Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-25T02:03:25.255Z Has data issue: false hasContentIssue false

Impact of boundary nucleation on product grain size distribution

Published online by Cambridge University Press:  31 January 2011

W. S. Tong
Affiliation:
Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
J. M. Rickman
Affiliation:
Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
K. Barmak
Affiliation:
Department of Materials Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
Get access

Abstract

We examine quantitatively the impact of boundary nucleation on the size distribution of product grains in a computer simulation of a two-dimensional phase transformation. This is accomplished by determining the probability distribution of product grain areas under different nucleation conditions. Specifically, a comparison of the moments of normalized area distributions of product grains arising from site-biased nuclei with the corresponding moments of the area distribution of Voronoi grains reveals those spatial features of the collection of catalytic sites which most affect product microstructure. The impact of other relevant length scales, including the square root of the inverse nucleation site density, the lattice parameter, and the system size, on microstructure is also discussed.

Type
Articles
Copyright
Copyright © Materials Research Society 1997

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

REFERENCES

1.Gilbert, E. N., Ann. Math. Stat. 33, 958 (1962).CrossRefGoogle Scholar
2.Rickman, J. M., Tong, W. S., and Barmak, K., Acta Mater. 45, 1153 (1997).Google Scholar
3.Weaire, D. and Wejchert, J., in Computer Simulation of Microstructural Evolution, edited by Srolovitz, D. J. (The Metallurgical Society, Warrendale, PA, 1986).Google Scholar
4.Mahin, K. W., Hanson, K., and Morris, J. W., Jr., Acta Metall. 28, 443 (1980).Google Scholar
5.Frost, H. J. and Thompson, C. V., Acta Metall. 35, 529 (1987).CrossRefGoogle Scholar
6.Hong, Q. Z., Barmak, K., and Clevenger, L. A., J. Appl. Phys. 72, 3423 (1992).CrossRefGoogle Scholar
7.Hong, Q. Z., Barmak, K., Hong, S. Q., and Clevenger, L. A., J. Appl. Phys. 74, 4958 (1993).CrossRefGoogle Scholar
8. For consistency, throughout this paper we will denote a quantity normalized by its average with a prime.Google Scholar
9.Kiang, T., Z. Astrophys. 48, 433 (1966).Google Scholar
10.Weaire, D., Kermode, J. P., and Wejchert, J., Philos. Mag. B 53, L101–105 (1986).Google Scholar
11.Freund, J. E. and Walpole, R. E., Mathematical Statistics (Prentice-Hall, Englewood Cliffs, NJ, 1980).Google Scholar
12. Clearly there is an upper limit to the density n which can be studied. If is of the order of lattice parameter, then it will not be possible to accurately sample relatively small grains.Google Scholar
13.Meijering, J. L., Philips Res. Rep. 8, 270 (1953).Google Scholar
14.Coffey, K. R., Clevenger, L. A., Barmak, K., Rudman, D. A., and Thompson, C. V., Appl. Phys. Lett. 55, 852 (1989).CrossRefGoogle Scholar
15.Barmak, K., Michaelsen, C., Rickman, J. M., and Dahms, M., in Polycrystalline Thin Films—Structure, Texture, Properties and Applications II, edited by Frost, H. J., Parker, M. A., Ross, C. A., and Holm, E. A. (Mater. Res. Soc. Symp. Proc. 403, Pittsburgh, PA, 1996), p. 51.Google Scholar
16.Frost, H. J. and Thompson, C. V., in Computer Simulation of Microstructural Evolution, edited by Srolovitz, D. J. (The Metallurgical Society, Inc., Warrendale, PA, 1986), p. 33.Google Scholar