A machine learning approach to predict austenite finish temperature in quaternary NiTiHfPd SMAs
Materials Today Communications, cilt.38, 2024 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 38
- Basım Tarihi: 2024
- Doi Numarası: 10.1016/j.mtcomm.2023.107847
- Dergi Adı: Materials Today Communications
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
- Anahtar Kelimeler: Machine learning, Shape memory alloys, Alloy design, Material informatics, High entropy alloys
- Erciyes Üniversitesi Adresli: Evet
Özet
Machine learning (ML) has emerged as a promising tool for the design of multicomponent alloys due to their vast design spaces. Quaternary NiTiHfPd shape memory alloys (SMAs) possess unique potential to be employed in high-temperature actuation as well as damping systems. This study presents a machine learning approach using the currently available limited data regime to accelerate research on NiTiHfPd SMAs. To this end, a database of transformation temperatures of NiTiHfPd SMAs was compiled and expanded through compositional and post-processing features of the alloys. Various ML algorithms were utilized to predict the austenite finish temperature of NiTiHfPd SMAs and then validated through experiments.