About this Abstract |
Meeting |
6th International Congress on 3D Materials Science (3DMS 2022)
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Symposium
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6th International Congress on 3D Materials Science (3DMS 2022)
|
Presentation Title |
Using Deep Learning to Reconstruct Grains from Simulated Far-field Diffraction Data |
Author(s) |
Ashley Lenau, Yuefeng Jin, Ashley Bucsek, Stephen Niezgoda |
On-Site Speaker (Planned) |
Ashley Lenau |
Abstract Scope |
Far-Field High Energy Diffraction Microscopy (ff-HEDM) is invaluable for quantifying the orientation and elastic strain within the bulk of a 3D polycrystalline sample. However, it has limited ability to capture morphology or orientation and strain gradients. In this presentation, we demonstrate a deep learning framework that reconstructs the 3D grain shape given diffraction spots from a single grain. The deep learning framework is based on Pix2Vox, which uses an encoder-decoder structure to convert multiple 2D images of an object into a 3D volume render. Unlike standard Pix2Vox, which uses a single encode-decoder for all 2D images, our network utilizes an independent encoder for each diffraction spot. This network is demonstrated on synthetic 3D datasets. The ground truth grain shapes are generated via DREAM.3D and the simulated ff-HEDM data is generated by a virtual diffractometer. While still in the nascent stages of development, here we demonstrate high-fidelity 3D grain reconstruction. |
Proceedings Inclusion? |
Definite: Other |