Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning-Reference-Cited by-同舟云学术

Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning

Published:2023-06 Issue: Volume:35 Page:105806
ISSN:2352-4928
Container-title:Materials Today Communications
language:en
Short-container-title:Materials Today Communications

Author:

Machaka Ronald^ORCID,Radingoana Precious M.

Publisher

Elsevier BV

Subject

Materials Chemistry,Mechanics of Materials,General Materials Science

Reference45 articles.

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1. Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys;Computational Materials Science;2024-01

2. Datasets of narrow thermal hysteresis behaviour Ti-Ni-based HT-SMAs and the predicted accumulated local effects;Data in Brief;2023-12

3. Corrigendum to “Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning” [Mater. Today Commun. 35 (2023) 105806];Materials Today Communications;2023-12

4. Machine learning prediction of hardness in solid solution high entropy alloys;Materials Today Communications;2023-12