Atomistic Simulation of Nano-Rolling Process for Nanocrystalline Tungsten

Abstract

Nanocrystalline tungsten (NC W) sheets and foils have significant high-temperature applications in various technological sectors and hence their economical large-scale production is highly necessary. However, research to help understand the underlying nanoscale deformation mechanisms is limited. Here, we have developed an atomistic model to study the temperature effect on the structural and grain orientation evolution in NC W during nano-rolling. Structural analysis shows that the contribution of dislocation mechanisms decreases and twin mechanisms increases with an increase in temperature. Moreover, atomic strain analysis revealed that cryo-rolling causes formation of a smoother surface, whereas hot-rolling leads to uneven surfaces. A bimodal grain structure is obtained during the cryo-rolling, whereas equiaxed grains are formed at high temperature due to dynamic recrystallization. This work provides insights into comprehending the deformation mechanisms at atomic level, and the compendium of this research will help in studying nano-rolling in other metallic systems.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    E. Lassner and W.D. Schubert, Properties, chemistry, technology of the element, alloys, and chemical compounds, 1st ed. (New York: Vienna University of Technology, Kluwer Academic, Plenum Publishers, 1999), pp. 124–125.

    Google Scholar 

  2. 2.

    Q. Wei, T. Jiao, K.T. Ramesh, E. Ma, L.J. Kecskes, L. Magness, R. Dowding, V.U. Kazykhanov, and R.Z. Valiev, Acta Mater. 54, 77 (2006).

    Google Scholar 

  3. 3.

    Q. Wei, K.T. Ramesh, B.E. Schuster, L.J. Kecskes, and R.J. Dowding, JOM 58, 40 (2006).

    Google Scholar 

  4. 4.

    L. Hu, B.D. Wirth, and D. Maroudas, Appl. Phys. Lett. 111, 081902 (2017).

    Google Scholar 

  5. 5.

    M. Zhao, I. Issa, M.J. Pfeifenberger, M. Wurmshuber, and D. Kiener, Acta Mater. 182, 215 (2020).

    Google Scholar 

  6. 6.

    S. Saha and M. Motalab, Comput. Mater. Sci. 149, 360 (2018).

    Google Scholar 

  7. 7.

    M. Miyamoto, D. Nishijima, Y. Ueda, R.P. Doerner, H. Kurishita, M.J. Baldwin, S. Morito, K. Ono, and J. Hanna, Nucl. Fusion 49, 065035 (2009).

    Google Scholar 

  8. 8.

    W.M. Shu, A. Kawasuso, Y. Miwa, E. Wakai, G.N. Luo, and T. Yamanishi, Phys. Scr. 2007, 96 (2007).

    Google Scholar 

  9. 9.

    Y. Yuan, H. Greuner, B. Böswirth, C. Linsmeier, G.N. Luo, B.Q. Fu, H.Y. Xu, Z.J. Shen, and W. Liu, J. Nucl. Mater. 437, 297 (2013).

    Google Scholar 

  10. 10.

    X. Zhang, Q. Yan, S. Lang, M. Xia, and C. Ge, J. Nucl. Mater. 468, 339 (2016).

    Google Scholar 

  11. 11.

    Z. Chen, W. Han, J. Yu, L. Kecskes, K. Zhu, and Q. Wei, J. Nucl. Mater. 479, 418 (2016).

    Google Scholar 

  12. 12.

    Y.B. Park, D.N. Lee, and G. Gottstein, Acta Mater. 46, 3371 (1998).

    Google Scholar 

  13. 13.

    N. Zhang and W. Mao, Int. J. Refract. Met. Hard Mater. 80, 210 (2019).

    Google Scholar 

  14. 14.

    C.S. Perugu, S. Kumar, and S. Suwas, JOM 72, 1627 (2020).

    Google Scholar 

  15. 15.

    B. Deng, P.C. Hsu, G. Chen, B.N. Chandrashekar, L. Liao, Z. Ayitimuda, J. Wu, Y. Guo, L. Lin, Y. Zhou, M. Aisijiang, Q. Xie, Y. Cui, Z. Liu, and H. Peng, Nano Lett. 15, 4206 (2015).

    Google Scholar 

  16. 16.

    D. Goswami, J.C. Munera, A. Pal, B. Sadri, C.L.P. Scarpetti, and R.V. Martinez, Nano Lett. 18, 3616 (2018).

    Google Scholar 

  17. 17.

    K.V. Reddy and S. Pal, Philos. Mag. Lett. 99, 253 (2019).

    Google Scholar 

  18. 18.

    K.V. Reddy and S. Pal, JOM 71, 3407 (2019).

    Google Scholar 

  19. 19.

    K.V. Reddy and S. Pal, J. Appl. Phys. 125, 095101 (2019).

    Google Scholar 

  20. 20.

    K.V. Reddy and S. Pal, Steel Res. Int. 90, 1800636 (2019).

    Google Scholar 

  21. 21.

    K.V. Reddy and S. Pal, J. Appl. Phys. 127, 154305 (2020).

    Google Scholar 

  22. 22.

    N.Q. Vo, J. Zhou, Y. Ashkenazy, D. Schwen, R.S. Averback, and P. Bellon, JOM 65, 382 (2013).

    Google Scholar 

  23. 23.

    Y. Shibuta, S. Sakane, E. Miyoshi, S. Okita, T. Takaki, and M. Ohno, Nat. Commun. 8, 1 (2017).

    Google Scholar 

  24. 24.

    K.V. Reddy and S. Pal, J. Mol. Model. 24, 277 (2018).

    Google Scholar 

  25. 25.

    K.V. Reddy, C. Deng, and S. Pal, Acta Mater. 164, 347 (2019).

    Google Scholar 

  26. 26.

    V.A. Menon and S. James, J. Manuf. Mater. Process. 2, 51 (2018).

    Google Scholar 

  27. 27.

    S. Plimpton, J. Comput. Phys. 117, 1 (1995).

    Google Scholar 

  28. 28.

    M.C. Marinica, L. Ventelon, M.R. Gilbert, L. Proville, S.L. Dudarev, J. Marian, G. Bencteux, and F. Willaime, J. Phys. Condens. Matter 25, 395502 (2013).

    Google Scholar 

  29. 29.

    D.J. Evans and B.L. Holian, J. Chem. Phys. 83, 4069 (1985).

    Google Scholar 

  30. 30.

    P.M. Larsen, S. Schmidt, and J. Schiøtz, Modell. Simul. Mater. Sci. Eng. 24, 055007 (2016).

    Google Scholar 

  31. 31.

    F. Shimizu, S. Ogata, and J. Li, Mater. Trans., 0710160231-0710160231 (2007)

  32. 32.

    M.L. Falk and J.S. Langer, Phys. Rev. E 57, 7192 (1998).

    Google Scholar 

  33. 33.

    A. Stukowski, V.V. Bulatov, and A. Arsenlis, Modell. Simul. Mater. Sci. Eng. 20, 085007 (2012).

    Google Scholar 

  34. 34.

    R. Krakow, R.J. Bennett, D.N. Johnstone, Z. Vukmanovic, W. Solano-Alvarez, S.J. Lainé, J.F. Einsle, P.A. Midgley, C.M.F. Rae, and R. Hielscher, Proc R Soc A. 473, 20170274 (2017).

    Google Scholar 

  35. 35.

    A. Stukowski, Modell. Simul. Mater. Sci. Eng. 18, 015012 (2009).

    MathSciNet  Google Scholar 

  36. 36.

    J.D. Honeycutt and H.C. Andersen, J. Phys. Chem. 91, 4950 (1987).

    Google Scholar 

  37. 37.

    R.E. Reed and H.R. Abbaschian, Physical metallurgy principles (Boston: PWS Engineering, 1973).

    Google Scholar 

  38. 38.

    G.J. Tucker, M.A. Tschopp, and D.L. McDowell, Acta Mater. 58, 6464 (2010).

    Google Scholar 

  39. 39.

    Q. Wei, S. Cheng, K.T. Ramesh, and E. Ma, Mater. Sci. Eng., A 381, 71 (2004).

    Google Scholar 

  40. 40.

    Q. Wei, H.T. Zhang, B.E. Schuster, K.T. Ramesh, R.Z. Valiev, L.J. Kecskes, R.J. Dowding, L. Magness, and Cho, K. Acta Mater., 54(15), 4079 (2006)

  41. 41.

    H.J. McQueen and D.L. Bourell, JOM 39, 28 (1987).

    Google Scholar 

  42. 42.

    Y. Chen, J. Li, B. Tang, H. Kou, X. Xue, and Y. Cui, J. Alloys Compd. 618, 146 (2015).

    Google Scholar 

  43. 43.

    S.A. Farzadfar, E. Martin, M. Sanjari, E. Essadiqi, and S. Yue, J. Mater. Sci. 47, 5488 (2012).

    Google Scholar 

Download references

Acknowledgement

The authors acknowledge the Computer Centre of the National Institute of Technology Rourkela for providing the high-performance computing facility (HPCF) necessary for carrying out this research work.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Snehanshu Pal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Verify currency and authenticity via CrossMark

Cite this article

Reddy, K.V., Pal, S. Atomistic Simulation of Nano-Rolling Process for Nanocrystalline Tungsten. JOM 72, 3977–3986 (2020). https://doi.org/10.1007/s11837-020-04337-8

Download citation