Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-02T23:51:40.026Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling of Subgrain Growth Kinetics: 3D Monte-Carlo Simulation

Published online by Cambridge University Press:  31 January 2011

Tomoaki Suzudo ,
Hideo Kaburaki  and
Mitsuhiro Itakura
Tomoaki Suzudo
Affiliation:
[email protected], Japan Atomic Energy Agency, Center for Computational Science & e-Systems, Tokai-mura, Japan
Hideo Kaburaki
Affiliation:
[email protected], Japan Atomic Energy Agency, Center for Computational Science & e-Systems, Tokai-mura, Japan
Mitsuhiro Itakura
Affiliation:
[email protected], Japan Atomic Energy Agency, Center for Computational Science & e-Systems, Taito-ku, Japan
Get access

Abstract

We used a three-dimensional Monte Carlo method to investigate subgrain growth with different initial values for average grain-boundary misorientation, and found that abnormal grain growth emerges for relatively large average misorientation, with remaining cases revealing an exponential kinetics of subgrain growth. We also found that the growth exponent was ˜4.4, and that it was virtually independent of the average misorientation. Self-similarity of the misorientation distribution was observed during growth.

Keywords

simulationgrain sizegrain boundaries
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1225: Symposium HH – Multiscale Polycrystal Mechanics of Complex Microstructures , 2009 , 1225-HH02-04
Copyright
Copyright © Materials Research Society 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1 Humphreys, F.J. Hatherly, M. Recrystallization and Related Annealing Phenomena, Pergamon Press, 1995.Google Scholar
2 Sandström, R., Lehtinen, B. Hedman, E. Groza, I. Karlsson, S. J. Mater. Sci. 13, 1229 (1978).Google Scholar
3 Varma, S.K. Willits, B.L. Metall. Trans. A 15 A, 1502 (1984).Google Scholar
4 Varma, S.K. Mater. Sci. Eng. 82, L19 (1986).Google Scholar
5 Varma, S.K. Wesstrom, B.C. J. Mater. Sci. Lett. 7, 1092 (1988).Google Scholar
6 Furu, T. ørsund, R., Nes, E. Acta Metell. Mater. 43, 2209 (1995).Google Scholar
7 Huang, Y. Humphreys, F.J. Acta Mater. 48, 2017 (2000).Google Scholar
8 Humphreys, F.J. Scripta Metall., 27, 1557 (1992).Google Scholar
9 Holm, E.A. Miodownik, M.A. Rollett, A.D. Acta Mater., 51, 2701 (2003).Google Scholar
10 Humphreys, F.J. Acta Mater., 45, 4231 (1997).Google Scholar
11 Gottstein, G. Shivindlerman, L.S. Scripta Metall. 27, 1515 (1992).Google Scholar
12 Ivasishin, O.M. Shevchenko, S.V. Vasiliev, N.L. Semiatin, S.L. Acta Mater. 51, 1019 (2003).Google Scholar
13 Ivasishin, O.M. Shevchenko, S.V. Semiatin, S.L. Scripta Mater. 50, 1241 (2004).Google Scholar
14 Raabe, D. Computational Material Science, WILEY-VCH Verlag GmbH, 1995.Google Scholar
15 Rollett, A.D. Manohar, P. in: Raabe, D. Roters, F. Barlat, F. Chen, L.Q. (Eds.), Continuum Scale Simulation of Engineering Materials: Fundamentals-Microstructures-Process Applications, Wiley-VCH, 2006, pp. 77–114.Google Scholar
16 Janssens, K.G.F. Raabe, D. Kozeschnik, E. Miodownik, M.A. Nestler, B. Computational Materials Engineering -An introduction to Microstructure Evolution, Burlington: Elsevier Academic Press, 2007 Google Scholar
17 Metropolis, N. Rosenbluth, A.W. Rosenbluth, M.N. Teller, A.H. J. Chem. Phys. 21, 1087 (1953).Google Scholar
18 Read, W.T. Shockley, W. Phys. Rev. 78, 275 (1950).Google Scholar
19 Suzudo, T. and Kaburaki, H. Phys. Lett. A 373, 4484 (2009).Google Scholar
20 Miodownik, M.A. Smereka, P. Srolovitz, D.J. Holm, E.A. Proc. R. Soc. Lond. A 457, 1807 (2001).Google Scholar
21 Hughes, D.A. Liu, Q. Chrzan, D.C. Hansen, N. Acta Mater. 45, 105 (1997).Google Scholar