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Optimization of CVC shifting mode for hot strip mill based on the proposed LightGBM prediction model of roll shifting

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A Correction to this article was published on 13 July 2021

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Abstract

In the routine roll shifting mode, work rolls of the continuous variable crown (CVC) hot strip mill are always in repeated shifting positions, which affects the uniform wear of work rolls. As an available solution to the above problem, a new random shifting mode for CVC work rolls has been developed in this paper. According to the relationship between shifting position and bending force, the new CVC shifting mode shifts work rolls in a random pattern within the limits by randomly changing the bending force, so that the roll shifting is dispersed and the strip shape remains good. The Light Gradient Boosting Machine (LightGBM) algorithm is applied to build the prediction models of CVC shifting to accurately express the relationship between shifting position and bending force. Random search and Bayesian optimization are used to optimize the LightGBM models, respectively. By comparison, LightGBM with Bayesian optimization is recommended to predict roll shifting, which is more accurate and efficient than using random search. The new CVC shifting mode has been implemented by an off-line application in the 1780 mm hot rolling line. The results reveal that the proposed CVC shifting mode can well disperse roll shifting positions and accurately control strip shape.

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Data availability

My manuscript has no associated data in data repositories and the data was collected from a 1780 mm hot steel strip rolling line in China and will not be deposited in a repository.

Code availability

The calculation program was self-coded and run on JetBrains PyCharm Community Edition 2019.1 x64.

Change history

Abbreviations

AE:

absolute error

AI:

artificial intelligence

ASR:

asymmetry self-compensating work roll

CSP:

compact strip production

CVC:

continuously variable crown

EFB:

exclusive feature bundling

EI:

expected improvement

FEM:

finite element method

GBDT:

gradient boosting decision tree

GOSS:

gradient-based one-side sampling

GP:

gaussian process

HSS:

high-speed steel

K-WRS:

Kawasaki steel work-roll shifting

LightGBM:

Light Gradient Boosting Machine

MAE:

mean absolute error

MNMSE:

mean of negative mean square error

MSE:

mean square error

RBF:

radial basis function

SFR:

schedule-free rolling

SMS:

Schloemann-Siemag

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Acknowledgements

This work was supported by National Key R&D Program of China (2017YFB0304100), National Natural Science Foundation of China (51704067, 51774084, 51634002).

Funding

This work was supported by National Key R&D Program of China (2017YFB0304100), National Natural Science Foundation of China (51704067, 51774084, 51634002)

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Authors and Affiliations

  1. The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, 110819, Liaoning, China

    Guangtao Li, Dianyao Gong & Dianhua Zhang

  2. HBIS Group Chengsteel Company, Chengde, 067002, Hebei, China

    Junfang Xing

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  1. Guangtao Li
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  2. Dianyao Gong
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Contributions

All authors contributed to the study of this paper. Guangtao Li: Software, Methodology, Formal analysis, Data Curation, Conceptualization, Writing–original draft. Dianyao Gong: Methodology, Conceptualization, Writing–review & editing, Supervision. Junfang Xing: Data collection. Dianhua Zhang: Investigation.

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Correspondence to Guangtao Li.

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The original online version of this article was revised: The original article contained symbol mistakes regarding formulas (10) and (11).

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Li, G., Gong, D., Xing, J. et al. Optimization of CVC shifting mode for hot strip mill based on the proposed LightGBM prediction model of roll shifting. Int J Adv Manuf Technol 116, 1491–1506 (2021). https://doi.org/10.1007/s00170-021-07395-7

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