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Inverse processing-structure relation for the nucleation and growth mechanism

Published online by Cambridge University Press:  07 February 2013

Mark Jhon ,
Yang Hao Lau ,
Siu Sin Quek  and
David T. Wu
Mark Jhon
Affiliation:
Institute of High Performance Computing, A*STAR 1 Fusionopolis Way, #16-16 Connexis Singapore, 138632, Singapore
Yang Hao Lau
Affiliation:
Institute of High Performance Computing, A*STAR 1 Fusionopolis Way, #16-16 Connexis Singapore, 138632, Singapore
Siu Sin Quek
Affiliation:
Institute of High Performance Computing, A*STAR 1 Fusionopolis Way, #16-16 Connexis Singapore, 138632, Singapore
David T. Wu
Affiliation:
Institute of High Performance Computing, A*STAR 1 Fusionopolis Way, #16-16 Connexis Singapore, 138632, Singapore
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Abstract

The formation of realistic, polycrystalline microstructures can be simulated by modeling the kinetics of nucleation and growth. However, it is difficult to perform the inverse simulation, where details of the nucleation and growth process are inferred from geometric properties of the final microstructure. In the present study, we develop a methodology for solving the inverse problem for interface-limited growth in 1D, utilizing a reverse Monte Carlo (RMC) algorithm. The algorithm produces a time dependent nucleation rate that gives a grain size distribution closest to a target distribution. Its results may be used to understand the limitations of manipulating the grain boundary distributions through temperature alone.

Keywords

microstructurenucleation & growthgrain size
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1524: Symposium RR – Advanced Multiscale Materials Simulation―Toward Inverse Materials Computation , 2013 , mrsf12-1524-rr08-09
Copyright
Copyright © Materials Research Society 2013

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References

REFERENCES

Fang, Z. and Patterson, B., Acta Metall. Mater. 41, 2017 (1993).10.1016/0956-7151(93)90372-YCrossRefGoogle Scholar
Koch, C.C., Morris, D.G., Lu, K., and Inoue, A., MRS Bull. 24, 54 (1999).10.1557/S0883769400051551CrossRefGoogle Scholar
Sanders, P.G., Youngdahl, C.J., and Weertman, J.R., Materials Sci. Eng. A 234–236, 77 (1997).CrossRefGoogle Scholar
Sanders, P.G., Eastman, J.A., and Weertman, J.R., Acta Mater. 45, 4019(1997).10.1016/S1359-6454(97)00092-XCrossRefGoogle Scholar
Wang, Y., Chen, M., Zhou, F., and Ma, E., Nature 419, 912 (2002).10.1038/nature01133CrossRefGoogle Scholar
Valiev, R.Z., Alexandrov, I.V., Zhu, Y.T., and Lowe, T.C., J. Mater. Res. 17, 5 (2002).CrossRefGoogle Scholar
McGreevy, R.L. and Pusztai, L., Molecular Sim. 1, 359 (1988).10.1080/08927028808080958CrossRefGoogle Scholar
McGreevy, R.L., Journal of Physics: Condensed Matter 13, R877 (2001).Google Scholar
Tong, W.S., Rickman, J.M., and Barmak, K., Acta Metallurgica 47, 435 (1999).Google Scholar
Gross, D. and Li, M., Applied Physics Letters 80, 746 (2002).CrossRefGoogle Scholar
Kühn, M. and Steinhauser, M.O., Applied Physics Letters 93, 034102 (2008).CrossRefGoogle Scholar
Chiu, J.W. and Wu, D.T., in preparation.Google Scholar
Meijering, J. L., Philips Res. Rep. 8, 270 (1953).Google Scholar
Cahn, J.W., in Thermodynamics and Kinetics of Phase Transformations (Materials Research Society, Pittsburgh, PA, 1996), vol. 398 of Materials Research Society Symposia Proceedings, pp. 425–438.Google Scholar
Pineda, E., Bruna, P., and Crespo, D., Physical Review E 70, 066119 (2004).10.1103/PhysRevE.70.066119CrossRefGoogle Scholar
Farjas, J. and Roura, P., Physical Review B 78, 144101 (2008).10.1103/PhysRevB.78.144101CrossRefGoogle Scholar