About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Data Driven Ductility Modeling in Refractory High Entropy Alloys |
| Author(s) |
Kate L. M. Elder, Brandon Bocklund, Adam Krajewski, Lassi Linnala, Nicholas Ury, Tatu Pinomaa, Anssi Laukkanen, Scott K. McCall, Joseph T. McKeown, Aurélien Perron |
| On-Site Speaker (Planned) |
Brandon Bocklund |
| Abstract Scope |
The subset of high entropy alloys (HEAs) consisting of refractory metals (RHEAs) with a body-centered cubic (BCC) structure is known to maintain a high yield strength at elevated temperatures. However, just tailoring RHEAs to be strong and BCC-stable is insufficient and ductility, which is more challenging to model, must be incorporated into any alloy design process. Several ductility indicators: D parameter, valence electron concentration, room temperature yield strength, Pugh ratio, and Rice-Thompson criteria are investigated for single-phase BCC RHEAs from the Al-Ce-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr element palette. A new, transfer-learning-based model for the D parameter is developed using the pySIPFENN featurization framework. These ductility models are compared with experimental compression and tension data, from the ULTrahigh TEmperature Refractory Alloys (ULTERA) project. This work identifies how the diTerent indicators perform when compared to experimental data and highlights the trends seen for different alloy families and processing conditions. Prepared by LLNL under Contract DE-AC52-07NA27344. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, ICME |