Key points are not available for this paper at this time.
The martensitic transformation in NiTi-based Shape Memory Alloys (SMAs) provides a basis for shape memory effect and superelasticity, thereby enabling applications requiring solid-state actuation and large recoverable shape changes upon mechanical load cycling. In order to tailor the transformation to a particular application, the compositional dependence of properties in NiTi-based SMAs, such as martensitic transformation temperatures and hysteresis, has been exploited. However, the compositional design space is large and complex, and experimental studies are expensive. In this work, we develop an interpretable piecewise linear regression model that predicts the ₂ parameter, a measure of compatibility between austenite and martensite phases, and an (indirect) factor that is well-correlated with martensitic transformation hysteresis, based on the chemical features derived from the alloy composition. The model is capable of predicting, for the first time, the type of martensitic transformation for a given alloy chemistry. The proposed model is validated by experimental data from the literature as well as in-house measurements. The results show that the model can effectively distinguish between B19 and B19^ regions for any given composition in NiTi-based SMAs and accurately estimate the ₂ parameter. Our analysis also reveals that the weighted average of the quotient of the first ionization energy and the Voronoi coordination number is a key compositional characteristic that correlates with the ₂ parameter and thermodynamic responses, including the transformation hysteresis, martensite start temperature, and critical temperature. The work herein demonstrates the potential of data-driven methodologies for understanding and designing NiTi-based SMAs with desired transformation characteristics.
Zadeh et al. (Mon,) studied this question.