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 |
Orientation Imaging Microscopy Grain Reconstruction Using Deep Learning |
Author(s) |
Patxi Fernandez-Zelaia, Andrés Márquez Rossy, Quinn Campbell, Andrzej Nycz, Christopher Ledford, Michael M Kirka |
On-Site Speaker (Planned) |
Patxi Fernandez-Zelaia |
Abstract Scope |
Allotropic phase transformations take place in many commonly used structural materials following solidification. Phase reconstruction algorithms, which make inferences based on spatial structure present in orientation micrographs, are commonly used to estimate the underlying parent phase crystal structure. In this work we present a deep convolutional neural network architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy enables the training of our model using only four micrographs. The model generalizes well when tested on micrographs of a different material but its efficacy depends on the scale of microstructural features and the receptive field of the vision model. This work demonstrates that modern computer vision approaches can quantify complex spatial-orientation patterns present in orientation imaging micrographs. |
Proceedings Inclusion? |
Undecided |