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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 Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Author(s) Pradyumna Elavarthi, Arun J Bhattacharjee, Anca Ralescu, Ashley E Paz y Puente
On-Site Speaker (Planned) Pradyumna Elavarthi
Abstract Scope X-ray tomography is extensively used in materials science for nondestructive detection of phases and porosity in 3D. In-situ synchrotron tomography is used to track the evolution of porosity in real time. A fully convolutional neural network was used to segment and classify two different types of pores that were observed during in-situ x-ray tomography of pack titanized Ni wires. However, it is difficult to quantify these two pore types separately because of their same intensity and varying shapes. Hence, a series of classical computer vision techniques were used to create initial masks for training a deep learning model. A fully convolutional neural network based on the architecture of U-net was designed and trained on the created masks. Various domain specific data-augmentation techniques were used in the training to improve the generalizability of the model. An F1 score of 0.96 and 0.95 was achieved for pore types I and II, respectively.
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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