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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Novel Strategies for Rapid Acquisition and Processing of Large Datasets from Advanced Characterization Techniques
Presentation Title Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Author(s) Daniel J. Savage, Zhangxi Feng, Christopher Biwer, Michael McKerns, Sven Vogel
On-Site Speaker (Planned) Daniel J. Savage
Abstract Scope Diffraction experiments often result in tens to thousands of patterns from which information can be extracted ranging from microstructure to equation of state. The extraction happens by fitting models of the diffraction process which describe the instrument (e.g. sample-to-detector distances and angles), the sample’s crystal structure (e.g. crystallographic space group and lattice parameters), and the microstructure (e.g. phase fractions and texture). Finding the set of parameters that best describes a measurement is hard to do in a robust way without expert intervention and in a way that leverages parallel resources (critical for real-time analysis). Benchmark examples of the open-source Python scripting framework for MAUD Rietveld software (MILK) will be presented with the Python Rietveld optimization and uncertainty quantification package (Spotlight) which together enable the scalable Rietveld optimization strategy and advanced analysis workflows (e.g. sampling uncertainty in key parameters).
Proceedings Inclusion? Planned:
Keywords Characterization, Modeling and Simulation, Other


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Deep Learning-Driven Semantic Segmentation of large 4D Lab-Scale X-ray Tomography Data for Quantification of Microstructural Features
Directional Reflectance Microscopy: Beyond Conventional Crystal Orientation Mapping
Enabling Uninterrupted In-situ X-ray Experiments through Rapid Data Feedback and On-the-fly Experiment Optimization
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Hierarchical Bayesian Data Analysis for Accelerating Structural Materials Characterization
HPC+AI@Edge Enabled Real-Time Materials Characterization
Melt Pool Quantification from In Situ Radiography of Directed Energy Deposition of Nickel Superalloys
New Strong and Ductile Titanium-oxygen-iron Alloys Enabled by AM and Insights from Multiscale Microscopy
Probabilistic Orientation Analysis via Direct ODF Calculation from Far Field HEDM
Quantitative 2D and 3D Characterization of Precipitates Microstructure in the Additively Manufactured Titanium Alloy
Real-Time In-Situ Characterization with Web Technologies at Any Scale
Streamlining Engineering Diffraction Analysis Using the MAUD Interface Language Kit (MILK)
Understanding Relaxation Dynamics Beyond Equilibrium Using AI-Informed X-ray Photon Correlation Spectroscopy
Using Video Games for Training Data on Microstructural Design
Utilizing Advanced Computer Vision Techniques Based on Machine Learning and Artificial Neural Networks to Process Micrographs of Ni-base Superalloys
Utilizing Deep Learning Techniques to Accelerate X-ray Absorption and Diffraction Contrast Imaging

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