About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithms Development in Materials Science and Engineering
|
Presentation Title |
3D Surrogate Modeling of Elasto-Viscoplastic FFT Simulations for Porosity-Driven Fatigue Prediction in Additive Manufacturing |
Author(s) |
Daniel Diaz, Xingyang Li, Elizabeth Holm, Anthony Rollett |
On-Site Speaker (Planned) |
Daniel Diaz |
Abstract Scope |
Additive manufacturing is revolutionizing the way we manufacture products, but properties are limited by the porosity produced during processing. To better understand the relationship between pore morphologies and fatigue we use an elasto-viscoplastic FFT simulation called MASSIF to calculate stress and strain field outputs and fatigue harmfulness rankings. To overcome the computational costs of simulating loading on hundreds of thousands of pores, we developed a 3D deep learning surrogate model that directly predicts the MASSIF stress and strain field outputs given a pore morphology. In our approach we incorporate active learning with Monte Carlo Dropout to train the model by selectively labeling pores from the unlabeled pool using MASSIF as the oracle labeler. This approach prioritizes labeling pore instances that the model is most uncertain of while leveraging clustering to maintain diversity in the growing training dataset. Our work opens the capacity for complex physical simulation on big data. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Modeling and Simulation, Additive Manufacturing |