About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
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Algorithms Development in Materials Science and Engineering
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Presentation Title |
Working towards a buildable and transferable deep learning model simulating full-field micromechanical evolution of polycrystalline materials |
Author(s) |
Ashley Lenau, Reeju Pokharel, Alexander Scheinker, Stephen Niezgoda |
On-Site Speaker (Planned) |
Ashley Lenau |
Abstract Scope |
High energy diffraction microscopy (HEDM) is a non-destructive characterization technique that studies a material’s evolution during a mechanical load. As valuable as HEDM is, the extensive planning, data collection and time needed for a successful experiment can make it an expensive endeavor. Numerically based crystal plasticity simulations may allow for better planning but are too slow to be used in real-time with an experiment. A deep learning model would allow for in real-time feedback that could focus data collection and increase the design space for experimental planning. However, deep learning is currently limited by the small datasets available. This work proposes a U-Net model to predict the full-field micromechanical evolution of a 3D Cu polycrystal and the transferability of this network is demonstrated on three different materials. The possibility of using the Cu-trained network as a building block to incorporate additional materials into the model’s capability is explored. |
Proceedings Inclusion? |
Planned: |
Keywords |
Mechanical Properties, Machine Learning, Computational Materials Science & Engineering |