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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Author(s) Marc J. De Graef
On-Site Speaker (Planned) Marc J. De Graef
Abstract Scope 3D microstructure characterization by means of serial sectioning has become a mature field in recent years. The advent of fast sectioning systems (plasma FIB, femto-second laser) make possible the rapid acquisition of large data sets, measuring in the multi-Tb range. Converting this data into a usable 3D model remains a challenging task because many important samples have been exposed to an external influence (e.g., deformation) which often negatively impacts the signal-to-noise ratio of electron back-scatter patterns (EBSPs). Traditional Hough-based indexing approaches are now being replaced by machine learning algorithms, including dictionary-based indexing, convolutional neural networks, and spherical harmonic transform indexing. In this contribution we will highlight the state-of-the-art in EBSD data analysis as applied to large scale multi-layer multi-modal data sets; in particular we will describe example of the recently introduced spherical indexing technique which may, in the near future, rival the real-time performance of Hough-based indexing.
Proceedings Inclusion? Definite: At-meeting proceedings

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

3D Nanoprinting: An Integrated Approach of Experiments, Computer-aided Design and Simulations
4D STEM Data Acquisition, Analytics and Functional Material Property Extraction
A Machine Learning Approach to Independent Component Analysis for Nuclear Magnetic Resonance Spectra
Adversarial Networks for Digital Microstructure Generation
Application of a Statistical Analysis Technique for Characterizing the Deformation Behavior of the Material under Dynamic Impact Loading
Application of Artificial Neural Networks to Low Cycle Fatigue and Creep Data Processing for Power Plant Materials
Automated Defect Detection in Electron Microscopy with Machine Learning
Data Analytics for Correlative Multimodal Chemical and Functional Imaging
Data Science and the MGI
Deciphering the Atomic Origin of Glasses’ Properties by Machine Learning
Deep Learning and MC-X ray, toward Automatic Sample Segmentation
Digital Protocols for Statistical Quantification of Microstructure Features in Polycrystalline Nickel-based Superalloys
Human-in-the-loop Strategies for Dimensionality Reduction and Optimization in Materials Design
Model-based Reconstruction Algorithms for Time-of-Flight Neutron Tomography
Modeling and Simulation of Rare Events in Multidimensional Spaces
Multi-modal Data Fusion and 3D Reconstruction of Serial Sectioning Data
Neural Networks for Processing of Low Signal-to-noise Data in Scanning Probe Microscopy
P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
Phase Field Regularization for Optimal Grain Reconstruction of Noisy Images
Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Recent Advances in 3D Reconstruction Based on Spherical Indexing of EBSD Data
Structure Prediction and Property-based Optimization of Molecular Crystals with GAtor
Workflows for Curation and Analysis of Microstructure-Aware Materials Data: Application to Aging of U-Nb Alloys

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