Random composites characterization using a classifier model

L. Liu, S. R. Arwade, Takeru Igusa

Research output: Contribution to journalArticle

Abstract

A new method is introduced for characterizing and analyzing materials with random heterogeneous microstructure. The method begins with classifiers which process information from high-fidelity analyses of small-sized simulated microstructures. These classifiers are subsequently used in a multipass moving window to identify subregions of potentially critical microscale behavior such as strain concentrations. In the derivation of the method, it is shown how information theory-based concepts can be formulated in a Bayesian decision theory framework that addresses microstructural issues. Furthermore, it is shown how a sequence of classifiers can be constructed to refine the analysis of microstructure. While the method presented herein is general, a relatively simple example of a two-dimensional, two-phase composite is used to illustrate the analysis steps.

Original languageEnglish (US)
Pages (from-to)129-140
Number of pages12
JournalJournal of Engineering Mechanics
Volume133
Issue number2
DOIs
Publication statusPublished - Feb 1 2007

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Keywords

  • Bayesian analysis
  • Composite materials
  • Damage
  • Decision making
  • Fracture
  • Microstructures
  • Statistics
  • Uncertainty principles

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering

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