Title: Modelling and intelligent optimisation for hot-rolling roll changes of high-speed tool steel rod and wire

Authors: Jinxiang Chen; Yilan Yin; Can Li; Yanjin Chen; Jinjun Song; Yimin Shi

Addresses: State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Iron and Steel Green and Intelligent Center, China Iron and Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron and Steel Research Institute Group, Beijing, 100081, China ' State Key Laboratory of Hybrid Process Industry Automation Systems and Equipment Technology, Automation Research and Design Institute of Metallurgical Industry, China Iron and Steel Research Institute Group, Beijing, 100081, China ' HEYE Special Steel Co., Ltd., Shijiazhuang, 050031, China ' HEYE Special Steel Co., Ltd., Shijiazhuang, 050031, China ' HEYE Special Steel Co., Ltd., Shijiazhuang, 050031, China

Abstract: The production of high-speed tool steel rod and wire is characterised by multiple varieties and small batches. The optimisation of roll change times of hot continuous rolling mill must be considered during production scheduling. Otherwise, frequent roll changes will lead to the decrease of work efficiency and the huge increase of production cost. In order to solve the above problems, an optimal model with multi-constraints is established, and a HACA to optimise the number of roll changes is presented by considering various factors of the hot continuous rolling and roll changing process in this paper. By comparing and analysing the single model GA, ACA, and empirical method in a special steel factory, it can be found that the optimised roll change times are better than the original empirical roll change times, which improves the production efficiency and reduces the labour cost.

Keywords: high-speed tool steel rod and wire; hot continuous rolling; roll changes optimisation; ant colony algorithm; genetic algorithm.

DOI: 10.1504/IJMIC.2021.122466

International Journal of Modelling, Identification and Control, 2021 Vol.38 No.1, pp.74 - 80

Received: 12 Dec 2020
Accepted: 25 Jan 2021
Published online: 26 Apr 2022 *

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