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Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Machine Learning and Electron Backscatter Diffraction
Author(s)	Alessandro Previero, Guillaume de Certaines, Alex Foden, Thomas Benjamin Britton
On-Site Speaker (Planned)	Thomas Benjamin Britton
Abstract Scope	Electron backscatter diffraction (EBSD) is a data-rich, analysis technique. Each diffraction pattern contains information about the crystal phase and orientation of the material within the electron beam interaction volume. The advent of high-quality, dynamical-based electron diffraction simulations enables us to explore and test new analysis approaches. We apply (un)supervised machine learning methods, focusing on convolutional neural networks, transfer learning, and principal component analysis to augment the capabilities of the technique. Inspection of the trained networks also provides direction for the next generation of deterministic analysis procedures that we will use to classify phases and crystal orientations, especially for challenging cases such as differentiation of FCC-Cu patterns from austenite (also FCC). We will explore the benefits and disadvantages of these approaches considering current state-of-the-art diffraction pattern analysis methods.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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3D Morphological Characterization of Porous Cu by Vapor Phase Dealloying Zn-Cu Alloys

A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning

Advancement of Data Intensive Approaches in Materials Discovery and Design

Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics

Application of Machine Learning to Microstructure Quantification and Understanding

Artificial Intelligence Approaches to Microstructural Science

Automated Anomaly Detection in Unlabeled Computed Tomography Images

Basis Functions for Quantifying Grain Boundary Texture in Polycrystalline Microstructures

Characterizing GB Atomic Structures at Multiple Scales

Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques

Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data

Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks

Feature Engineering of Material Structure for Extracting Process-structure-property Linkages

GB Property Localization: Inference and Uncertainty Quantification of Grain Boundary Structure-property Models

Higher Order Spectral Terms in Grain Boundary Networks

Indexing of Electron Back-Scatter Diffraction Patterns Using a Convolutional Neural Network

Integrated Structural Methods Addressing Aviation Challenges in Composites

Investigating the Atomistic Nature of Grain Boundary Failure

Investigating the Effect of Solute Segregation to Grain Boundaries in Nanocrystalline Alloys Toward Stability and Strengthening

Investigations of Microstructural Effects on Porosity Evolution

Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy

Large Scale Microstructure Synthesis Using LEGOMAT: Application to Additive Manufacturing

Machine Learning and Electron Backscatter Diffraction

Machine Learning Approach for On-the-fly Crystal System Classification from Powder X-ray Diffraction Pattern

Machine Learning Approaches to Image Segmentation of Large Materials Science Datasets

Machine Learning Reinforced Crystal Plasticity Modeling of Titanium-Aluminum Alloys under Uncertainty

Methods for the Correction of Epistemic Resolution Error through Data Collection Process Simulations

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Monte Carlo Studies of EBSPs Spectroscopy

Neural Networks for Real-time Processing of Scanning Transmission Electron Microscopy Data

Parametric Models for Crystallographic Texture: Estimation and Uncertainty Quantification

Predicting Compressive Strength of Consolidated Solids from Features Extracted from SEM Images

Predicting Crack Location Using a Radial Distribution Function as a Unique Descriptor of Pore Networks

Predicting Microstructure-sensitive Fatigue-crack Path in 3D Using a Machine Learning Framework

The Grain Boundary Octonion: Metrics, Paths, and Fundamental Zones

Uncertainty Propagation in a Multiscale CALPHAD-reinforced Elastochemical Phase-field Model

Uncertainty Quantification of Far-field HEDM Measurements

Uncertainty Quantification Techniques Applied to Ductile Damage Predictions in the 3rd Sandia Fracture Challenge

Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images

X-Ray Computed Tomography of 3D Crack Lattices in Advanced Ceramics and their Effect on Mechanical Response

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