About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
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Advances in Multi-Principal Element Alloys V: Mechanical Behavior
|
| Presentation Title |
Phase and Property Prediction in Multi-Principal Element Alloys for Aerospace Applications Using Data-Driven Approaches |
| Author(s) |
Yiğit Can Çavdarlı, Ali Fethi Erdem, Taner Göktuğ Tekin, Sertaç Altınok, Eren Yunus Kalay |
| On-Site Speaker (Planned) |
Yiğit Can Çavdarlı |
| Abstract Scope |
Ti-6Al-4V alloy holds a crucial place in aerospace applications due to its high specific yield strength and excellent elastic modulus. However, its conventional manufacturing requires intensive energy, and machining wastes a significant portion of the original material. Designing compatible additively manufactured Multi-Principal Element Alloys (MPEAs) can significantly reduce both cost and environmental impact. For composition selection, extensive data were gathered and refined for machine learning applications to predict phase structures and mechanical properties of various compositions. Phase structure, specific yield strength, specific ultimate tensile strength, and elongation were predicted with accuracies of 83%, 92.68%, 92.37%, and 82.75%, respectively. In lab-scale tests, the selected MPEA exhibited 1700 MPa yield strength and 45% elongation under compression, with a specific compressive yield strength of 252 MPa·cm³/g—higher than Ti-6Al-4V (~220 MPa·cm³/g). Mechanical properties of large-scale specimens produced via laser powder bed fusion will also be presented. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Mechanical Properties, Machine Learning |