| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Multimodal Characterization and Modeling of Additively Manufactured Alloys with Intentionally Seeded Pores |
| Author(s) |
Krzysztof S. Stopka, Yixuan Sun, Peter Kenesei, Jun-Sang Park, Jose Solano, Andrew Desrosiers, Amber Andreaco, Guang Lin, Michael Sangid |
| On-Site Speaker (Planned) |
Krzysztof S. Stopka |
| Abstract Scope |
Widespread adoption of additively manufactured (AM) components is hindered due to porosity that often governs minimum fatigue life. This work leverages several characterization and modeling techniques to examine the detrimental effects of porosity on fatigue resistance of alloy 718. Specimens intentionally seeded with pores were characterized using electron backscatter diffraction (EBSD), two computed tomography (CT) techniques with an order of magnitude difference in resolution, and strain-controlled low-cycle fatigue experiments. CT characterization alongside far-field and near-field high-energy x-ray diffraction microscopy (HEDM) revealed fatigue crack initiation and propagation mechanisms, associated micromechanical states of grains, and microstructure variability due to differences in heat conductivity during processing. The characterization data are used to instantiate microstructure models that undergo crystal plasticity (CP) simulations, which are used to predict fatigue lives with and without the presence of pores. This enables augmenting experimental data sets and the development of graph neural networks (GNN) to predict material response. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Computational Materials Science & Engineering |