About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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Fatigue in Materials: Fundamentals, Multiscale Characterizations and Computational Modeling
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Presentation Title |
A Machine Learning Model to Predict Fatigue Progression Using 3D Topology Data of Materials Obtained from X-ray Microscope |
Author(s) |
Gunjick Lee, Leslie Ching Ow Tiong, Donghun Kim, Seok Su Sohn |
On-Site Speaker (Planned) |
Gunjick Lee |
Abstract Scope |
It is very difficult to quantitatively determine how much fatigue failure has progressed. In particular, from the macroscopic point of view, it is more difficult to notice the progress of fatigue failure until almost immediately before failure occurs. However, from a microscopic point of view, void and micro-cracks change as the fatigue progresses. Therefore, it is necessary to correlate with microscopic information to determine how much fatigue failure has occurred, and to predict how much life is left.
X-ray microscope can measure the microstructure of a material in 3D form without damaging the material. The measured 3D topology data includes information of voids and micro-cracks inside the material, and it has a very complex shape for human analysis. In this study, we develop a machine learning model that predicts the fatigue progress and remaining life by using this complex 3D topology data. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Mechanical Properties |