Predictions of electromotive force of magnetic shape memory alloy (MSMA) using constitutive model and generalized regression neural network

Abstract

Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals, can exhibit the shape memory effect due to an applied magnetic field at room temperature. Under a variable magnetic field and a constant bias stress loading, MSMAs have been used for actuation applications. Under variable stress and a constant bias field, MSMAs can be used in power harvesting or sensing devices, e.g., in structural health monitoring applications. This behavior is primarily a result of the approximately tetragonal unit cell whose magnetic easy axis is approximately aligned with the short axis of the unit cell within the Ni-Mn-Ga single crystals. Under an applied field, the magnetic easy axis tends to align with the external field. Similarly, under an applied compressive force, the short side of the unit cell tends to align with the direction of the force. This work introduced a new feature to the existing macroscale magneto-mechanical model for Ni-Mn-Ga single crystal. This model includes the fact that the magnetic easy axis in the two variants is not exactly perpendicular as observed by D’silva et al. [1]. This offset helps explain some of the power harvesting capabilities of MSMAs. Model predictions are compared to experimental data collected on a Ni-Mn-Ga single crystal. The experiments include both stress-controlled loading with constant bias magnetic field load (which mimics power harvesting or sensing) and field controlled loading with constant bias compressive stress (which mimics actuation). Each type of test was performed at several different load levels and the applied field was measured without the MSMA specimen present so that demagnetization does not affect the experimentally measured field as suggested by Eberle et al. [2]. Results show decent agreement between model predictions and experimental data. Although the model predicts experimental results decently, it does not capture all the features of the experimental data. In order to capture all the experimental features, finally, a generalized regression neural network (GRNN) was trained using the experimental data (stress, strain, magnetic field, & emf) so that it can make a reasonably better prediction.

Designing and modeling of a new MSMA vibration energy transducer

“…While Safari et al [11] proposed a mathematically based model rooted in thermodynamics, it falls short in complying with all thermodynamic laws. To achieve more precise predictions of system output, a machine learning-based modeling approach was introduced [12][13][14]. Confronting the challenges posed by the nonlinear relationship between inputs and outputs, machine learning techniques demonstrate efficacy in predictive modeling.…”

Mathematical modelling and experimental study of a novel planar micro-positioning stage using shape memory alloy actuators

“…In another example, Souza et al [25] developed a threedimensional phenomenological model explaining the mechanical response of polycrystalline solids experiencing stressinduced phase transformation. Based on these models, numerous studies have been conducted to further investigate the behaviour of SMAs and simulate their responses under various mechanical and thermomechanical loads [26][27][28][29][30][31]. Among the various constitutive models available, the Souza model stands out for its simplicity and effectiveness, making it a popular choice for numerous research endeavours including this study.…”