About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
A Semi-Supervised Approach to Characterizing Multiple Morphological Features in Microstructure Images |
Author(s) |
Arun Sathanur, William Frazier, Jing Wang, Ram Devanathan |
On-Site Speaker (Planned) |
Arun Sathanur |
Abstract Scope |
Complex microstructures comprise of multiple structural features such as grains, phases, voids, and precipitates. Automatic statistical characterization of individual volume fractions is challenging. Recent advances in deep learning using supervised machine learning rely on large, labeled datasets. Labeling large datasets of complex microstructure images at the pixel-level is an intractable task. Further these models also suffer from lack of interpretability.
In this work, we present an interpretable semi-supervised machine learning approach to solve this problem. This approach leverages unsupervised computer vision approaches to first segment the image into feature regions. Next, it builds interpretable descriptions of the segmented regions using Hu and Zernike image moments. Finally, with user inputs on a small number of labels, it uses a similarity graph between the segmented regions and a graph convolutional network to predict the labels on all of the segmented areas. We demonstrate our approach on synthetic and real-world microstructure datasets. |
Proceedings Inclusion? |
Undecided |