Abstract
A thorough investigation of the 2-D hysteresis processes under arbitrary excitations was carried out for a specimen of innovative Fe-Si magnetic powder material. The vector experimental measurements were first performed via a single disk tester (SDT) apparatus under a controlled magnetic induction field, taking into account circular, elliptic, and scalar processes. The experimental data relative to the circular loops were utilized to identify a vector model of hysteresis based on feedforward neural networks (NNs), having as an input the magnetic induction vector B and as an output the magnetic field vector H. Then the model was validated by the simulation of the other experimental hysteresis processes. The comparison between calculated and measured loops evidenced the capability of the model in both the reconstruction of the magnetic field trajectory and the prediction of the power loss under various excitation waveforms. Finally, the computational efficiency of the model makes it suitable for future application in finite element analysis (FEA).
Highlights
The material examined in this work is an innovative and performing Fe-Si magnetic powder alloy with distributed air gaps provided by Chang Sung Corporation® for scientific purposes
The second test case investigated dealt with the analysis of vector hysteresis processesThe obtained with a distorted magnetic induction, which was characterized by a fifthsecond test case investigated dealt with the analysis of vector hysteresis processes order harmonic to the fundamental tone
We presented a thorough vector experimental characterization for an innovative soft ferromagnetic material based on Fe-Si magnetic powder and distributed air gaps, as well as a dedicated technique to reproduce measured magnetization processes via a neural network approach
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A thorough experimental analysis was performed by means of a single disk tester (SDT) apparatus with the scope to show the material behavior under various types of vector excitations that may occur in practical operating conditions For this reason, the measurements were performed under both circular and elliptic magnetic induction trajectories and with either sinusoidal or non-sinusoidal waveforms. In addition to the experimental verification, a computationally efficient vector hysteresis model based on feedforward neural networks (NNs) was utilized to reproduce the behavior of the test sample and to predict power losses. The application of a neural system consisting of an assembly of feedforward networks was proposed in [26] to reproduce vector magnetization patterns for Fe-Si laminated steels, but the comparison with the experimental data only covered the rotational loops under circular magnetic induction with sinusoidal waveforms.
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