Deakin University
Browse

File(s) not publicly available

Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys

Version 2 2024-06-02, 14:48
Version 1 2023-02-14, 03:07
journal contribution
posted on 2024-06-02, 14:48 authored by UMHU Kankanamge, Johannes ReinerJohannes Reiner, X Ma, Santiago Corujeira GalloSantiago Corujeira Gallo, Wei XuWei Xu
AbstractWith the increasing use of CubeSats in space exploration, the demand for reliable high-temperature shape memory alloys (HTSMA) continues to grow. A wide range of HTSMAs has been investigated over the past decade but finding suitable alloys by means of trial-and-error experiments is cumbersome and time-consuming. The present work uses a data-driven approach to identify NiTiHf alloys suitable for actuator applications in space. Seven machine learning (ML) models were evaluated, and the best fit model was selected to identify new alloy compositions with targeted transformation temperature (Ms), thermal hysteresis, and work output. Of the studied models, the K-nearest neighbouring ML model offers more reliable and accurate prediction in developing NiTiHf alloys with balanced functional properties and aids our existing understanding on compositional dependence of transformation temperature, thermal hysteresis and work output. For instance, the transformation temperature of NiTiHf alloys is more sensitive to Ni variation with increasing Hf content. A maximum Ms reduction rate of 6.12 °C per 0.01 at.% Ni is attained at 30 at.% Hf, and with a Ni content between 50 and 51 at.%. Graphical abstract

History

Journal

Journal of Materials Science

Volume

57

Pagination

19447-19465

ISSN

0022-2461

eISSN

1573-4803

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

41

Publisher

SPRINGER

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC