About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Sparse Sampling for 3D Electron Backscatter Diffraction |
Author(s) |
Zachary Varley, Gregory Rohrer, Marc De Graef |
On-Site Speaker (Planned) |
Zachary Varley |
Abstract Scope |
Electron backscatter diffraction (EBSD) is a popular microstructure analysis technique due to the direct, relatively high-resolution, measurement of phase and orientation. Three-dimensional EBSD (3D-EBSD) extends this microstructure analysis to a volume of material, measured in 2D sections, with a commensurate penalty in data acquisition time. The present work proposes a sampling routine to leverage the organized granular structure of orientation data present in microstructures, both within individual serial sections, and across consecutive ones. By using online machine learning to avoid redundant measurements, and then infilling unmeasured data during post-processing, significant theoretical savings are realized without offline training. User parameters allow tuning of the tradeoff between reconstruction accuracy and sampling speed. By coupling this sampling approach with real-time Kikuchi pattern indexing the authors aim to create a robust 3D-EBSD sampling technique which improves the Pareto frontier in the tradeoff between scan time and sample volume. |