Shape memory characteristics of highly porous Ti-rich TiNi alloys

Kim, Yeon‐wook; Do, D.

doi:10.1016/j.matlet.2015.09.101

Cited by 13 publications

(1 citation statement)

References 8 publications

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“…Shape memory alloys (SMAs) are commonly considered to be smart materials because they exhibit shape memory effects and hyper-elastic properties that are not observed in typical metals [ 1 , 2 , 3 , 4 , 5 , 6 ]. These extraordinary properties are due to a temperature-dependent martensitic phase transformation from a low (martensite) to high (austenite) symmetric crystallographic structure upon cooling, and a reverse martensitic transformation in the opposite direction upon heating.…”

Section: Introductionmentioning

confidence: 99%

Grain Size and Phase Transformation Behavior of TiNi Shape-Memory-Alloy Thin Film under Different Deposition Conditions

et al. 2020

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TiNi shape-memory-alloy thin films can be used as small high-speed actuators or sensors because they exhibit a rapid response rate. In recent years, the transformation temperature of these films, manufactured via a magnetron sputtering method, was found to be lower than that of the bulk alloys owing to the small size of the grain. In this study, deposition conditions (growth rate, film thickness, and substrate temperature) affecting the grain size of thin films were investigated. The grain size of the thin film alloys was found to be most responsive to the substrate temperature.

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Section: Introductionmentioning

confidence: 99%

Grain Size and Phase Transformation Behavior of TiNi Shape-Memory-Alloy Thin Film under Different Deposition Conditions

et al. 2020

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A knowledge‐based materials descriptor for compositional dependence of phase transformation in NiTi shape memory alloys

Li,

Liang,

Zhou

et al. 2024

Materials Genome Engineering Advances

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This study presents ∆τ, a novel descriptor that captures the compositional dependence of phase transformation temperature (Ap) in NiTi‐based shape memory alloys (SMAs). Designed to address the complexity of multicomponent SMAs, ∆τ was integrated into symbolic regression (SR) and kernel ridge regression (KRR) models, yielding substantial improvements in predicting key functional properties: transformation temperature, enthalpy, and thermal hysteresis. Using the KRR model with ∆τ, we explored the NiTiHfZrCu compositional space, identifying six promising alloys with high Ap (>250°C), large enthalpy (>27 J/g), and low thermal hysteresis. Experimental validation confirmed the model's accuracy with the alloys showing high‐temperature transformation behavior and low hysteresis, suitable for high‐performance applications in aerospace and nuclear industries. These findings underscore the power of domain‐informed descriptors like ∆τ in enhancing machine learning‐driven materials design.

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