About this Abstract |
| Meeting |
TMS Specialty Congress 2026
|
| Symposium
|
4th World Congress on High Entropy Alloys (HEA 2026)
|
| Presentation Title |
Design of NiTi-Based High Entropy Shape Memory Alloys Based on Transformation Temperature Predictions |
| Author(s) |
Leo Thiercelin, Noe Venet, Laurent Peltier |
| On-Site Speaker (Planned) |
Leo Thiercelin |
| Abstract Scope |
This study presents a data-driven framework for designing NiTi-based high-entropy shape memory alloys (HESMAs). The presentation will first address the construction of a comprehensive database of NiTi-based HESMAs through data mining, incorporating the four transformation temperatures (Ms, Mf, As, Af). It will then cover the selection and evaluation of machine learning models—including Extremely Randomized Trees, XGBoost, and Neural Networks—followed by a design methodology that explores the compositional space of (NiCuCo)(TiZrHf) HESMAs to optimize both the martensitic start temperature (Ms) and thermal hysteresis (Af − Ms). |
| Proceedings Inclusion? |
Undecided |