|Title||Random composites characterization using a classifier model|
|Publication Type||Journal Article|
|Year of Publication||2007|
|Authors||Liu H., Arwade SR, Igusa T.|
|Journal||Journal of Engineering Mechanics|
|Keywords||Bayesian analysis, Composite materials, Damage, Decision making, fracture, Microstructures, Statistics, Uncertainty principles|
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.