Research

Applied computer vision for microstructure characterization

Recent advances in computing power and automated microstructural image acquisition have opened the doors to data-driven quantitative microstructure analysis. Extraction of salient microstructure features is a crucial enabling component in this rapidly developing field of research; in the past decade the computer vision community has made enormous progress in this area, much of which has gone relatively unexplored by the quantitative microstructure analysis community. My research explores applications of image texture recognition algorithms to engineer efficiently computable generic microstructure descriptors, enabling quantitative microstructure comparisons between and across a wide variety of materials systems. Novel materials science applications include characterization and qualification of powder materials, exploratory analysis of large microstructure datasets, and extraction of quantitative relationships between materials processing and properties metadata and microstructural image features. The fusion of microstructure image analysis and contemporary machine vision techniques will facilitate development of robust autonomous microscopy systems, and may support quantitative engineering standards for complex hierarchical microstructure systems.

Monte Carlo simulations of abnormal grain growth

The goal of this project is to estimate the rate of occurrence of abnormal grain growth in microstructures with various crystallographic textures, and to identify local microstructural features and conditions associated with the development of abnormal grains. Deeper understanding of the factors responsible for abnormal grain growth could suggest strategies for controlling abnormal grain growth in nanocrystalline metals, widening their applicability in extremely tough, wear-resistant coatings.

My main research tool is the Monte Carlo Potts model for grain growth as implemented in the SPPARKS software package from Sandia.