About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Computer Vision Assisted Automated Grain Segmentation and High-Throughput Composition Analysis with Scanning Electron Transmission Microscopy |
Author(s) |
Doruk Aksoy, Jenna Wardini, Timothy J. Rupert, William J. Bowman |
On-Site Speaker (Planned) |
Doruk Aksoy |
Abstract Scope |
High-throughput data-driven methods offer rapid screening tools for microstructural characterization, an otherwise slow and laborious task. In this work, annular dark field, and bright field images of an electrically conducting polycrystalline oxide are obtained with scanning transmission electron microscopy. First, grain boundaries are marked manually on acquired images to create masks for semantic image segmentation. Then, these images and masks are vectorized and processed using a data augmentation pipeline to simulate image acquisition deficiencies, in addition to avoid overfitting. Augmented images and masks are fed into a hyperparameter optimized convolutional neural network-based architecture to obtain high-fidelity grain segmentation maps. These maps are utilized as guides for obtaining spatially resolved spectroscopic data. Ultimately, leveraging computer vision in experimental image acquisition enables fast and detailed interrogation of the local composition and chemistry of grain boundary populations in polycrystalline materials. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Characterization, |