About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
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Symposium
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Artificial Intelligence Applications in Integrated Computational Materials Engineering
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Presentation Title |
Machine Learning and High-Throughput Computations Guided Development of High Temperature Oxidation-Resisting Ni-Co-Cr-Al-Fe High-Entropy Alloys |
Author(s) |
Xingru Tan, William Trehern, Aditya Sundar, Yi Wang, Saro San, Tianwei Lu, Fan Zhou, Ting Sun, Youyuan Zhang, Yuying Wen, Zhichao Liu, Michael Gao, Shanshan Hu |
On-Site Speaker (Planned) |
Xingru Tan |
Abstract Scope |
The Ni-Co-Cr-Al-Fe high-entropy alloy (HEA) system have been demonstrated to possess exceptional oxidation resistance, making them promising coating candidates to protect critical components in turbine power system. However, only a few Ni-Co-Cr-Al-Fe HEAs have been explored with the traditional trial-and-error alloy design approaches. In this paper, we developed an efficient technique with the aid of machine learning (ML) and high throughput computations, enabling the rapid screening of high-temperature oxidation-resistant Ni-Co-Cr-Al-Fe HEAs. A well-trained ML model was developed to correlate elemental composition with parabolic rate constant (Kp). High throughput CALPHAD and coefficient of thermal expansion calculation, coating manufacturing criteria, and thermodynamic-informed ranking have been adopted to screen promising candidates. The predictive oxidation resistance of selected HEA compositions were further experimentally verified. This work illustrates an efficient and reliable pathway for the development of high-performance coatings in the realm of next-generation turbine engine technology. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, High-Entropy Alloys, High-Temperature Materials |