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Meeting 2021 TMS Annual Meeting & Exhibition
Symposium Data Science and Analytics for Materials Imaging and Quantification
Presentation Title Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Author(s) Edward L. Pang, Peter M. Larsen, Christopher A. Schuh
On-Site Speaker (Planned) Edward L. Pang
Abstract Scope Resolving pseudosymmetry has long presented a challenge for electron backscatter diffraction (EBSD) and has been notoriously challenging in the case of tetragonal ZrO2 in particular. In this work, a method is proposed to resolve pseudosymmetry by building upon the dictionary indexing method and augmenting it with the application of global optimization to fit accurate pattern centers, clustering of the Hough-indexed orientations to focus the dictionary in orientation space, and interpolation to improve the accuracy of the indexed solution. We demonstrate that the proposed method can successfully and efficiently resolve pseudosymmetry in simulated patterns of tetragonal ZrO2 with high degrees of binning and noise as well as experimental datasets. We then explore the effect of crystal orientation and camera distance on the ability to resolve pseudosymmetry using intensity-based indexing approaches.
Proceedings Inclusion? Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancements in EBSD Using Machine Learning
Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Convolutional neural network-assisted recognition of nanoscale L12 ordered structures in face-centred cubic alloys
Deep Neural Network Facilitated Complex Imaging of Phase Domains
Dictionary Indexing of EBSD Patterns Assisted by Convolutional Neural Network
High Dimensional Analysis of Abnormal Grain Growth under Dynamic Annealing Conditions
Improved EBSD Indexing through Non-Local Pattern Averaging
Materials Characterization in 3D Using High Energy X-ray Diffraction Microscopy: Irradiated and Deformed Materials
Microstructure Image Segmentation with Deep Learning: from Supervised to Unsupervised Methods
Quantitative EBSD Image Analysis and Prediction via Deep Learning
Quantitative X-ray Fluorescence Nanotomography
Resolving Pseudosymmetry in Tetragonal ZrO2 Using EBSD with a Modified Dictionary Indexing Approach
Understanding Powder Morphology and Its Effect on Flowability Through Machine Learning in Additive Manufacturing
Understanding the Keyhole Dynamics in Laser Processing Using Time-resolved X-ray Imaging Coupled With Computer Vision and Data Analytics

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