Journal article
Understanding and visualizing microstructure and microstructure variance as a stochastic process
Acta materialia, v 59(16), pp 6387-6400
01 Sep 2011
Abstract
The study of microstructure property relationships is a defining concept in the field of materials science and engineering. Despite the paramount importance of microstructure to the field a rigorous systematic framework for the description of structural variance between samples of materials with the same processing history and between different materials classes has yet to be adopted. Here the authors utilize the formalism of stochastic processes to develop a statistical definition of microstructure and develop measures of structural variance in terms of the measured variance of estimators of higher order probability distributions. Principal component analysis (PCA) of higher order distributions is used to produce visualization of the space spanned by an ensemble of microstructure realizations and for quantification of the structural variance within the ensemble. The structural variance is correlated with the variance in properties and structure/property maps are produced in the PCA space. Published by Elsevier Ltd. on behalf of Acta Materialia Inc.
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Details
- Title
- Understanding and visualizing microstructure and microstructure variance as a stochastic process
- Creators
- Stephen R. Niezgoda - Los Alamos National LaboratoryYuksel C. Yabansu - Drexel UniversitySurya R. Kalidindi - Drexel University
- Publication Details
- Acta materialia, v 59(16), pp 6387-6400
- Publisher
- Elsevier
- Number of pages
- 14
- Grant note
- N000140510504 / DARPA-ONR; United States Department of Defense; Defense Advanced Research Projects Agency (DARPA); Office of Naval Research US Department of Energy through LANL/LDRD
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Materials Science and Engineering
- Web of Science ID
- WOS:000294936600019
- Scopus ID
- 2-s2.0-80051794631
- Other Identifier
- 991021901010604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Materials Science, Multidisciplinary
- Metallurgy & Metallurgical Engineering