About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
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Symposium
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Thermodynamics and Phase Diagrams Applied to Materials Design and Processing: An FMD/SMD Symposium Honoring Rainer Schmid-Fetzer
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Presentation Title |
Designing lightweight alloys based on CALPHAD modeling and machine learning |
Author(s) |
Alan A. Luo, Renhai Shi, Jianyue Zhang |
On-Site Speaker (Planned) |
Alan A. Luo |
Abstract Scope |
Lightweight alloys, including Mg-based and multicomponent concentrated (high-entropy) alloys, are of great interest due to their high specific mechanical and physical properties. It is challenging to design such alloys to achieve a synergistic combination of density, strength, ductility and manufacturability, due to the large composition space and complex property requirements. The first example is designing Mg sheet alloys for room temperature (RT) forming based on computational thermodynamic (CALPHAD) modeling and the critical understanding of alloying-processing-microstructure relationship using machine learning (ML) algorithms. In a second example, a hybrid CALPHAD/ML approach is developed to design lightweight multicomponent concentrated alloys with improved ductility and castability. This hybrid approach of CALPHAD modeling and machine learning shows promise in designing new materials. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, Magnesium, High-Entropy Alloys |