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Metals and Materials International

, Volume 25, Issue 3, pp 768–778 | Cite as

Neural Network Approach to Construct a Processing Map from a Non-linear Stress–Temperature Relationship

  • Chan Hee ParkEmail author
  • Dojin Cha
  • Minsoo Kim
  • N. S. Reddy
  • Jong-Taek Yeom
Article
  • 46 Downloads

Abstract

An accurate processing map for a metal provides a means of attaining a desired microstructure and required shape through thermo-mechanical processing. To construct such a map, the isothermal flow stress, σiso, is required. Conventionally, the non-isothermal flow stress measured by experiment is corrected to σiso using whole-temperature-range linear interpolation (WRLI) or partial-temperature-range linear interpolation (PRLI). However, these approaches could incur significant errors if the non-isothermal flow stress exhibits a non-linear relationship with the temperature. In this study, an artificial neural network (ANN) model was applied to correct the non-isothermal flow stress in 10 wt% Cr steel, which exhibits a non-linear temperature dependence within a target temperature range of 750–1250 °C. Processing maps were constructed using σiso corrected by applying the WRLI, PRLI, and ANN approaches, respectively, and were then compared with the actual microstructures. The WRLI approach produced the highest minimum error of σiso (17.2%) and over-predicted the shear-band formation. The PRLI approach reasonably predicted the microstructural changes, but the minimum error for σiso (8.9%) was somewhat high. The ANN approach not only realized the lowest minimum error of σiso (~ 0%), but also effectively predicted the microstructural changes.

Keywords

Metallic alloys Thermomechanical Processing Microstructure Stress–strain measurements Computer modelling 

Notes

Acknowledgements

This study was supported by a grant (2013025839) from Doosan Heavy Industries & Construction, Republic of Korea. Author N. S. Reddy greatly acknowledges for the support of research group promotion, Gyeongsang National University, 2014.

References

  1. 1.
    H.L. Gegel, in Computer Simulation in Materials Science, ed. by R.J. Arsenault, J.R. Beeler, D.M. Easterling (ASM, Metals Park, 1987), p. 291Google Scholar
  2. 2.
    J.C. Malas, V. Seetharaman, JOM 44, 8 (1992)CrossRefGoogle Scholar
  3. 3.
    S.V.S.N. Murty, B.N. Rao, Mater. Sci. Eng. A 254, 76 (1998)CrossRefGoogle Scholar
  4. 4.
    S.L. Semiatin, G.D. Lahoti, Metall. Trans. A 12, 1705 (1981)CrossRefGoogle Scholar
  5. 5.
    Y.V.R.K. Prasad, K.P. Rao, S. Sasidhara, Hot Working Guide, 2nd edn. (ASM International, Ohio, 2015), pp. 1–30Google Scholar
  6. 6.
    G.R. Johnson, W.H. Cook, in Proceedings of Seventh International Symposium on Ballistics. A Constitutive Model and Data for Metals subjected to Large Strains, High Strain Rates and High Temperatures (The Hague, The Netherlands, 1983)Google Scholar
  7. 7.
    A.S. Khan, S. Huang, Int. J. Plasticity 8, 397 (1992)CrossRefGoogle Scholar
  8. 8.
    A.S. Khan, R. Liang, Int. J. Plasticity 15, 1089 (1999)CrossRefGoogle Scholar
  9. 9.
    M.J. Kim, H.J. Jeong, J.W. Park, S.T. Hong, H.N. Han, Met. Mater. Int. 24, 42 (2018)CrossRefGoogle Scholar
  10. 10.
    F. Kabirian, A.S. Khan, A. Pandey, Int. J. Plasticity 55, 232 (2014)CrossRefGoogle Scholar
  11. 11.
    H. Mecking, U.F. Kocks, Acta Metall. 29, 1865 (1981)CrossRefGoogle Scholar
  12. 12.
    P.S. Follansbee, G.T. Gray III, Metall. Trans. A 20, 863 (1989)CrossRefGoogle Scholar
  13. 13.
    S. Nemat-Nasser, J.B. Isaacs, Acta Mater. 45, 907 (1997)CrossRefGoogle Scholar
  14. 14.
    C.H. Park, S.G. Hong, C.S. Lee, Mater. Sci. Eng. A 528, 1154 (2011)CrossRefGoogle Scholar
  15. 15.
    I. Yoo, J. Park, S. Choe, Korean J. Met. Mater. 34, 973 (1996)Google Scholar
  16. 16.
    X. Yang, H. Guo, Z. Yao, J. Mater. Res. 31, 2863 (2016)CrossRefGoogle Scholar
  17. 17.
    N.S. Reddy, Y.H. Lee, C.H. Park, C.S. Lee, Mater. Sci. Eng. A 492, 276 (2008)CrossRefGoogle Scholar
  18. 18.
    G. Ji, F. Li, Q. Li, H. Li, Z. Li, Mater. Sci. Eng. A 528, 4774 (2011)CrossRefGoogle Scholar
  19. 19.
    P.S. Robi, U.S. Dixit, J. Mater. Process Technol. 142, 289 (2003)CrossRefGoogle Scholar
  20. 20.
    S. Mandal, P.V. Sivaprasad, S. Venugopal, K.P.N. Murthy, Appl. Soft Comput. 9, 237 (2009)CrossRefGoogle Scholar
  21. 21.
    J. Zhao, H. Ding, W. Zhao, M. Huang, D. Wei, Z. Jiang, Comput. Mater. Sci. 92, 47 (2014)CrossRefGoogle Scholar
  22. 22.
    Y. Zhu, W. Zeng, Y. Sun, F. Feng, Y. Zhou, Comput. Mater. Sci. 50, 1785 (2011)CrossRefGoogle Scholar
  23. 23.
    B.H. Lee, N.S. Reddy, J.T. Yeom, C.S. Lee, J. Mater. Process Tech. 187–188, 766 (2007)CrossRefGoogle Scholar
  24. 24.
    C.H. Park, Y.G. Ko, J. Park, C.S. Lee, Mater. Sci. Eng. A 496, 150 (2008)CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Metals and Materials 2018

Authors and Affiliations

  • Chan Hee Park
    • 1
    Email author
  • Dojin Cha
    • 2
  • Minsoo Kim
    • 2
  • N. S. Reddy
    • 3
  • Jong-Taek Yeom
    • 1
  1. 1.Advanced Metals DivisionKorea Institute of Materials ScienceChangwonRepublic of Korea
  2. 2.Corporate R&D InstituteDoosan Heavy Industries and ConstructionChangwonRepublic of Korea
  3. 3.School of Materials Science and EngineeringGyeongsang National UniversityJinjuRepublic of Korea

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