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Meeting MS&T24: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Author(s) Woohyun Eum, Yi Wang, Kang Yang, Vishal Yadav, Michael R. Tonks, Amanda R. Krause, Joel B. Harley
On-Site Speaker (Planned) Woohyun Eum
Abstract Scope Laboratory diffraction contrast tomography (LabDCT) is a laboratory-scale x-ray tomography for non-destructively imaging grains. Its combination with machine learning, such as the recent Physics-Regularized Interpretable Machine Learning Microstructure Evolution (PRIMME) framework, has the potential to elucidate mechanistic relationships within microstructural evolution. However, appropriate data preprocessing and cleaning is essential for machine learning, and common preprocessing pipelines for segmenting and matching grains between consecutive LabDCT measurements is often insufficient, sometimes failing to track up to 10% of grains. This study employs strategies to better identify and match grains across time by minimizing differences in grain centroids and orientation through the Hungarian algorithm. We validate our approach by observing its performance with simulated microstructure data as well as the error distributions for matched grain centroids and orientations in experimental data. Results show our approach to mitigate experimental noise and improve the PRIMME framework's ability to learn from experimental data.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Advancing AI-Driven Analysis of Synchrotron Data via FAIR Practices, Ontology and Knowledge Graphs
Advancing Sustainable Agriculture through Multiscale Spatiotemporal Data Integration and High-Performance Computing
Aligning Grains in Time-Series Laboratory Diffraction Contrast Tomography (LabDCT) Data for Machine Learning of Microstructure Evolution
Autonomous approaches for determining structure-processing-property relationships in materials
Categorization of Fracture Surfaces using Deep Learning-enabled 2D Image Analysis
Deep Learning Accelerated Lab-Scale X-Ray Computed Tomography of Low-Melting-Point Solder Alloys Used in Heterogeneously Integrated Semiconductor Packages
Enhancing Rietveld Refinement Analyses with Machine Learning Techniques
Extraction of Local Scalar 3D Microstructural Properties of SOFC Electrodes from 2D Micrographs using Convolutional Neural Networks.
Feature Extraction from SEM Images of Fatigue Fracture Surfaces
Foundation models for multimodal data mining with applications in materials science.
Hierarchical Bayesian Models for Automating Structural Materials Characterization
Machine Learning Enhanced Data Analytics for Transmission Electron Microscopy
Synthetic 3D Microstructure Generation of Solid Oxide Cell Electrodes using Denoising Diffusion Models

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