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Cellular automata modeling of static recrystallization based on the curvature driven subgrain growth mechanism

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Abstract

A two-dimensional cellular automata model was developed to describe the static recrystallization (SRX) arising from the subgrain growth, the driving force of which is dependent on boundary energy and local curvature. At the same time, the subgrain boundary energy and mobility rely on the boundary misorientation angle. On the basis, a deterministic switch rule was adopted to simulate the subgrain growth and kinetics of recrystallization quantitatively to provide an insight into the grain boundary bulging nucleation mechanism. Microstructure evolutions during SRX in different cases were simulated by the developed model. At the beginning of the simulation, the initial polycrystalline microstructure which contains large number of uniformly distributed subgrains in every pre-existing grain was prepared using simple assumption based on experimental observations. Then, both homogeneous and inhomogeneous subgrain growth phenomena were captured by the simulation with different inter-subgrain misorientation, which showed continuous and discontinuous recrystallization, respectively. The effects of initial mean subgrain radius, distribution of initial subgrains, distribution of inter-subgrain misorientations, and annealing temperature on the recrystallization kinetics were also investigated.

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References

  1. Humphreys FJ, Hatherly M (2004) Recrystallization and related annealing phenomena. Pergamon Press, Oxford

    Google Scholar 

  2. Holm EA, Miodownik MA, Rollett AD (2003) Acta Mater 51:2701

    Article  CAS  Google Scholar 

  3. Radhakrishnan B, Zacharia T (2003) Model Simul Mater Sci Eng 11:307

    Article  Google Scholar 

  4. Radhakrishnan B, Sarma G, Zacharia T (1998) Acta Mater 46:4415

    Article  CAS  Google Scholar 

  5. Weygand D, Brechett Y, Lepinoux J (2000) Philos Mag B 80:1987

    Article  CAS  Google Scholar 

  6. Weygand D, Brechet Y, Lepinoux J (2001) Interface Sci 9:311

    Article  CAS  Google Scholar 

  7. Suwa Y, Saito Y, Onodera H (2007) Mater Sci Eng A 457:132

    Article  Google Scholar 

  8. Suwa Y, Saito Y, Onodera H (2008) Comput Mater Sci 44:286

    Article  CAS  Google Scholar 

  9. Takaki T, Tomita Y (2010) Int J Mech Sci 52:320. doi:10.1016/j.ijmecsci.2009.09.037

    Article  Google Scholar 

  10. Humphreys F (1997) Acta Mater 45:4231

    Article  CAS  Google Scholar 

  11. Rollett A, Mullins W (1997) Scr Mater 36:975

    Article  CAS  Google Scholar 

  12. Raabe D (2002) Annu Rev Mater Res 32:53

    Article  CAS  Google Scholar 

  13. Raabe D (1998) Computational materials science. Wiley-VCH, Weiheim

    Book  Google Scholar 

  14. Janssens KGF (2010) Math Comput Simul 80:1361. doi:10.1016/j.matcom.2009.02.011

    Article  Google Scholar 

  15. Yang H, Wu C, Li H, Fan X (2011) Sci China Technol Sci 54:2107

    Article  Google Scholar 

  16. Hesselbarth HW, Göbel IR (1991) Acta Metall Mater 39:2135. doi:10.1016/0956-7151(91)90183-2

    Article  CAS  Google Scholar 

  17. Davies C (1995) Scr Metall Mater 33:1139

    Article  CAS  Google Scholar 

  18. Davies C (1997) Scr Mater 36:35

    Article  CAS  Google Scholar 

  19. Goetz R, Seetharaman V (1998) Metall Mater Trans A 29:2307

    Article  Google Scholar 

  20. Marx V, Reher FR, Gottstein G (1999) Acta Mater 47:1219. doi:10.1016/s1359-6454(98)00421-2

    Article  CAS  Google Scholar 

  21. Davies C, Hong L (1999) Scr Mater 40:1145–1150

    Article  CAS  Google Scholar 

  22. Zheng C, Xiao N, Li D, Li Y (2008) Comput Mater Sci 44:507. doi:10.1016/j.commatsci.2008.04.010

    Article  CAS  Google Scholar 

  23. Zheng C, Xiao N, Li D, Li Y (2009) Comput Mater Sci 45:568. doi:10.1016/j.commatsci.2008.11.021

    Article  CAS  Google Scholar 

  24. Yu X, Chen S, Wang L (2009) Comput Mater Sci 46:66. doi:10.1016/j.commatsci.2009.02.008

    Article  CAS  Google Scholar 

  25. Seyed Salehi M, Serajzadeh S (2012) Comput Mater Sci 53:145

    Article  CAS  Google Scholar 

  26. Kazeminezhad M (2008) Mater Sci Eng A 486:202

    Article  Google Scholar 

  27. Raabe D, Becker RC (2000) Model Simul Mater Sci Eng 8:445

    Article  CAS  Google Scholar 

  28. Kugler G, Turk R (2006) Comput Mater Sci 37:284. doi:10.1016/j.commatsci.2005.07.005

    Article  CAS  Google Scholar 

  29. Zambaldi C, Roters F, Raabe D, Glatzel U (2007) Mater Sci Eng A 454–455:433. doi:10.1016/j.msea.2006.11.068

    Google Scholar 

  30. Roters F, Eisenlohr P, Hantcherli L, Tjahjanto DD, Bieler TR, Raabe D (2010) Acta Mater 58:1152. doi:10.1016/j.actamat.2009.10.058

    Article  CAS  Google Scholar 

  31. Gleiter H (1969) Philos Mag 20:821

    Article  CAS  Google Scholar 

  32. Doherty R, Hughes D, Humphreys F et al (1997) Mater Sci Eng A 238:219

    Article  Google Scholar 

  33. Demirel M, Kuprat A, George D, Rollett A (2003) Phys Rev Lett 90:16106

    Article  CAS  Google Scholar 

  34. Lan Y, Li D, Li Y (2006) Metall Mater Trans B 37:119

    Article  Google Scholar 

  35. Ding R, Guo ZX (2001) Acta Mater 49:3163. doi:10.1016/s1359-6454(01)00233-6

    Article  CAS  Google Scholar 

  36. Read W, Shockley W (1950) Phys Rev 78:275

    Article  CAS  Google Scholar 

  37. Kremeyer K (1998) J Comput Phys 142:243

    Article  CAS  Google Scholar 

  38. Jazaeri H, Humphreys FJ (2004) Acta Mater 52:3239. doi:10.1016/j.actamat.2004.03.030

    Article  CAS  Google Scholar 

  39. Zheng C, Raabe D, Li D (2012) Acta Mater 60:4768

    Article  CAS  Google Scholar 

  40. Zurob H, Brechet Y, Dunlop J (2006) Acta Mater 54:3983

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the Project of Introducing Talents of Discipline to Universities (No. B08040) and the National Fundamental Research of China (Project No. 2011CB605502).

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Authors and Affiliations

  1. State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Fengbo Han, Bin Tang, Hongchao Kou, Jinshan Li & Yong Feng

  2. Western Superconducting Technologies Co., Ltd., Xi’an, 710018, People’s Republic of China

    Yong Feng

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  1. Fengbo Han
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  2. Bin Tang
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  3. Hongchao Kou
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  4. Jinshan Li
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  5. Yong Feng
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Correspondence to Fengbo Han.

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Han, F., Tang, B., Kou, H. et al. Cellular automata modeling of static recrystallization based on the curvature driven subgrain growth mechanism. J Mater Sci 48, 7142–7152 (2013). https://doi.org/10.1007/s10853-013-7530-3

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  • DOI: https://doi.org/10.1007/s10853-013-7530-3

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