Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks

Simone Quondam Antonio, Francesco Riganti Fulginei, Gabriele Maria Lozito, Antonio Faba, Alessandro Salvini, Vincenzo Bonaiuto and Fausto Sargeni
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Simone Quondam Antonio: Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy
Francesco Riganti Fulginei: Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome, Italy
Gabriele Maria Lozito: Department of Information Engineering, University of Florence, Via di S. Marta 3, 50139 Florence, Italy
Antonio Faba: Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia, Italy
Alessandro Salvini: Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome, Italy
Vincenzo Bonaiuto: Department of Industrial Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy
Fausto Sargeni: Department of Electronic Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy

Mathematics, 2022, vol. 10, issue 13, 1-14

Abstract: A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the energy versus the amplitude of the magnetic induction) of soft ferromagnetic materials at different operating frequencies is proposed herein. Firstly, an innovative Fe-Si magnetic alloy, grade 35H270, is experimentally characterized via an Epstein frame in a wide range of frequencies, from 1 Hz up to 600 Hz. Parts of the dynamic hysteresis loops obtained through the experiments are involved in the training of a feedforward neural network, while the remaining ones are considered to validate the model. The training procedure is accurately designed to, firstly, identify the optimum network architecture (i.e., the number of hidden layers and the number of neurons per layer), and then, to effectively train the network. The model turns out to be capable of reproducing the magnetization processes and predicting the dynamic energy losses of the examined material in the whole range of inductions and frequencies considered. In addition, its computational and memory efficiency make the model a useful tool in the design stage of electrical machines and magnetic components.

Keywords: dynamic hysteresis; magnetic alloys; energy losses; neural network modeling; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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