| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Refractory Metals 2023
|
| Presentation Title |
ULTIMATE: Machine Learning Guided Oxide Dispersion Strengthened Refractory HEA Discovery |
| Author(s) |
John Sharon, Ryan Deacon, Soumalya Sarkar, Kenneth Smith, Anthony Ventura, GV Srinivasan, Alexandru Cadar, Michael Gao |
| On-Site Speaker (Planned) |
John Sharon |
| Abstract Scope |
To achieve higher efficiency turbine operation and reduce fuel consumption, Raytheon Technologies Research Center, collaborating with NETL, is exploring oxide-dispersion strengthened (ODS) refractory high entropy alloys (HEAs) capable of operating at temperatures above Ni-superalloy. From research conducted under the ARPA-E ULTIMATE program, this talk will describe a machine learning framework assembled to aid in identifying HEA candidates. Results from experimental screening of the predicted HEAs will be highlighted. Additive trials were performed with the top candidate using nanoparticle modified feedstock to generate the ODS microstructure. Results from the additive trials and subsequent mechanical property characterization will also be detailed. The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001423. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
High-Entropy Alloys, High-Temperature Materials, Additive Manufacturing |