Effect of Si doping on microstructure and martensite transformation in Ni-Mn-Sb ferromagnetic shape memory alloys

2018 ◽  
Vol 97 ◽  
pp. 1-7 ◽  
Author(s):  
Ruochen Zhang ◽  
Xuexi Zhang ◽  
Mingfang Qian ◽  
Jianfei Sun ◽  
Lin Geng
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Martensite Transformation ◽  
Ferromagnetic Shape Memory Alloys ◽  
Si Doping ◽  
Ferromagnetic Shape Memory

Martensite Transformation in Ni-Mn-Ga Ferromagnetic Shape-Memory Alloys

2007 ◽  
Vol 38 (4) ◽  
pp. 777-780 ◽  
Author(s):  
M. L. Richard ◽  
J. Feuchtwanger ◽  
S. M. Allen ◽  
R. C. O’handley ◽  
P. Lázpita ◽  
...  
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Martensite Transformation ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory

Magnetic properties of ferromagnetic shape memory alloys Ni _{2-x} Cu _{x} MnGa

2005 ◽  
Vol 21 (3-4) ◽  
pp. 151-157 ◽  
Author(s):  
Takeshi Kanomata ◽  
Takuji Nozawa ◽  
Daisuke Kikuchi ◽  
Hironori Nishihara ◽  
Keiichi Koyama ◽  
...  
Keyword(s):  
Magnetic Properties ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory

Effects of Annealing on Microstructure and Martensitic Transition of Ni-Mn-Co-In Ferromagnetic Shape Memory Alloys

2011 ◽  
Vol 674 ◽  
pp. 171-175
Author(s):  
Katarzyna Bałdys ◽  
Grzegorz Dercz ◽  
Łukasz Madej
Keyword(s):  
Electron Microscopy ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Martensitic Transition ◽  
Magnetic Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Initial State ◽  
X Ray ◽  
Ferromagnetic Shape Memory

The ferromagnetic shape memory alloys (FSMA) are relatively the brand new smart materials group. The most interesting issue connected with FSMA is magnetic shape memory, which gives a possibility to achieve relatively high strain (over 8%) caused by magnetic field. In this paper the effect of annealing on the microstructure and martensitic transition on Ni-Mn-Co-In ferromagnetic shape memory alloy has been studied. The alloy was prepared by melting of 99,98% pure Ni, 99,98% pure Mn, 99,98% pure Co, 99,99% pure In. The chemical composition, its homogeneity and the alloy microstructure were characterized using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The phase composition was also studied by X-ray analysis. The transformation course and characteristic temperatures were determined by the use of differential scanning calorimetry (DSC) and magnetic balance techniques. The results show that Tc of the annealed sample was found to decrease with increasing the annealing temperature. The Ms and Af increases with increasing annealing temperatures and showed best results in 1173K. The studied alloy exhibits a martensitic transformation from a L21 austenite to a martensite phase with a 7-layer (14M) and 5-layer (10M) modulated structure. The lattice constants of the L21 (a0) structure determined by TEM and X-ray analysis in this alloy were a0=0,4866. The TEM observation exhibit that the studied alloy in initial state has bigger accumulations of 10M and 14M structures as opposed from the annealed state.

Characterisation and modelling of vacancy dynamics in Ni–Mn–Ga ferromagnetic shape memory alloys

2015 ◽  
Vol 639 ◽  
pp. 180-186 ◽  
Author(s):  
D. Merida ◽  
J.A. García ◽  
V. Sánchez-Alarcos ◽  
J.I. Pérez-Landazábal ◽  
V. Recarte ◽  
...  
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory

Modeling Smart Materials Using Hysteretic Recurrent Neural Networks

2009 ◽  
Author(s):  
Arun Veeramani ◽  
John Crews ◽  
Gregory D. Buckner
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Real Time Control ◽  
Ferromagnetic Shape Memory Alloys ◽  
Two Phase ◽  
Time Control ◽  
Novel Approach ◽  
Three Phase ◽  
Ferromagnetic Shape Memory

This paper describes a novel approach to modeling hysteresis using a Hysteretic Recurrent Neural Network (HRNN). The HRNN utilizes weighted recurrent neurons, each composed of conjoined sigmoid activation functions to capture the directional dependencies typical of hysteretic smart materials (piezoelectrics, ferromagnetic, shape memory alloys, etc.) Network weights are included on the output layer to facilitate training and provide statistical model information such as phase fraction probabilities. This paper demonstrates HRNN-based modeling of two- and three-phase transformations in hysteretic materials (shape memory alloys) with experimental validation. A two-phase network is constructed to model the displacement characteristics of a shape memory alloy (SMA) wire under constant stress. To capture the more general thermo-mechanical behavior of SMAs, a three-phase HRNN model (which accounts for detwinned Martensite, twinned Martensite, and Austensite phases) is developed and experimentally validated. The HRNN modeling approach described in this paper readily lends itself to other hysteretic materials and may be used for developing real-time control algorithms.

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