Concentration, temperature and deformation dependences of martensite lattice parameters in binary Ti-Ni shape memory alloys

2003 ◽  
Vol 112 ◽  
pp. 651-654 ◽  
Author(s):  
S. D. Prokoshkin ◽  
V. Brailovski ◽  
S. Turenne ◽  
I. Yu Khmelevskaya ◽  
A. V. Korotitskiy ◽  
...  
2006 ◽  
Vol 426 (1-2) ◽  
pp. 144-147 ◽  
Author(s):  
Changwei Gong ◽  
Fenfang Guo ◽  
Dazhi Yang

Author(s):  
Lorenzo La Rosa ◽  
Francesco Maresca

Abstract Ni-Ti is a key shape memory alloy (SMA) system for applications, being cheap and having good mechanical properties. Recently, atomistic simulations of Ni-Ti SMAs have been used with the purpose of revealing the nano-scale mechanisms that control superelasticity and the shape memory effect, which is crucial to guide alloying or processing strategies to improve materials performance. These atomistic simulations are based on molecular dynamics modelling that relies on (empirical) interatomic potentials. These simulations must reproduce accurately the mechanism of martensitic transformation and the microstructure that it originates, since this controls both superelasticity and the shape memory effect. As demonstrated by the energy minimization theory of martensitic transformations [Ball, James (1987) Archive for Rational Mechanics and Analysis, 100:13], the microstructure of martensite depends on the lattice parameters of the austenite and the martensite phases. Here, we compute the bounds of possible microstructural variations based on the experimental variations/uncertainties in the lattice parameter measurements. We show that both density functional theory and molecular dynamics lattice parameters are typically outside the experimental range, and that seemingly small deviations from this range induce large deviations from the experimental bounds of the microstructural predictions, with notable cases where unphysical microstructures are predicted to form. Therefore, our work points to a strategy for benchmarking and selecting interatomic potentials for atomistic modelling of shape memory alloys, which is crucial to modelling the development of martensitic microstructures and their impact on the shape memory effect.


2015 ◽  
Vol 25 (2) ◽  
pp. 025001 ◽  
Author(s):  
L Straka ◽  
J Drahokoupil ◽  
O Pacherová ◽  
K Fabiánová ◽  
V Kopecký ◽  
...  

2004 ◽  
Vol 52 (15) ◽  
pp. 4479-4492 ◽  
Author(s):  
S.D. Prokoshkin ◽  
A.V. Korotitskiy ◽  
V. Brailovski ◽  
S. Turenne ◽  
I.Yu. Khmelevskaya ◽  
...  

1994 ◽  
Vol 6 (24) ◽  
pp. 4601-4614 ◽  
Author(s):  
Jianian Gui ◽  
Yanling Cui ◽  
Shengqiu Xu ◽  
Qinglin Wang ◽  
Yiying Ye ◽  
...  

2011 ◽  
Vol 172-174 ◽  
pp. 43-48 ◽  
Author(s):  
Anna Manzoni ◽  
Karine Chastaing ◽  
Anne Denquin ◽  
Philippe Vermaut ◽  
Richard Portier

Among the different systems for high temperature shape memory alloys (SMA’s), equiatomic RuNb and RuTa alloys demonstrate both shape memory effect (SME) and MT temperatures above 800°C. For both systems, it is interesting to find a way to control the transformation temperatures while keeping the shape memory effect. One way to change the transformation temperatures is to change the composition in the binary alloys; another is to add a ternary element like Fe. The eight investigated alloys show two different space groups at room temperature. The monoclinic alloys undergo two successive displacive transformations on cooling, starting from the high temperature β phase field: β (B2) à β’ (tetragonal) à β’’ (monoclinic). The tetragonal alloys exhibit a single transition from cubic to tetragonal. A multiple twinned microstructure can be found in all alloys. Transformation temperatures decrease with lower Ru content and with the addition of Fe. The β’ à β transformation seems to be the main responsible for the SME. Compression tests performed in the martensitic phase give a quantitative result of the shape memory effect. In the binary alloys, the SME decreases with decreasing Ru content, which is in accordance with the evolution of the lattice parameters of martensites. A lower SME in the ternary alloys can also be linked to the lattice parameters and seems to be quite reliable to predict the evolution of the shape memory effect.


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