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Wear prediction model of hot rolling backup roll based on FEM + ML algorithm

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Abstract

The wear of backup rolls will have a great impact on the quality of the shape of hot rolled strip sheet. In order to overcome the limitations of the finite element method (FEM) in calculating backup roll wear in terms of efficiency and accuracy, this paper proposes a tandem FEM + ML hybrid model to optimise the predictive effect of the finite element method (FEM) on backup roll wear. Firstly, a backup roll wear model based on FEM is established. Secondly, in order to select the optimal machine learning (ML) algorithm as the finite element error compensation model, three types of finite element error compensation models were established based on the random forest (RF) algorithm, the radial basis function (RBF) neural network algorithm, and the particle swarm optimisation support vector machine (PSO-SVM) algorithm. Finally, the three types of finite element error compensation models were connected in series with the FEM model to compare the prediction performance of the three types of FEM + ML models on backup roll wear. The numerical experimental results show that the FEM + PSO-SVM model can better predict the wear of the backup roll, and the PSO-SVM algorithm is the most suitable for building the finite element error compensation model. It is proved that the FEM + ML model proposed in this paper can effectively improve the accuracy and computational efficiency of the FEM model for predicting backup roll wear without adding microelements. In addition, among the hot rolling parameters, the rolling force has the greatest influence on the backup roll wear, and excessive rolling force for a single pass should be avoided to slow down the backup roll wear.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos. 52074242 and U20A20187), the Central Guiding Local Science and Technology Development Special Fund Project (Grant No. 216Z1602G), the Natural Science Foundation of Hebei Province (Grant No. E2020203068), the fund of the State Key Laboratory of Rolling and Automation (Grant No. 2022RALKFKT001), and the Liao Ning Revitalization Talents Program of Liao Ning Province (No. XLYC2007087). L.S. is very grateful for the financial support from the Australian Research Council (ARC) through Discovery Early Career Researcher Award (DECRA) fellowship (Grant No. DE180100124). G.D. would like to acknowledge the support from the University of Queensland (UQ) through the UQ Research Stimulus Allocation Fellowship.

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Authors and Affiliations

  1. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinhuangdao, 066004, China

    Jia Lu, Luhan Hao, Pengfei Wang & Huagui Huang

  2. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang, 110819, China

    Xu Li

  3. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Changchun Hua

  4. School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW, 2522, Australia

    Lihong Su

  5. School of Mechanical and Mining Engineering, University of Queensland, St Lucia, QLD, 4072, Australia

    Guanyu Deng

Authors
  1. Jia Lu
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  2. Luhan Hao
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  3. Pengfei Wang
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  4. Huagui Huang
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  5. Xu Li
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  6. Changchun Hua
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  8. Guanyu Deng
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Contributions

All authors contributed to the study conception and design. Jia Lu has established the optimisation method, designed the experiments, and written the program and the manuscript; Luhan Hao and Pengfei Wang have conducted the experiments and analysed and arranged data; Huagui Huang has organised the project and reviewed the manuscript; Xu Li and Changchun Hua have conducted the experiments and collected and analysed data; Guanyu Deng and Lihong Su have reviewed the manuscript. All authors read and approved the final manuscript.

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Correspondence to Pengfei Wang, Xu Li or Guanyu Deng.

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Lu, J., Hao, L., Wang, P. et al. Wear prediction model of hot rolling backup roll based on FEM + ML algorithm. Int J Adv Manuf Technol 131, 5923–5939 (2024). https://doi.org/10.1007/s00170-024-13311-6

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