Datasets of narrow thermal hysteresis behaviour Ti-Ni-based HT-SMAs and the predicted accumulated local effects

Abstract

This article refers to data derived from a research article entitled “Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning” [1]. It is based on the knowledge that alloying Ti-Ni-based shape memory alloys (SMAs) with additional ternary or multicomponent elements can alter the SMAs’ characteristic transformation temperatures, including the thermal hystereses. Two datasets are reported. The first and primary dataset documents experimental Ti-Ni-based shape memory alloys’ high-transformation temperature characteristics reported in the literature. The second auxiliary dataset presented in this article was obtained following the explainable prediction of the narrow high-temperature thermal hysteresis behaviour in Ti-Ni-based high-transformation temperature SMAs (HT-SMAs). The second dataset is intended to generalise and summarise the ML prediction and visualisation of the thermal hysteresis behaviour as also observed experimentally in multiple reports elsewhere.The datasets are provided as supplementary files and the second dataset is also visualised as an intuitive marginal effects plot. We believe that these data will find applications in advancing experimental and theoretical HT-SMA research.

Keywords

• High-temperature shape memory alloys
• HT-SMA
• Ti-Ni-based HT-SMA
• Narrow thermal hysteresis
• Explainable machine learning
• Accumulated local effect