| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Optimal Design of High-temperature, Oxidation-resistant Complex Concentrated Alloys |
| Author(s) |
Alejandro Strachan |
| On-Site Speaker (Planned) |
Alejandro Strachan |
| Abstract Scope |
Metallic alloys capable of maintaining high strength and oxidation resistance at high temperatures are key in aerospace and energy applications. State-of-art Ni-based superalloys are limited by their melting temperature of ~1300 C. In recent years, refractory complex concentrated alloys (RCCAs) emerged as a possible alternative, with high-temperature strength surpassing Ni superalloys. Unfortunately, their oxidation resistance is not ideal. The optimization of RCCAs is hindered by the high-dimensionality of the design space and the fact that full-scale experiments of the quantities of interest are costly and time consuming. I will discuss the combination of multi-fidelity experiments and physics-based modeling with machine learning tools with the ultimate goal of designing RCCAs with unprecedented combination of high-temperature strength and oxidation resistance. Specifically, I will discuss the integration of information from disparate sources and with different uncertainties into predictive models and the use of surrogate models to reduce the number of full-scale experiments required. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Machine Learning, Computational Materials Science & Engineering |