|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Fusing Analytical Models and Hardness Experiments for Accelerated Optimization of Yield Strength in RHEAs
||Brent G. Vela, Danial Khatamsaz, Cafer Acemi, Prashant Singh, Douglas Allaire, Raymundo Arroyave, Ibrahim Karaman, Duane Johnson
|On-Site Speaker (Planned)
||Brent G. Vela
Refractory high entropy alloys (RHEAs) have gained attention as potential replacements for Ni-based superalloys in gas turbine applications. Improving their properties, such as their high-temperature yield strength, is crucial to their success. Unfortunately, exploring this vast chemical space using only experimental approaches is impractical due to the cost of testing of candidate alloys at operation-relevant temperatures. The lack of reasonably accurate strength models makes traditional Integrated Computational Materials Engineering (ICME) methods inadequate. We address this challenge by combining machine-learning models, easy-to-implement physics-based models, and inexpensive proxy experiments to develop robust and fast-acting models via Bayesian-updating. The framework combines data from one of the most comprehensive databases on RHEAs with a widely used physics-based strength model for BCC-based RHEAs into a compact predictive model that is significantly more accurate that the state-of-the-art. This model is amenable to ICME frameworks that screen for RHEAs with superior high-temperature properties.
||Planned: Metallurgical and Materials Transactions