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Meeting MS&T21: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Now On-Demand Only - Computational or Experimental? Interpreting X-ray Absorption and Diffraction Contrast for Massive Non-destructive 3D Grain Mapping of Metals in Laboratory CT
Author(s) Andy Holwell, Hrishi Bale
On-Site Speaker (Planned) Andy Holwell
Abstract Scope Laboratory 3D X-ray microscopy (XRM) has previously been limited to imaging via material density differences within the sample. As such, single-phase polycrystalline materials (e.g. alloys) do not exhibit any absorption contrast to reveal the underlying grain microstructure. For microstructural crystallography, researchers have turned to time-consuming 3D electron backscatter diffraction in the scanning electron microscope in metallurgy, ceramics, semiconductors, pharmaceuticals, geology etc. Now, laboratory-based diffraction contrast tomography (DCT) can extract crystallographic information from single-phase polycrystalline samples, non-destructively and in three dimensions. DCT scans collect x-ray diffraction patterns which are deconvoluted for crystallographic reconstruction. Information on grain morphology, orientation, size and centroid position is available from the reconstructed 3D grain map, for studies of grain growth, tensile testing and aniostropy, delivering explicit grain structures for modeling. We show how LabDCT provides a routine solution for experimentally acquiring explicit 3D grain structures in various materials, enabling direct coupling of experimental results and simulations.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Building a Database of Fatigue Fracture Images to train a CNN
Characterization of Additively Manufactured ZrB2-SiC Ultra High Temperature Ceramics via X-ray Microtomography
Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials
Machine Learning and Image Processing Techniques for Materials Evaluation
Machine Learning Ferroelectrics: Bayesianity, Parsimony, and Causality
Multivariate Statistical Analysis (MVSA) for Hyperspectral Images
Now On-Demand Only - Computational or Experimental? Interpreting X-ray Absorption and Diffraction Contrast for Massive Non-destructive 3D Grain Mapping of Metals in Laboratory CT
Open-source Hyper-dimensional Materials Analytics Using Hyperspy
Quantitative Comparisons of 2D Microstructures with the Wasserstein Metric
Spatial and Statistical Representation of Strain Localization as a Function of the 3D Microstructure Using Multi-modal and Multi-scale Data Merging
Training Deep-learning Models with 3D Microstructure Images to Predict Location-dependent Mechanical Properties in Additive Manufacturing
Understanding Degradation and Failure Mechanisms by Multiscale and Multiresolution Electron Microscopy

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