|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Driving Discovery: Integration of Multi-Modal Imaging and Data Analysis
||Structure Quantification, Property Prediction and 4D Reconstruction Using Limited X-ray Tomography Data
||Hechao Li, Somya Singh, C. Shashank Kaira, James Mertens, Nikhilesh Chawla, Yang Jiao
|On-Site Speaker (Planned)
In this talk, we present our recent work on utilizing limited x-ray tomography data for heterogeneous material structure quantification, property prediction, and microstructure reconstruction in 3D and 4D. We first show that an inverse superposition of properly normalized attenuated intensity along different x-ray paths leads to the probability map for the material, which provides the probably of finding the phase of interest at a point in the material sample. Spatial correlation functions, which are statistical morphological descriptors of the material, are readily computed from the associated probability map. Using effective medium theory and the computed correlation functions, accurate predictions of physical properties (e.g., elastic moduli) can then be obtained. Finally, we present a stochastic reconstruction procedure that generate accurate rendition of 3D material microstructure from a handful of tomographic projections. This stochastic procedure can be easily adapted to a dynamically reconstruct 4D structural evolution from small in situ data set.
||Planned: A print-only volume