|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Advances in Multi-Principal Elements Alloys X: Alloy Development and Properties
||Discovery of New Refractory High-entropy Alloys with Improved High-temperature Properties
||Stephen Giles, Debasis Sengupta, Scott Broderick, Krishna Rajan, Peter K Liaw
|On-Site Speaker (Planned)
Refractory high-entropy alloys (RHEA) are a promising class of alloys that show elevated-temperature yield strengths and have potential to use as high-performance materials in gas turbine engines. However, exploring the vast RHEAs compositional space experimentally is challenging, and only a small fraction of this space has been explored to date. The work demonstrates the development and use of a framework by coupling the state-of-the-art machine learning and optimization methods to intelligently explore the vast compositional space and drive the search in a direction that improves high-temperature yield strengths. On development of a robust yield strength prediction model, the coupled framework is used to discover new RHEAs with superior high temperature yield strength. We have shown that one can customize a RHEA composition to have maximum yield strength at a specific temperature. The model predictions are validated against experiments.
||High-Entropy Alloys, High-Temperature Materials, Machine Learning