A machine learning approach to predict austenite finish temperature in quaternary NiTiHfPd SMAs

Raji H., Rad M., ACAR E., Karaca H., Saedi S.

Materials Today Communications, cilt.38, 2024 (SCI-Expanded) identifier identifier

  • 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: Alloy design, High entropy alloys, Machine learning, Material informatics, Shape memory 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.