Texture, Stress, and Microstructure

Texture, Stress, and Microstructure / 1991 / Article
Special Issue

Ninth International Conference on Textures of Materials

View this Special Issue

Open Access

Volume 14 |Article ID 608012 | https://doi.org/10.1155/TSM.14-18.13

Brent L. Adams, E. Turan Onat, "N-Point Measures of Polycrystalline Microstructure: Measurement, Representation and Applications", Texture, Stress, and Microstructure, vol. 14, Article ID 608012, 12 pages, 1991. https://doi.org/10.1155/TSM.14-18.13

N-Point Measures of Polycrystalline Microstructure: Measurement, Representation and Applications


Advanced models for engineering properties of polycrystalline materials require description of the spatial distribution of lattice phase and orientation. For this purpose the n-point statistical measures present a natural extension of the orientation distribution function, which is equivalent to the one-point measure of lattice orientation in single-phase microstructures. This paper describes the origin of the n-point measures in the context of statistical theory, and some aspects of their experimental determination. Fourier representation of the n-point measures in terms of tensorial basis functions is described. It is proposed that tensorial representations have some natural advantages over the ordinary representation using generalized spherical functions. An example of the application of occurrence of the n-point statistics of lattice orientation in a theory of creep in polycrystals is presented, and some limited comparisons with the uniform strain-rate and self-consistent theories are described.

Copyright © 1991 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Related articles

No related content is available yet for this article.
 PDF Download Citation Citation
 Order printed copiesOrder

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.