Skip to main content
SpringerLink
Log in

Application of Artificial Neural Network to Predict the Hot Flow Behavior of Ti-Nb Microalloyed Steel During Hot Torsion Deformation

  • Original Article
  • Published:
Transactions of the Indian Institute of Metals Aims and scope Submit manuscript

Abstract

In the present study, the hot compressive deformation behavior of Ti-Nb bearing microalloyed steel was investigated in the temperature range of 850–1100 °C and strain rates of 0.01–1 s−1. Flow curves obtained from hot compression tests were analyzed, and it was demonstrated that dynamic recrystallization is the main softening mechanism during deformation at 900, 1000, and 1100 °C at all strain rates. It was also demonstrated that dynamic recovery occurs during deformation at 800 °C. Based on the obtained results, an artificial neural network was constructed to predict the hot flow softening behavior of Ti-Nb microalloyed steel which is due to the occurrence of dynamic recrystallization and recovery. Results obtained from artificial neural network were compared with the experimental values of flow stress, and a very good correlation was observed between them. This indicates the excellent capability of the developed artificial neural network to predict the flow softening due to complex microstructural evolutions accompanying dynamic recrystallization and dynamic recovery.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 9
Fig. 9

Similar content being viewed by others

References

  1. Matlock D K, Krauss G, and Speer J G, Mater Sci Forum Trans Tech Publ (2005), p 87.

  2. Cruz M G and Viecelli A Mater Des 29(2) (2008) 539.

    Article  CAS  Google Scholar 

  3. Naylor D Mater Sci Forum Trans Tech Publ (1998), p 83.

  4. Mao X, Huo X, Sun X, and Chai Y, J Mater Process Technol 210 (2010) 1660.

    Article  CAS  Google Scholar 

  5. Xie K Y, Zheng T, Cairney J M, Kaul H, Williams J G, Barbaro F J, Killmore C R, and Ringer S P, Scr Mater 66 (2012) 710.

    Article  CAS  Google Scholar 

  6. Xu K, Thomas B G, and O’malley R, Metall Mater Trans A 42 (2011) 524.

    Article  CAS  Google Scholar 

  7. Shanmugam S, Tanniru M, Misra R, Panda D, and Jansto S, Mater Sci Technol 21 (2005) 883.

    Article  CAS  Google Scholar 

  8. Yuan S and Liang G, Mater Lett 63 (2009) 2324.

    Article  CAS  Google Scholar 

  9. Kolbasnikov N, Matveev M, Zotov O, Mishin V, Mishnev P, and Nikonov S, Steel Transl 44 (2014) 149.

    Article  Google Scholar 

  10. Muntin A, Metallurgist 62 (2019) 900.

    Article  CAS  Google Scholar 

  11. Zhang Y-J, Hui W-J, Dong H, J Iron Steel Res Int 14 (2007) 189.

    Article  Google Scholar 

  12. Lan L, Zhou W, and Misra R, Mater Sci Eng A 756 (2019) 18.

    Article  CAS  Google Scholar 

  13. Ghazani M S, Eghbali B, and Ebrahimi G, Met Mater Int 23 (2017) 964.

    Article  CAS  Google Scholar 

  14. Ghazani M S, Eghbali B, and Ebrahimi G R, Trans Indian Inst Met 70 (2017) 1755.

    Article  CAS  Google Scholar 

  15. Ahmadi H, Ashtiani H R, Heidari M, Mater Today Commun 25 (2020) 101528.

    Article  CAS  Google Scholar 

  16. Akbari Z, Mirzadeh H, and Cabrera J-M ,Mater Des 77 (2015) 126.

    Article  CAS  Google Scholar 

  17. Wei H-l, Liu G-Q, and Zhang M-H, Mater Sci Eng A 602 (2014) 127.

    Article  CAS  Google Scholar 

  18. Zhang S, Feng D, Huang Y, Wei S, Mohrbacher H, and Zhang Y, J Mater Eng Perform 25(3) (2016) 948.

    Article  CAS  Google Scholar 

  19. Feng W and Fu Y, Mater Des 57 (2014) 465.

    Article  CAS  Google Scholar 

  20. Ji G, Li F, Li Q, Li H, and Li Z, Comput Mater Sci 48 (2010) 626.

    Article  CAS  Google Scholar 

  21. Li L-X, Guan W, Jie L, and Yao Z-Q, Trans Nonferrous Met Soc China 24 (2014) 42.

    Article  Google Scholar 

  22. Han Y, Zeng W, Qi Y, and Zhao Y, Mater Sci Eng A 528 (2011) 8410.

    Article  CAS  Google Scholar 

  23. Dehghan-Manshadi A and Hodgson P D, ISIJ Int 47 (2007) 1799.

    Article  CAS  Google Scholar 

  24. Sabokpa O, Zarei-Hanzaki A, Abedi H, Haghdadi N, Mater Des 39 (2012) 390.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Materials Science Engineering, University of Bonab, P.O. Box 5551761167, Bonab, Iran

    Mehdi Shaban Ghazani

Authors
  1. Mehdi Shaban Ghazani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Shaban Ghazani.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghazani, M.S. Application of Artificial Neural Network to Predict the Hot Flow Behavior of Ti-Nb Microalloyed Steel During Hot Torsion Deformation. Trans Indian Inst Met 75, 2345–2353 (2022). https://doi.org/10.1007/s12666-022-02611-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12666-022-02611-8

Keywords

Search

Navigation