Neural Network Model for Magnetization Characteristics of Ferromagnetic Materials

• Published in: IEEE access Vol. 9; pp. 71236 - 71243
• Main Authors: Tian, Mingxing, Li, Hongchen, Zhang, Huiying
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Biological neural networks; Dynamic Jiles–Atherton model; Ferromagnetic materials; frequency; Frequency measurement; Frequency variation; hysteresis loop; Hysteresis loops; Hysteresis models; Magnetic hysteresis; Magnetic properties; Magnetism; Magnetization; Metal sheets; Model accuracy; neural network; Neural networks; Saturation magnetization; Silicon steels
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3078554

The magnetic characteristics of silicon steel sheet 30Q120 under different AC frequencies were measured by an Epstein frame in order to analyze the effects of frequency variation on the hysteresis loop of ferromagnetic materials and compare the differences of such materials at different frequencies. First, the forecasting method of the magnetic properties of ferromagnetic materials under the influence of frequency using neural network was proposed based on the measured experimental data. Hysteresis loops at different frequencies were obtained. Then, the obtained results were compared with the measured results. Second, the dynamic Jiles-Atherton hysteresis model was established based on the Jiles-Atherton hysteresis theory, and hysteresis loops at different frequencies were obtained. The accuracies of the neural network model and Jiles-Atherton hysteresis model were verified by comparing the simulation results with the measured data. Upon comparing the dynamic Jiles-Atherton hysteresis and the neural network hysteresis models, results show that the latter has better accuracy. Furthermore, the correctness and effectiveness of the proposed method are verified.