Composition-based machine learning approach for <i>A</i><sub>f</sub> prediction in NiTiZr alloys
MATERIALS RESEARCH EXPRESS, vol.13, no.13, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 13 Issue: 13
- Publication Date: 2026
- Doi Number: 10.1088/2053-1591/ae7e4b
- Journal Name: MATERIALS RESEARCH EXPRESS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Compendex, INSPEC, Directory of Open Access Journals, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Open Archive Collection: AVESIS Open Access Collection
- Dokuz Eylül University Affiliated: Yes
Abstract
NiTiZr, which is among high-temperature shape memory alloys, stands out with its cost-effectiveness and superior properties. However, determining the optimal composition through traditional trial-and-error methods is time-consuming and costly. In this study, a comprehensive machine learning (ML) framework was developed to predict the austenite finish temperature (Af), a critical phase transformation parameter of NiTiZr alloys. Using experimental data compiled from the literature, five different algorithms were trained and their performances were compared. SHAP-based explainability analyses were used to identify the most influential factors on transformation temperatures. The developed model was also tested on an independent experimental sample and showed trend-level agreement with the experimentally observed transformation behavior. The findings show that ML models based solely on chemical composition, independent of processing history, can provide a useful framework for preliminary screening of candidate NiTiZr alloys. This study presents a composition-based and interpretable ML framework tailored to Af prediction in the NiTiZr system.