Skip to main content
SpringerLink
Log in

Multi-objective quality control method for cold-rolled products oriented to customized requirements

  • Published:
International Journal of Minerals, Metallurgy and Materials Aims and scope Submit manuscript

Abstract

To deal with the increasing demand for low-volume customization of the mechanical properties of cold-rolled products, a two-way control method based on mechanical property prediction and process parameter optimization (PPO) has become an effective solution. Aiming at the multi-objective quality control problem of a company’s cold-rolled products, based on industrial production data, we proposed a process parameter design and optimization method that combined multi-objective quality prediction and PPO. This method used the multi-output support vector regression (MSVR) method to simultaneously predict multiple quality indices. The MSVR prediction model was used as the effect verification model of the PPO results. It performed multi-process parameter collaborative design and realized the optimization of production process parameters for customized multi-objective quality requirements. The experimental results showed that, compared with the traditional single-objective quality prediction model based on support vector regression (SVR), the multi-objective prediction model could better take into account the coupling effect between process parameters and quality index, the MSVR model prediction accuracy was higher than that of the SVR, and the optimized process parameters were more capable and reflected the influence of metallurgical mechanism on the quality index, which were more in line with actual production process requirements.

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

Similar content being viewed by others

References

  1. S.Y. Xing, J.G. Ju, and J.S. Xing, Research on hot-rolling steel products quality control based on BP neural network inverse model, Neural Comput. Appl., 31(2019), No. 5, p. 1577.

    Article  Google Scholar 

  2. S. Vaidya, P. Ambad, and S. Bhosle, Industry 4.0 — a glimpse, Procedia Manuf., 20(2018), p. 233.

    Article  Google Scholar 

  3. Z.H. Deng, H.Q. Yin, X. Jiang, C. Zhang, G.F. Zhang, B. Xu, G.Q. Yang, T. Zhang, M. Wu, and X.H. Qu, Machine-learning-assisted prediction of the mechanical properties of Cu-Al alloy, Int. J. Miner. Metall. Mater., 27(2020), No. 3, p. 362.

    Article  CAS  Google Scholar 

  4. S. Hore, S.K. Das, S. Banerjee, and S. Mukherjee, An adaptive neuro-fuzzy inference system-based modelling to predict mechanical properties of hot-rolled TRIP steel, Ironmaking Steelmaking, 44(2017), No. 9, p. 656.

    Article  CAS  Google Scholar 

  5. X.Y. Sui and Z.M. Lv, Prediction of the mechanical properties of hot rolling products by using attribute reduction ELM, Int. J. Adv. Manuf. Technol., 85(2016), No. 5–8, p. 1395.

    Article  Google Scholar 

  6. Y. Wang, D.W. Jiang, X.D. Wang, J.L. Zhang, Z.J. Liu, and B.J. Zhao, Prediction of blast furnace hot metal temperature based on support vector regression and extreme learning machine, Chin. J. Eng., 43(2021), No. 324(4), p. 569.

    Google Scholar 

  7. F.Y. Yan, Y.C. Chan, A. Saboo, J. Shah, G.B. Olson, and W. Chen, Data-driven prediction of mechanical properties in support of rapid certification of additively manufactured alloys, Comput. Model. Eng. Sci., 117(2018), No. 3, p. 343.

    Google Scholar 

  8. S.C. Worland, S. Steinschneider, W. Asquith, R. Knight, and M. Wieczorek, Prediction and inference of flow duration curves using multioutput neural networks, Water Resour. Res., 55(2019), No. 8, p. 6850.

    Article  Google Scholar 

  9. P. Zhou, D.W. Guo, and T.Y. Chai, Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification, Neurocomputing, 308(2018), p. 101.

    Article  Google Scholar 

  10. I. Charlier, D. Paindaveine, and J. Saracco, Multiple-output quantile regression through optimal quantization, Scand. J. Stat., 47(2020), No. 1, p. 250.

    Article  Google Scholar 

  11. W. Deng, J.J. Xu, H.M. Zhao, and Y.J. Song, A novel gate resource allocation method using improved PSO-based QEA, IEEE Trans. Intell. Transp. Syst., 2020, No. 99, p. 1.

  12. Z.Y. Hu, Z.H. Wei, H. Sun, J.M. Yang, and L.X. Wei, Optimization of metal rolling control using soft computing approaches: A review, Arch. Comput. Methods Eng., 28(2021), No. 2, p. 405.

    Article  Google Scholar 

  13. S.W. Wu, X.G. Zhou, J.K. Ren, G.M. Cao, Z.Y. Liu, and N.A. Shi, Optimal design of hot rolling process for C-Mn steel by combining industrial data-driven model and multi-objective optimization algorithm, J. Iron Steel Res. Int., 25(2018), No. 7, p. 700.

    Article  Google Scholar 

  14. A.K. Datta, G. Das, P.K. De, P. Ramachandrarao, and M. Mukhopadhyaya, Finite element modeling of rolling process and optimization of process parameter, Mater. Sci. Eng. A, 426(2006), No. 1–2, p. 11.

    Article  Google Scholar 

  15. E. Kanca, F. Çavdar, and M.M. Erşen, Prediction of mechanical properties of cold rolled steel using genetic expression programming, Acta Phys. Pol. A, 130(2016), No. 1, p. 365.

    Article  CAS  Google Scholar 

  16. Z. Wen, J. Shi, Q. Li, B. He, and J. Chen, ThunderSVM: A fast SVM library on GPUs and CPUs, J. Mach. Learn. Res., 19(2018), p. 1.

    Google Scholar 

  17. F. Pérez-Cruz, G. Camps-Valls, E. Soria-Olivas, J.J. Pérez-Ruixo, A.R. Figueiras-Vidal, and A. Artés-Rodríguez, Multi-dimensional function approximation and regression estimation, [in] International Conference on Artificial Neural Networks, Berlin, 2002, p. 757.

  18. F. Pérez-Cruz, A. Navia- Vázquez, P. L. Alarcón-Diana, and A. Artés-Rodríguez, An IRWLS procedure for SVR, [in] 2000 10th European Signal Processing Conference, Tampere, 2000, p. 1.

  19. F. Yan, L.D. An, Z.M. Lv, Prediction of mechanical properties of cold rolled products based on maximal information coefficient attribute selection, J. Cent. South. Univ. Sci. Technol., 51(2020), No. 1, p. 68.

    Google Scholar 

  20. W. Deng, H.M. Zhao, X.H. Yang, J.X. Xiong, M. Sun, and B. Li, Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment, Appl. Soft Comput., 59(2017), p. 288.

    Article  Google Scholar 

  21. M.S. Nobile, P. Cazzaniga, D. Besozzi, R. Colombo, G. Mauri, and G. Pasi, Fuzzy Self-Tuning PSO: A settings-free algorithm for global optimization, Swarm Evol. Comput., 39(2018), p. 70.

    Article  Google Scholar 

  22. E.T. Mohamad, D.J. Armaghani, E. Momeni, A.H. Yazdavar, and M. Ebrahimi, Rock strength estimation: A PSO-based BP approach, Neural Comput. Appl., 30(2018), No. 5, p. 1635.

    Article  Google Scholar 

  23. B. Keshtegar, T. Nguyen-Thoi, T.T. Truong, and S.P. Zhu, Optimization of buckling load for laminated composite plates using adaptive Kriging-improved PSO: A novel hybrid intelligent method, Def. Technol., 17(2021), No. 1, p. 85.

    Article  Google Scholar 

  24. H. Moayedi, M. Mehrabi, M. Mosallanezhad, A.S.A. Rashid, and B. Pradhan, Modification of landslide susceptibility mapping using optimized PSO-ANN technique, Eng. Comput., 35(2019), No. 3, p. 967.

    Article  Google Scholar 

  25. P.Y. Wen, J.S. Han, H.W. Luo, and X.P. Mao, Effect of flash processing on recrystallization behavior and mechanical performance of cold-rolled IF steel, Int. J. Miner. Metall. Mater., 27(2020), No. 9, p. 1234.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Fundamental Research Funds for the Central Universities (No. FRF-MP-20-08).

Author information

Authors and Affiliations

  1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, China

    Yi-fan Yan & Zhi-min Lü

Authors
  1. Yi-fan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-min Lü
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-min Lü.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, Yf., Lü, Zm. Multi-objective quality control method for cold-rolled products oriented to customized requirements. Int J Miner Metall Mater 28, 1332–1342 (2021). https://doi.org/10.1007/s12613-021-2292-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12613-021-2292-4

Keywords

Search

Navigation