About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing Fatigue and Fracture V: Processing-Structure-Property Investigations and Application to Qualification
|
Presentation Title |
Bayesian Inference of Elastic Constants and Texture Coefficients in Additively Manufactured Alloys Using Resonant Ultrasound Spectroscopy |
Author(s) |
Jeffrey O. Rossin, Patrick Leser, Chris Torbet, Stephen Smith, Samantha Daly, Tresa Pollock |
On-Site Speaker (Planned) |
Jeffrey O. Rossin |
Abstract Scope |
Additive manufacturing (AM) has enabled design and processing advantages for metallic components. Despite the advantages of AM, qualification of AM components for critical applications has limited its widespread usage. Further, AM microstructures are typically anisotropic. Resonant ultrasound spectroscopy (RUS) is a useful technique for determining elastic constants using inversion techniques such as Bayesian inference. Determining the elastic constants of built AM specimens with unknown polycrystalline texture is problematic, as any of the 21 possible independent unknown elastic constants can be non-zero. However, determining each of the 21 elastic constants by RUS inversion is not computationally feasible. Instead, texture coefficients (ODF) are transformed into second-order Hashin-Shtrikman bounds of the elastic constants. The calculated elastic constants are used to perform RUS inversions with a parallelizable sequential Monte Carlo (SMC) Python package. This is a novel use of RUS inversion to quantify the elastic constants and texture coefficients of arbitrarily anisotropic AM polycrystals. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Characterization, Other |