Skip to main content
SpringerLink
Log in

Online segmented thickness prediction of hot rolling strip based on IBA-XGBoost

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

An online segmented thickness prediction algorithm for steel strips based on machine learning is proposed to address issues of strong coupling and low accuracy in existing mathematical thickness models. Firstly, the rolling data are divided into stages of steel biting, accelerated rolling, stable rolling, and steel throwing according to the rolling process. Secondly, an online thickness prediction model with eXtreme gradient boosting (XGBoost) algorithm is established by using segmented data. Then, an improved bat algorithm is applied to optimize the XGBoost model. After that, an adaptive self-learning adjustment method based on the PI closed-loop feedback method is deployed to upgrade the correction speed of the IBA-XGBoost thickness prediction model. Finally, the predicted results are compared with the actual thickness to verify the modeling accuracy. The experimental results indicate that the online segmented thickness prediction model can achieve high accuracy while satisfying time requirements. When IBA-XGBoost uses exit thickness specifications of 3 mm, 4 mm, and 11.45 mm strip rolling data to predict the actual strips thickness, the root mean square error of predicted results is 9.1 μm, 10.3 μm, and 21.8 μm. The results can serve as feedback for the existing automatic gauge control system to further enhance the capability of the thickness control system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Edwin, B.: World Steel in Figures 2023. World Steel Association. https://worldsteel.org/steel-topics/statistics/world-steel-in-figures-2023/ (2023)

  2. Wang, Q.N., Song, L.B., Zhao, J.W., Wang, H.Y., Dong, L.J., Wang, X.C., Yang, Q.: Application of the gradient boosting decision tree in the online prediction of rolling force in hot rolling. Int. J. Adv. Manuf. Technol.. 125, 387–398 (2022). https://doi.org/10.1007/s00170-022-10716-z

    Article  Google Scholar 

  3. Li, D., Liu, J. C., Tan, S. B., Yu, X., Zhang, C. J.: A new monitor-AGC system in hot continues rolling. In: Proceedings of the 33rd Chinese Control Conference Nanjing China. 2014: 6319–6323 (2014)

  4. Tan, S. B., Liu J. C.: Research on mill modulus control of strip rolling AGC systems. In: Proceedings of the 2007 IEEE International Conference on Control and Automation Conference, Guangzhou, China. 2007: 497–500 (2007). https://doi.org/10.1109/ICCA.2007.4376406

  5. Müller, M., Prinz, K., Steinboeck, A., Schausberger, F., Kugi, A.: Adaptive feedforward thickness control in hot strip rolling with oil lubrication. Control. Eng. Pract. 103, 104584 (2020). https://doi.org/10.1016/j.conengprac.2020.104584

    Article  Google Scholar 

  6. Li, X., He, Y.D., Ding, J.G., Luan, F., Zhang, D.H.: Predicting hot-strip finish rolling thickness using stochastic configuration networks. Inf. Sci. 611, 677–689 (2022). https://doi.org/10.1155/2016/3041538

    Article  Google Scholar 

  7. Sun, L.J., Zeng, L., Zhou, H.J., Zhang, L.: Strip thickness prediction method based on improved border collie optimizing LSTM. PeerJ. Computer Sci. 8, e1114 (2022). https://doi.org/10.7717/peerj-cs.1114

    Article  Google Scholar 

  8. Huang, Y., Zhou, X.M., Gao, Z.Y.: Thickness prediction of thin strip cold rolling based on VBGM-RBF. Int. J. Adv. Manuf. Technol. 120, 5865–5884 (2022). https://doi.org/10.1007/s00170-022-09122-2

    Article  Google Scholar 

  9. Liu, C., Li, X.S., You, J.: Prediction and simulation of cold rolled strip thickness based on PSO-SVM-AdaBoost. J. Phys. Conf. Ser. (2023). https://doi.org/10.1088/1742-6596/2303/1/012078

    Article  Google Scholar 

  10. Liu, Y., Wang, X., Sun, J., Liu, G., Li, H., Ji, Y.: Strip thickness and profile-flatness prediction in tandem hot rolling process using mechanism model-guided machine learning. Steel Res. Int. 94, 2200447 (2022). https://doi.org/10.1002/srin.202200447

    Article  Google Scholar 

  11. Li, X., He, Y.D., Ding, J.G., Luan, F., Zhang, D.H.: Predicting hot-strip finish rolling thickness using stochastic configuration networks. Inf. Sci. 611, 677–689 (2022). https://doi.org/10.1016/j.ins.2022.07.173

    Article  Google Scholar 

  12. Xiao, S.Z., Zhang, F., Huang, X.Z.: Online thickness prediction of hot-rolled strip based on ISSA-OSELM. Int. J. Interact. Des. Manuf. IJIDeM 16(3), 1089–1098 (2022)

    Article  Google Scholar 

  13. Mahshad, L., Soroosh, T. A.: Machine learning-based generalized model for finite element analysis of roll deflection during the austenitic stainless steel 316L strip rolling. arXiv:2102.02470 (2022). https://doi.org/10.48550/arXiv.2102.02470

  14. Chen, T. Q., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794 (2016). https://doi.org/10.1145/2939672.2939785

  15. Wang, L., Zhang, W., Bao, Q., Wang, Q.: XGBoost-based failure prediction method for metal additive manufacturing equipment. In: 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China. 3059–3064 (2022). https://doi.org/10.1109/CCDC55256.2022.10034052

  16. Pan, H.Z., Wu, C.J.: Bayesian optimization + XGBoost based life cycle carbon emission prediction for residential buildings—An example from Chengdu China. Build. Simul. 16(08), 1451–1466 (2023). https://doi.org/10.1007/s12273-023-1024-2

    Article  Google Scholar 

  17. Yang, H., Chen, T., Huang, N.J.: An adaptive bird swarm algorithm with irregular random flight and its application. J. Comput. Sci. 32, 57–65 (2019). https://doi.org/10.1016/j.jocs.2019.06.004

    Article  MathSciNet  Google Scholar 

  18. Yang, X.S.: A new metaheuristic bat-inspired algorithm. Nat. Inspired Cooper. Strat. Optim. NICSO 2010(248), 65–74 (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Article  Google Scholar 

  19. Wang, C.F., Song, W.X., Shen, P.P.: A new bat algorithm based on a novel topology and its convergence. J. Comput. Sci. 66, 101931 (2023). https://doi.org/10.1016/j.jocs.2022.101931

    Article  Google Scholar 

  20. Duan, Z.X., Wen, Q., Zhou, M., Song, J.F., Wang, J.: Short-term passenger flow prediction based on improved bat algorithm to optimize LSTM network. J. Railway Sci. Eng. 18(11), 2833–2840 (2021)

    Google Scholar 

  21. Jesús, D.J.R.: Bat algorithm based control to decrease the control energy consumption and modified bat algorithm based control to increase the trajectory tracking accuracy in robots. Neural Netw. 161, 437–448 (2023). https://doi.org/10.1016/j.neunet.2023.02.010

    Article  Google Scholar 

  22. Pascoal, C., Oliveira, M.R., Pacheco, A., Valadas, R.: Theoretical evaluation of feature selection methods based on mutual information. Neurocomputing 226, 168–181 (2017). https://doi.org/10.1016/j.neucom.2016.11.047

    Article  Google Scholar 

  23. Geng, Y.X., Zhang, L.Y., Guo, J.N., Jiang, Y.: Adaptive bat algorithm based on historical population control. Autom. Control. Comput. Sci. 56, 438–446 (2022). https://doi.org/10.3103/S0146411622050042

    Article  Google Scholar 

  24. Yu, H., He, D.N., Wang, G.Y., Li, J., Xie, Y.F.: Big data for intelligent decision making. Acta Automatica Sinica. 46(05), 878–896 (2020)

    Google Scholar 

  25. Dema, R., Amirov, R., Latypov, O.: Mathematical model for assessing the management of quality parameters of hot-rolled strips according to the criterion of local thickness variation. Mater. Today: Proc. 19(Pt 5), 2417–2421 (2019). https://doi.org/10.1016/j.matpr.2019.08.047

    Article  Google Scholar 

Download references

Acknowledgements

This research work was jointly supported by the National Key Research and Development Program of China (2022YFB3304002) and the Guangxi Key Research and Development Program (AB21196025).

Funding

This research work was jointly supported by the National Key Research and Development Program of China (2022YFB3304002) and the Guangxi Key Research and Development Program (AB21196025).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Advanced Rolling and Intelligent Manufacturing, University of Science and Technology Beijing, Beijing, 100083, China

    Fei Zhang, Shuo Huang, Yong-jun Zhang & Yan-jiao Li

  2. Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Fei Zhang, Shuo Huang & Li-jun Wang

  3. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Li-jun Wang

  4. Technology Research Institute of Guangxi Beigang New Material Co., Ltd., Neohai, 536017, China

    Xue-zhong Huang

Authors
  1. Fei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuo Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li-jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong-jun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan-jiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xue-zhong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study's conception and design. F. Zhang and Y. Li contributed to the conception of the study and helped perform the analysis with constructive discussions; S. Huang performed the experiment and the data analyses and wrote the manuscript; L. Wang and Y. Zhang participated in the coordination of the study and reviewed the manuscript; X. Huang collected the on-site rolling data. All authors analyzed the data, discussed the results, and approved the final manuscript.

Corresponding author

Correspondence to Fei Zhang.

Ethics declarations

Conflicts of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, F., Huang, S., Wang, Lj. et al. Online segmented thickness prediction of hot rolling strip based on IBA-XGBoost. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00543-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-024-00543-8

Keywords

Search

Navigation