Advertisement

JOM

pp 1–7 | Cite as

Machine Learning-Enabled Competitive Grain Growth Behavior Study in Directed Energy Deposition Fabricated Ti6Al4V

  • Jinghao Li
  • Manuel Sage
  • Xiaoyi Guan
  • Mathieu Brochu
  • Yaoyao Fiona ZhaoEmail author
ICME-Based Design and Optimization of Materials for Additive Manufacturing
  • 80 Downloads

Abstract

Directed energy deposition (DED) of titanium alloys is a rapidly developing technology because of its flexibility in freeform fabrication and remanufacturing. However, the uncertainties of a solidification microstructure during the deposition process are limiting its development. This article presents an artificial neural network (ANN) to investigate the relation between the grain boundary tilt angle and three causative factors, namely the thermal gradient, crystal orientation and Marangoni effect. A series of wire feedstock DED, optical microscope and electron backscatter diffraction experiments was carried out under the Taguchi experimental design to gather the training and testing data for the ANN. Compared with conventional microstructure simulation methods, the strategy and ANN model developed in this work were demonstrated to be a valid way to describe the competitive grain growth behavior in DED fabricated Ti6Al4V. They can be deployed to achieve a quantitative microstructure simulation and extended to other polycrystal material solidification processes.

Notes

Acknowledgements

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Collaborative Research and Development Grant CRDPJ 479630-15. The lead author also received partial funding from the NSERC Collaborative Research and Training Experience (CREATE) Program Grant 449343. The author also appreciates the McGill Engineering Doctoral Award (MEDA) grant and China Scholarship Council (201706460027).

References

  1. 1.
    G. Lütjering and J.C. Williams, Titanium, 2nd ed. (New York: Springer, 2007), pp. 1–12.Google Scholar
  2. 2.
    D. Greitemeier, C. Dalle Donne, A. Schoberth, M. Jürgens, J. Eufinger, and T. Melz, Appl. Mech. Mater. 807, 169 (2015).CrossRefGoogle Scholar
  3. 3.
    P. Kobryn and S. Semiatin, JOM 53, 40 (2001).CrossRefGoogle Scholar
  4. 4.
    L. Thijs, F. Verhaeghe, T. Craeghs, J. Van Humbeeck, and J.P. Kruth, Acta Mater. 58, 3303 (2010).CrossRefGoogle Scholar
  5. 5.
    F. Wang, S. Williams, and M. Rush, Int. J. Adv. Manuf. Technol. 57, 597 (2011).CrossRefGoogle Scholar
  6. 6.
    F. Wang, S. Williams, P. Colegrove, and A.A. Antonysamy, MMTA 44, 968 (2012).CrossRefGoogle Scholar
  7. 7.
    W.E. Frazier, J. Mater. Eng. Perform. 23, 1917 (2014).CrossRefGoogle Scholar
  8. 8.
    D. Herzog, V. Seyda, E. Wycisk, and C. Emmelmann, Acta Mater. 117, 371 (2016).CrossRefGoogle Scholar
  9. 9.
    J.J. Lin, Y.H. Lv, Y.X. Liu, B.S. Xu, Z. Sun, Z.G. Li, and Y.X. Wu, Mater. Des. 102, 30 (2016).CrossRefGoogle Scholar
  10. 10.
    N. Raghavan, R. Dehoff, S. Pannala, S. Simunovic, M. Kirka, J. Turner, N. Carlson, and S.S. Babu, Acta Mater. 112, 303 (2016).CrossRefGoogle Scholar
  11. 11.
    W.J. Sames, F.A. List, S. Pannala, R.R. Dehoff, and S.S. Babu, Int. Mater. Rev. 61, 315 (2016).CrossRefGoogle Scholar
  12. 12.
    A. Basak and S. Das, Annu. Rev. Mater. Res. 46, 125 (2016).CrossRefGoogle Scholar
  13. 13.
    S.S. Al-Bermani, M.L. Blackmore, W. Zhang, and I. Todd, MMTA 41, 3422 (2010).CrossRefGoogle Scholar
  14. 14.
    G.C. Obasi, S. Birosca, J. Quinta Da Fonseca, and M. Preuss, Acta Mater. 60, 1048 (2012).CrossRefGoogle Scholar
  15. 15.
    Y. Kok, X.P. Tan, P. Wang, M.L.S. Nai, N.H. Loh, E. Liu, and S.B. Tor, Mater. Des. 139, 565 (2018).CrossRefGoogle Scholar
  16. 16.
    M. Umehara, H.S. Stein, D. Guevarra, P.F. Newhouse, D.A. Boyd, and J.M. Gregoire, NPJ Comput. Mater. 5, 34 (2019).CrossRefGoogle Scholar
  17. 17.
    W. Liu and J. Dupont, Acta Mater. 52, 4833 (2004).Google Scholar
  18. 18.
    H. Wei, G. Knapp, T. Mukherjee and T. Debroy, Addit. Manuf. 25, 448 (2019).CrossRefGoogle Scholar
  19. 19.
    A. Pineau, G. Guillemot, D. Tourret, A. Karma, and C.-A. Gandin, Acta Mater. 155, 286 (2018).CrossRefGoogle Scholar
  20. 20.
    Y. Lee, M. Nordin, S.S. Babu, and D.F. Farson, Metall. Mater. Trans. B 45, 1520 (2014).CrossRefGoogle Scholar
  21. 21.
    M.F. Zhu, S.Y. Lee, and C.P. Hong, Phys. Rev. E: Stat., NonlinearSoft Matter Phys. 69, 061610 (2004).CrossRefGoogle Scholar
  22. 22.
    C. Cayron, J. Appl. Crystallogr. 40, 1183 (2007).CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMcGill UniversityMontrealCanada
  2. 2.Department of Mining and Materials EngineeringMcGill UniversityMontrealCanada

Personalised recommendations