A new method to predict mechanical properties for microalloyed steels via industrial data and mechanism analysis

  • Wei-gang LiEmail author
  • Wei Yang
  • Yun-tao Zhao
  • Guang Xu
  • Xiang-hua Liu
Original Paper


A new modeling method has been developed by combining industrial data and metallurgical mechanisms. This method utilizes a series of models to predict the mechanical properties for microalloyed steels with high reliability and strong generalization. Specifically, the modeling process includes determining the influencing factors, cleaning the actual data, building sub-models for each single factor and for the interactions between the factors, verifying the reproducibility of the sub-models, and building the whole model. The effects of alloying elements (such as C, Si, Nb, and V), precipitation processes of microalloying elements, and processing parameters (such as reheating temperature and coiling temperature) are quantitatively involved in the models. In addition, the obtained models can quantitatively describe the effect of each factor on the mechanical properties, which is impossible by using traditional modeling methods. A practical modeling case is introduced, and the influencing mechanisms of the factors on the mechanical properties are analyzed. The results show that the prediction errors for the tensile strength and yield strength are 2.54% and 3.34%, respectively, which exhibits the advantages of high precision and strong adaptability of the model used to design and develop new steel grades, reduce the number of physical tests, and reduce the development cost of new products.


Additive model Mechanical property prediction Metallurgical mechanism Industrial data 



This research is supported by National Natural Science Foundation of China (51774219) and Youth Science and Technology Program of Wuhan (2016070204010099).


  1. [1]
    D.F. Sokolov, A.A. Ogoltcov, A.A. Vasilyev, N.G. Kolbasnikov, S.F. Sokolov, Mater. Sci. Forum 762 (2013) 116–121.CrossRefGoogle Scholar
  2. [2]
    Y. Gan, Z.D. Liu, G.D. Wang, D. Wu, W. Wang, P.J. Zhang, Iron and Steel 41 (2006) No. 3, 39–43.Google Scholar
  3. [3]
    G. D. Wang, Iron and Steel 50 (2015) No. 9, 1–10.Google Scholar
  4. [4]
    C.M. Sellars, J.A. Whiteman, Met. Sci. 13 (1979) 187–194.CrossRefGoogle Scholar
  5. [5]
    J. Majta, R. Kuziak, M. Pietrzyk, H. Krzton, J. Mater. Process. Technol. 60 (1996) 581–588.CrossRefGoogle Scholar
  6. [6]
    S. Hore, S.K. Das, S. Banerjee, S. Mukherjee, Ironmak. Steelmak. 44 (2017) 656–665.CrossRefGoogle Scholar
  7. [7]
    J. Cui, C. Lei, Z. Xing, C. Li, S. Ma, J. Mater. Eng. Perform. 21 (2012) 2244–2254.CrossRefGoogle Scholar
  8. [8]
    K. Agarwal, R. Shivpuri, ISIJ Int. 52 (2012) 1862–1871.CrossRefGoogle Scholar
  9. [9]
    A. Kavousi Sisi, S.E. Mirsalehi, Sci. Technol. Weld. Join. 21 (2015) 43–52.Google Scholar
  10. [10]
    Z.H. Guo, Q.L. Zhang, Baosteel Technology (2011) No. 5, 1–6.Google Scholar
  11. [11]
    Z.H. Guo, Q.L. Zhang, Y.C. Su, Y. Xia, Metal Ind. Autom. 33 (2009) No. 2, 1–6.Google Scholar
  12. [12]
    M.B. Esfahani, M.R. Toroghinejad, A.R.K. Yeganeh, Mater. Des. 30 (2009) 3653–3658.CrossRefGoogle Scholar
  13. [13]
    M.B. Esfahani, M.R. Toroghinejad, S. Abbasi, ISIJ Int. 49 (2009) 1583–1587.CrossRefGoogle Scholar
  14. [14]
    X. Sui, Z. Lv, Int. J. Adv. Manuf. Technol. 85 (2016) 1395–1403.CrossRefGoogle Scholar
  15. [15]
    G. Khalaj, T. Azimzadegan, M. Khoeini, M. Etaat, Neural Comput. Appl. 23 (2013) 2301–2308.CrossRefGoogle Scholar
  16. [16]
    Y.H. Zhao, Y. Weng, N.Q. Peng, G.B. Tang, Z.D. Liu, J. Iron Steel Res. Int. 20 (2013) No. 7, 9–15.CrossRefGoogle Scholar
  17. [17]
    I. Mohanty, S. Sarkar, B. Jha, S. Das, R. Kumar, Ironmak. Steelmak. 41 (2014) 618–627.CrossRefGoogle Scholar
  18. [18]
    T. Bhattacharyya, S.B. Singh, S. Sikdar, S. Bhattacharyya, W. Bleck, D. Bhattacharjee, Mater. Sci. Eng. A 565 (2013) 148–157.CrossRefGoogle Scholar
  19. [19]
    A.A. Dos Santos, R. Barbosa, Steel Res. Int. 81 (2010) 55–63.CrossRefGoogle Scholar
  20. [20]
    L. Čiripová, E. Hryha, E. Dudrová, A. Výrostková, Mater. Des. 35 (2012) 619–625.CrossRefGoogle Scholar
  21. [21]
    C. Zhang, B. Gong, C. Deng, D. Wang, Mater. Sci. Eng. A 685 (2017) 310–316.CrossRefGoogle Scholar
  22. [22]
    D. Šimek, A. Oswald, R. Schmidtchen, M. Motylenko, G. Lehmann, D. Rafaja, Steel Res. Int. 85 (2014) 1369–1378.CrossRefGoogle Scholar
  23. [23]
    N.Q. Peng, Development of microstructure evolution and property prediction model for hot rolled strip and preliminary exploration of ultra fast cooling process, Central Iron and Steel Research Institute, Beijing, 2012.Google Scholar
  24. [24]
    G. Khalaj, H. Pouraliakbar, K.R. Mamaghani, M.J. Khalaj, Neural Network World 23 (2013) 351–367.CrossRefGoogle Scholar
  25. [25]
    H. Pouraliakbar, G. Khalaj, M.R. Jandaghi, M.J. Khalaj, J. Min. Metall. Sect. B-Metall. 51 (2015) 173–178.CrossRefGoogle Scholar
  26. [26]
    M.J. Faizabadi, G. Khalaj, H. Pouraliakbar, M.R. Jandaghi, Neural Comput. Appl. 25 (2014) 1993–1999.CrossRefGoogle Scholar
  27. [27]
    N. Narimani, B. Zarei, H. Pouraliakbar, G. Khalaj, Measurement 62 (2015) 97–107.CrossRefGoogle Scholar
  28. [28]
    G. Khalaj, A. Nazari, H. Pouraliakbar, Neural Network World 23 (2013) 117–130.CrossRefGoogle Scholar
  29. [29]
    S. Yin, O. Kaynak, Proc. IEEE 103 (2015) 143–146.CrossRefGoogle Scholar
  30. [30]
    N. Rahman, F. Aldhaban, in: 2015 Portland Int. Conf. Manage. Eng. Technol., IEEE, 2015, pp. 478–484.Google Scholar
  31. [31]
    H. Adrian, Mater. Sci. Technol. 8 (1992) 406–420.CrossRefGoogle Scholar
  32. [32]
    T. Hastie, R. Tibshirani, Statist. Sci. 1 (1986) 297–310.MathSciNetCrossRefGoogle Scholar
  33. [33]
    S.W. Wu, Z.Y. Liu, X.G. Zhou, N.A. Shi, J. Iron Steel Res. 28 (2016) No. 12, 1–4.CrossRefGoogle Scholar

Copyright information

© China Iron and Steel Research Institute Group 2018

Authors and Affiliations

  1. 1.Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of EducationWuhan University of Science and TechnologyWuhanChina
  2. 2.The State Key Laboratory of Refractories and Metallurgy, Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of EducationWuhan University of Science and TechnologyWuhanChina
  3. 3.Research Institute of Science and TechnologyNortheastern UniversityShenyangChina

Personalised recommendations