AbstractsEngineering

In this thesis, several innovative methods for microstructure representation, reconstruction, property analysis and optimization are developed. Metallic microstructures are stochastic by nature and a single snapshot of the microstructure does not give the complete variability. However, experiments to assess the complete microstructure map of large aerospace structures are computationally prohibitive. One contribution of this thesis is on the development of a Markov Random Field approach to generate microstructures from limited experimental measurements of the microstructure. The result is a simple method for generating 3D microstructures from 2D micrographs that generates visually striking 3D reconstructions of anisotropic microstructures and is computationally efficient. Traditionally, finite elements techniques have been used to analyze properties of metallic microstructures. While finite element methods forms a viable approach for modeling a few hundred grains, a macroscale component such as turbine disk contains millions of grains and simulation of such `macroscale' components is a challenging task even when using current state-of-the-art supercomputers. In addition, finite element simulations are deterministic while polycrystalline microstructures are inherently stochastic in nature. An alternate class of schemes have been developed in this work that allows representation of microstructure using probabilistic descriptors.. We have employed this descriptor to represent the microstructure of an Iron-Gallium alloy (Galfenol). We have developed computational methods to link these properties with the ODF descriptor. Subsequently, we have employed data mining techniques to identify microstructural features (in the form of ODFs) that lead to an optimal combination of magnetostrictive strains, yield strength and elastic stiffness. Since ODF representation does not contain information about the local neighborhood of crystals, all crystals are subject to the same deformation and equilibrium across grain boundaries is not captured. We also done preliminary work on the use of higher order probability descriptors that contains neighborhood information. Of specific interest is the two – point correlation function(COCF) that arises in known expressions for mechanical and transport properties. The improvement in prediction of texture and strains achieved by the COCF approach is quantified through deformation analysis of a planar polycrystalline microstructure.