Mr. Jason Dikes Profile

Jason Dikes

at Northern Arizona Univ

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Proceedings Article | 3 April 2015 Paper

An investigation of the material and model parameters for a constitutive model for MSMAs

Jason Dikes, Heidi Feigenbaum, Constantin Ciocanel

Proceedings Volume 9435, 943539 (2015) https://doi.org/10.1117/12.2084359

KEYWORDS: Calibration, Magnetism, Anisotropy, Manufacturing, Data modeling, Shape memory alloys, Smart materials, Systems modeling, Thermodynamics, Magnetic sensors

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A two dimensional constitutive model capable of predicting the magneto-mechanical response of a magnetic shape memory alloy (MSMA) has been developed and calibrated using a zero field-variable stress test¹. This calibration approach is easy to perform and facilitates a faster evaluation of the three calibration constants required by the model (vs. five calibration constants required by previous models^2,3). The calibration constants generated with this approach facilitate good model predictions of constant field-variable stress tests, for a wide range of loading conditions¹. However, the same calibration constants yield less accurate model predictions for constant stress-variable field tests. Deployment of a separate calibration method for this type of loading, using a varying field-zero stress calibration test, also didn’t lead to improved model predictions of this loading case. As a result, a sensitivity analysis was performed on most model and material parameters to identify which of them may influence model predictions the most, in both types of loading conditions. The sensitivity analysis revealed that changing most of these parameters did not improve model predictions for all loading types. Only the anisotropy coefficient was found to improve significantly field controlled model predictions and slightly worsen model predictions for stress controlled cases. This suggests that either the value of the anisotropy coefficient (which is provided by the manufacturer) is not accurate, or that the model is missing features associated with the magnetic energy of the material.

Proceedings Article | 20 March 2014 Paper

Numerical predictions versus experimental findings on the power-harvesting output of a NiMnGa alloy

Isaac Nelson, Jason Dikes, Heidi Feigenbaum, Constantin Ciocanel

Proceedings Volume 9058, 905815 (2014) https://doi.org/10.1117/12.2046337

KEYWORDS: Magnetism, Data modeling, Manufacturing, Motion models, Shape memory alloys, Calibration, Actinium, Thermodynamics, Data acquisition, Temperature metrology

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Magnetic shape memory alloys (MSMAs) can display up to 10% recoverable strain in response to the application of a magnetic field or compressive mechanical stress. The amount of recoverable strain depends on the amount and direction of the applied stress and magnetic field as well as manufacturing, chemical composition, and training of the material. Due to their relatively large strains and fast response, MSMAs are suitable for a wide range of applications, including power harvesting, sensing, and actuation. The response of MSMAs is primarily driven by the reorientation of martensite variants. Power harvesting is possible due to this reorientation process and the accompanying change in material’s magnetization, which can be changed into an electric potential/voltage using a pick-up coil placed around (or on the side of) the specimen. The magnitude of the output voltage depends on the number of turns of the pick-up coil, the amplitude of the reorientation strain, the magnitude and direction of the biased magnetic field, and the frequency at which the reorientation occurs. This paper focuses on the ability of a two dimensional constitutive model, developed by the group to capture the magnetomechanical response of MSMAs under general two dimensional loading conditions, to predict the power harvesting output of a Ni2MnGa specimen. Comparison between model predictions of voltage output and experimental measurements of the same indicate that, while the model is able to replicate the stress-strain response of the material during power harvesting, it is unable to accurately predict the magnitude of the experimentally measured voltage output. This indicates that additional features still need to be included in the model to better capture the change in magnetization that occurs during variant reorientation.

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