Magnetic shape memory (MSM) alloys are a class of actuator materials which produce strain via the magnetic field induced reorientation of twin variants in response to an applied magnetic field. MSM effect depends on the success of parameters like mobility of twin boundaries, magnetic properties of the alloy, proper specimen orientation and specimen shape.

Solidification behavior of these alloys has a strong effect on their microstructure, and hence on their mechanical and MSM properties. In order to produce single crystals showing a distinct martensite transformation and a strong MSM effect, it is necessary to obtain a uniform martensite in the entire sample. The martensite transformation temperature is very sensitive to the alloy composition. A single phase having a homogeneous composition is thus desirable in order to avoid having different components of the microstructure undergoing the martensite transformation at different temperatures, or not undergoing the transformation at all in the case of the eutectic structures and Mn-rich particles.

In this study, the microstructure, segregation tendency of the constituent elements and the effect of the composition gradients of NiMnGa alloys were investigated. For the current growth conditions, coarse cellular structures have been obtained which show significant solute segregation. Mn was found to segregate to the cell boundary, whereas Ga tended to segregate to the cell core. The results showed that as solidified, off-stoichiometric, alloys had three distinct microstructural features—a Heusler phase, an Mn Rich phase and a eutectic or eutectoid region. The latter could be removed by prolonged annealing at elevated temperatures, but that the coarse Mn-rich particles are much more difficult to remove. This phase is likely harmful to the mechanical properties of the alloy and should be eliminated in the future.

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1. Determination of the compositions of NiMnGa magnetic shape memory alloys using hybrid evolutionary algorithms
2. Prediction of the Martensite Transformation Temperatures of NiMnGa Magnetic Shape Memory Alloys Using Artificial Neural Networks