Modelling of Magnetization Processes of 3D-Printed Fe-Si Components by Means of an Artificial Neural Network Implemented in a Finite Element Scheme

Presently, iron-silicon (Fe-Si) alloys are considered the optimal materials for the fabrication of magnetic cores for electric motors. Additive manufacturing (AM) facilitates the fabrication of Fe-Si alloys with elevated silicon concentrations, highly advantageous to limit the electric conductivity and maximize the magnetic permeability. Given the novelty of the approach, there is a paucity of research on hysteresis modeling and simulations of components fabricated by AM. In this paper, the focus is on a Fe-Si 3.7% wt Si fabricated by AM. The hysteresis has been modeled by means of an artificial neural network (ANN) trained on the quasi-static (1Hz) hysteresis loops measured using the Volt-Amperometric (VA) experimental setup on the bulk material, a full section toroid. The trained ANN is subsequently implemented in a finite element method (FEM) software to simulate the hysteresis on a material sample with air gaps and at higher frequencies never seen in the training phase. This work demonstrates, for the first time, the robust predictive capability of an ANN-FEM framework. A key contribution is the validation of the model under purely predictive conditions, using a geometry and frequency range entirely unseen during training. The simulated results have been compared with measurements and with results obtained with the classical Jiles-Atherton (JA) model. The correlation between the ANN results and the experimental data is substantial, consistent with the JA results, and in certain instances, superior.