Issues $\rightarrow$ Volume 27, Issue 2 (2026)

Investigation of the Composition–Phase-Transformation Temperature Relationship in the NiTi-Based Alloys Using Machine Learning

GURBANOV N.A.$^{1}$, IMAMALIZADE Ch.A.$^{1}$, GARDASHOVA L.A.$^{1}$, YAŞAR M.M.$^{2}$, and GULIYEV A.H.$^{3}$

$^1$Azerbaijan State Oil and Industry University, 20 Azadlig Ave., Baku, Azerbaijan
$^2$Karabuk University, 413 Ave., 10, 78050 Karabuk, Turkey
$^3$Baku Engineering University, Khirdalan City, 120 Hasan Aliyev Ave., AZ0101 Baku, Azerbaijan

Received / final version: 28.02.2026 / 02.06.2026 Download PDF logo PDF

Abstract
Accurate prediction of phase-transformation temperatures is crucial for the design and optimisation of NiTi-based shape-memory alloys, as these temperatures determine their functional performance and operating ranges. However, the relationship between alloy composition and phase-transformation behaviour is quite complex and nonlinear, making reliable prediction difficult, using conventional modelling approaches. Therefore, in this study, machine-learning methods are applied to predict the austenite final transformation temperature based on alloy composition. The dataset consists of experimentally measured NiTi-based alloys characterised by elemental atomic percentages and corresponding transformation temperatures. Before modelling, data pre-processing and feature standardisation are performed to ensure reliable model training and evaluation. Various regression methods, including ridge regression, support vector regression, Gaussian process regression, and k-nearest neighbours regression model, are applied and systematically compared. The results reveal that nonlinear machine-learning methods outperform significantly linear regression in capturing complex compositional dependences governing transformation temperatures. Specifically, nonparametric and probabilistic models demonstrate superior ability in modelling nonlinear relationships and experimental variability. The findings confirm that machine learning provides an effective and reliable framework for predicting transformation temperatures based solely on compositional parameters. The developed approach offers a valuable tool for accelerating data-driven design and optimisation of advanced shape-memory alloys, while reducing experimental effort and development time.

Keywords: shape-memory alloys, phase transformation, prediction model, machine learning, Python.

DOI: https://doi.org/10.15407/ufm.27.02.***

Citation: N.A. Gurbanov, Ch.A. Imamalizade, L.A. Gardashova, M.M. Yaşar, and A.H. Guliyev, Investigation of the Composition–Phase-Transformation Temperature Relationship in the NiTi-Based Alloys Using Machine Learning, Progress in Physics of Metals, 27, No. 2: ***–*** (2026)

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