TEM Study of Ni-Mn-Co-In Ferromagnetic Shape Memory Alloys

2012 ◽  
Vol 186 ◽  
pp. 271-274
Author(s):  
Krystian Prusik ◽  
Katarzyna Bałdys ◽  
Danuta Stróż
Keyword(s):  
Martensitic Transformation ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Smart Materials ◽  
Parent Phase ◽  
Cobalt Content ◽  
Indium Content ◽  
Ferromagnetic Shape Memory Alloys ◽  
Transformation Temperatures ◽  
Ferromagnetic Shape Memory

Ferromagnetic shape memory alloys (FSMA) are relatively new smart materials group. Recently, new FSMA from NiMnX (X=Sb, Sn, In, Co+In) systems are considered as alternative to the well known NiMnGa alloys. Four alloys of the following compositions: Ni43Mn35Co4In18, Ni41Mn35Co4In20, Ni42Mn35Co5In18, Ni40Mn35Co5In20 were studied in order to determine microstructure, phase composition and martensitic transformation temperatures versus their chemical composition. Structure of the alloys was studied by optical and transmission electron microscopy (TEM). All of the studied alloys showed macrostructure consisting of radially oriented columnar grains in the direction perpendicular to the casting axis. The structure of the phases occurred in the studied alloys depended on the cobalt and indium content. For the alloys containing 20 at. % of In at room temperature only L21 parent phase was observed whereas for those containing 18 at. % of In either single phase 14M modulated martensite or mixture of 14M martensite and L21 parent phase were seen. DSC measurements showed in studied alloys single-state martensitic transformation. Decrease In content of 2 at.% caused about 80°C fall of martensitic transformation temperatures. Curie temperature Tc increases of 20°C with 1 at% rise of the cobalt content.

TEM Studies on the Microstructure in Ferromagnetic Shape Memory Alloys

2008 ◽  
Vol 47-50 ◽  
pp. 515-518
Author(s):  
Y. Murakami ◽  
T. Yano ◽  
Daisuke Shindo
Keyword(s):  
Martensitic Transformation ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Parent Phase ◽  
Magnetic Domain ◽  
High Temperature Phase ◽  
Phase Changes ◽  
Temperature Phase ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory

The magnetic domain structures of the cubic parent phase (high-temperature phase) in ferromagnetic shape memory alloys (SMAs) have been studied by electron holography. In a Ni51Fe22Ga17 alloy, the magnetic flux distribution in the parent phase changes dramatically before the onset of martensitic transformation. In contrast, a Ni45Co5Mn36.7In13.3 alloy—a recently developed ferromagnetic SMA—does not show appreciable changes in the magnetic domain structure upon cooling. The anomaly observed in the Ni51Fe22Ga17 alloy appears to be due to lattice distortions, which become more pronounced as the temperature approaches the martensitic transformation start temperature, Ms.

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.

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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