| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
|
| Presentation Title |
Non-destructive Determination of Single Crystal Elastic Constants in Additively Manufactured Alloys by Bayesian Inference and Resonant Ultrasound Spectroscopy |
| Author(s) |
Jeffrey O. Rossin, Patrick Leser, Kira Pusch, Carolina Frey, Chris Torbet, Stephen Smith, Samantha Daly, Tresa Pollock |
| On-Site Speaker (Planned) |
Jeffrey O. Rossin |
| Abstract Scope |
Qualification of additively manufactured components has limited the usage of these components in critical applications. Knowledge of both the single crystal elastic constants and texture of built AM microstructures is necessary for designing the anisotropic (as-built) components, but often unknown for AM materials. Determination of the single crystal elastic constants typically requires fabrication of a single crystal. For AM alloy compositions, single crystals are difficult to produce. Resonant ultrasound spectroscopy (RUS) inversion techniques have proven reliable for determining the texture and aggregate elastic constants of AM components, but required single crystal elastic constants as prior knowledge. In this work, the single crystal elastic constants are determined by informing the RUS inversion with polycrystalline texture information. A parallelizable sequential Monte Carlo (SMC) Python package reduces the computational (Bayesian inference) load by an order of magnitude or more. Full probability distributions are obtained for each parameter, resulting in robust uncertainty estimates. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, Mechanical Properties |