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

Hatim Raji, Milad Rad, Emre Acar, Haluk Karaca, Soheil Saedi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number107847
JournalMaterials Today Communications
Volume38
DOIs
StatePublished - Mar 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

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

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Materials Chemistry

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