Elinvar property of cold-rolled NiTi alloy

When developing bone implants, Young’s modulus is one of the primary characteristics of the material that should be considered. This study focuses on regulating the modulus of Ti-50.8 at.% Ni alloy by varying the grain/subgrain size as well as the initial structure using subsequent aging at 430 °C for 10 h. After post-deformation annealing (PDA), the temperature dependence of Young’s modulus exhibits a pronounced V-shaped character with a minimum at the onset temperature of the forward martensitic transformation, Ms, regardless of the structure. The grain/subgrain size of B2-austenite strongly affects the modulus magnitude. This effect is ambiguous for a material with a grain size range of 0.13–3 µm and depends on the test temperature. The effect of aging on the modulus reduction depends on the initial structure; it is most pronounced in an alloy with a relatively coarse grain size of 9 µm and brings a decrease of 3.8 times at a temperature of 37 °C. Aging of the initially recrystallized Ni-rich NiTi alloy makes it possible to obtain a вone-like elastic modulus of E = 12–13 GPa at an operating temperature of 37 °C. An ultrafine-grained substructure exhibits the same Young’s modulus values in the low temperature range from −100 to 25 °C.

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“…The observed V-shaped elastic modulus curve after PDA (see Figures 4 and 5) as a function of the temperature is in good agreement [16,40,41].…”

Section: Discussionsupporting

confidence: 76%

On Structural Sensitivity of Young’s Modulus of Ni-Rich Ti-Ni Alloy

Рыклина

Murygin

Komarov

et al. 2023

Metals

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“…However, for some materials these properties can also be remarkably insensitive to temperature. A temperature-invariant elastic behavior of materials, referred to as elinvar effect, is, e.g., known for multicomponent Nibased alloys [17][18][19]. Recently, the elinvar effect has also been reported for elemental body-centered cubic (bcc) Ti [20].…”

Section: Introductionmentioning

confidence: 99%

Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials

Gubaev

Ikeda

Tasnádi

et al. 2021

Phys. Rev. Materials

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An active learning approach to train machine-learning interatomic potentials (moment tensor potentials) for multicomponent alloys to ab initio data is presented. Employing this approach, the disordered body-centered cubic (bcc) TiZrHfTa x system with varying Ta concentration is investigated via molecular dynamics simulations. Our results show a strong interplay between elastic properties and the structural ω phase stability, strongly affecting the mechanical properties. Based on these insights we systematically screen composition space for regimes where elastic constants show little or no temperature dependence (elinvar effect).

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