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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Author(s) Hamidreza T-Sarraf, Sridhar Niverty, Nikhilesh Chawla
On-Site Speaker (Planned) Hamidreza T-Sarraf
Abstract Scope Time-dependent x-ray tomography (4D) is an excellent approach to understand material behavior. The quality of the x-ray projections is proportional to the x-ray exposure time. Also, image modalities such as phase-contrast and diffraction-contrast can be used to highlight different microstructural features. These factors extend the scan time and limit the number of scan iterations for time-evolved tomography experiments. Moreover, image processing and segmentation are extremely time-intensive for 4D tomographic data. Thus, there is a need to establish a robust workflow and algorithms that can render time-dependent x-ray datasets accurately and efficiently. In this study, we discuss the utility and efficiency of different Deep Convolutional Neural Network (DCNN) architectures and Generative Adversarial Network (GAN) algorithms for quality enhancement and automated segmentation of x-ray tomography datasets obtained by different modalities. These developments demonstrate the ability to drastically reduce x-ray data acquisition times, thereby opening the window for efficient 4D experiments.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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