Microstructures and magnetostriction of two-phase Fe66-Pd30-Ni4 high-temperature ferromagnetic shape memory alloys

2011 ◽  
Vol 109 (7) ◽  
pp. 07A912 ◽  
Author(s):  
Yin-Chih Lin ◽  
Chien-Feng Lin ◽  
Jin-Bin Yang ◽  
Hwa-Teng Lee
Keyword(s):  
High Temperature ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Two Phase ◽  
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.

High temperature Ni 45 Co 5 Mn 25−x Fe x Ga 20 Cu 5 ferromagnetic shape memory alloys

2017 ◽  
Vol 134 ◽  
pp. 119-122 ◽  
Author(s):  
A. Pérez-Checa ◽  
J. Feuchtwanger ◽  
D. Musiienko ◽  
A. Sozinov ◽  
J.M. Barandiaran ◽  
...  
Keyword(s):  
High Temperature ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory

Combinatorial synthesis of Ni–Mn–Ga-(Fe,Co,Cu) high temperature ferromagnetic shape memory alloys thin films

2020 ◽  
Vol 178 ◽  
pp. 104-107 ◽  
Author(s):  
Vasileios Alexandrakis ◽  
José Manuel Barandiaran ◽  
Anabel Pérez-Checa ◽  
Patricia Lázpita ◽  
Peer Decker ◽  
...  
Keyword(s):  
Thin Films ◽  
High Temperature ◽  
Shape Memory Alloys ◽  
Shape Memory ◽  
Combinatorial Synthesis ◽  
Ferromagnetic Shape Memory Alloys ◽  
Ferromagnetic Shape Memory
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