Calibrated localization relationships for elastic polycrystalline aggregates

Multiscale modeling of material systems demands novel solution strategies to simulating physical phenomena that occur in a hierarchy of length scales. Majority of the current approaches involve one way coupling such that the information is transferred from a lower length scale to a higher length scale. To enable bi-directional scale-bridging, a new data-driven framework called Materials Knowledge System (MKS) has been developed recently. The remarkable advantages of MKS in establishing computationally efficient localization linkages (e.g., spatial distribution of a field in lower length scale for an imposed loading condition in higher length scale) have been demonstrated in prior work. In prior work, the viability and computational advantages of the MKS approach were demonstrated in a number of case studies involving multiphase composites, where the local material state in each spatial bin of the volume element was permitted to be any one of a limited number of material phases (i.e., restricted to a set of discrete local states of the material). As a major extension, the MKS framework has been extended for polycrystalline aggregates which need to incorporate crystal lattice orientation as a continuous local state. Another important extension of the MKS approach that permits calibration of the influence kernels of the localization linkages for an entire class of low to moderate contrast material systems will also be presented. This major extension of the MKS framework for elastic deformation of polycrystals is achieved by employing compact Fourier representations of functions defined in the crystal orientation space. The viability of this new formulation will be presented for several case studies involving single and multi-phase polycrystals.Ph.D

Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses

Eutectic solidification gives rise to a number of distinct microstructure patterns that might include lamella, rods and labyrinths in binary alloys. However, as the number of phases and components increases, the number of possible patterns that might be obtained during bulk solidification also become larger. While the morphological attributes of binary eutectic solidification have been fairly well understood, the same is not true for ternary and higher multicomponent alloys. In this paper, we study and quantify microstructures in ternary alloys as a function of two essential parameters, namely, the volume fraction of the solid phases and the surface energies of the interfaces (in particular the solid-liquid interfaces). For the selected ensemble of microstructures, quantification and classification were carried out using a recently developed data-driven (objective) approach based on principal component analyses of 2-point correlations. It is demonstrated that the method is capable of analyzing and quantifying the similarity/difference measures between the elements of the selected ensemble of microstructure

Supplementary Material for KDD2017: Efficient and Accurate Materials Structure-Property Linkages using 3D Convolutional Neural Networks

Code and Data to reproduce the work described in the KDD submission: Efficient and Accurate Materials Structure-Property Linkages using 3D Convolutional Neural Networks