A dynamic material library for the representation of single-phase polyhedral microstructures

Quantitative estimation of features from sectional images of microstructures is fundamental to determining the microstructural influences on material behavior. Geometric data extracted from planar materialographic sections through image analysis techniques are primarily lower-order descriptors of the microstructure. Such descriptors do not contain complete morphological information and hence cannot fully describe the material microstructure. This work uses a multi-class support vector machine classification method in conjunction with principal component analysis to build a dynamic and evolving microstructure library that can be used to efficiently describe single-phase polyhedral microstructures. Lower-order descriptors are initially used to associate the material microstructure to classes of microstructures stored in a digital material library. Complete description is obtained by quantifying the microstructure using the coefficients of a continuously updating basis within a class of the library. These techniques are essential towards the development of a dynamic materials library that will be able to analyze, classify and represent microstructures for modeling, accelerating the design and testing of single-phase polycrystalline materials.