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Optimization of Metal Rolling Control Using Soft Computing Approaches: A Review

  • Ziyu HuEmail author
  • Zhihui Wei
  • Hao Sun
  • Jingming Yang
  • Lixin Wei
Original Paper
  • 108 Downloads

Abstract

As one of the most important structural and functional materials, rolled-product plays an irreplaceable role in national economy, people’s lives and national development. Metallurgy industry is moving from traditional semi-automation to knowledge automation, process intelligence, and manufacturing information. Rolling exerts an essential impact on material properties and product quality as an important part of the production in steel industry. Rolling process is multi-scale, multi-variable, nonlinear and unbalanced with strong coupling and non-steady state. With an increasing rolling speed, more difficulties like process information monitoring, behavior characteristics modeling, and controlling of high speed operating are manifested in high-speed continuous rolling mills. The existing control system of rolling process is difficult to cope with the condition changes of high-speed rolling and the specification changes of complex products. The main reason lies in the fact that the prediction of force parameters is based on traditional mathematical models, and the procedure parameter setting depends on static optimization methods. In order to achieve precise control of large-scale and high-speed rolling, the analysis of rolling process rules based on industrial big data should be considered to establish the dynamic process model, and multi-objective real-time computational method of rolling schedules should also be introduced. Through a summary of steel industry and a review of the history of rolling optimization, the purpose is to explore the relationship between the optimization objectives of the rolling schedule and the process parameters of the rolling process, reveal the rules of how rolling conditions affecting rolling process in high speed rolling and provide theoretical basis and technical support to the production of steel industry.

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61803327, 61703361), Natural Science Foundation of Hebei Province (Grant Nos. F2016203249, E2018203162). The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions to improve the quality of this paper.

Compliance with Ethical Standards

Conflict of interest

The authors declared that we have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Copyright information

© CIMNE, Barcelona, Spain 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringYanshan UniversityQinhuangdaoPeople’s Republic of China

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