of process parameters and equipment performance based on cloud-edge collaborative method to achieve multi-time scale collaborative optimisation. Finally, a multi-objective co-evolutionary sparrow search algorithm is proposed to solve large-scale complex optimisation models for mechanical properties and shape. To validate the effectiveness and superiority of the proposed framework, a prototype system platform of cloud-edge collaboration is built and experiments are conducted in actual HSRP. The results show that the proposed framework can improve the shape and properties of strip steel at the same time. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volltext ist im Gastzugang nicht verfügbar.