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| Prediction of Magnetic Domains Magnetization Process and Feature Extraction of Electrical Steel Sheet Based on Deep Learning |
| Wu Xin, Zhang Yanli, Wang Zhen, Li Mengxing, Jiang Wei |
| Ministry of Education Key Laboratory of Special Motors and High Voltage Electrical Apparatus Shenyang University of Technology Shenyang 110870 China |
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Abstract The development of high-efficiency electrical products is inseparable from the accurate characterization of magnetic properties of high-quality electrical steel with low loss. The extraction of magnetic domains structure describing the dynamic magnetization characteristic in an electrical steel is a bridge to realize the simulation from mesoscopic magnetization mechanism to macroscopic characteristics. Current research on the magnetic properties of electrical steel tends to focus on the characterization of macroscopic magnetic properties, while the magnetic properties of materials often depend on the internal domains structure of materials, and the study of reflecting the observed dynamic processes of magnetic domains into the model of macroscopic magnetic properties is still in its initial stage. In order to further investigate the mesoscopic hysteresis characteristics model, a magneto-optical Kerr microscope was used to observe and study the dynamic evolution process of magnetic domains structure in a grain-oriented electrical steel sheet under an external magnetic field.
Firstly, the image information of dynamic evolution of magnetic domains structure in an electrical steel was obtained under the external magnetic field changing from saturation to zero to reverse saturation, and the process of magnetic domains reorganization, annihilation and nucleation was analyzed. Secondly, in order to obtain image information of magnetic domains enough to characterize the magnetization properties of electrical steel sheet, two kinds of neural networks based on deep learning theory such as ConvLSTM (convolutional long-short term memory) and ConvGRU (convolutional gate recurrent unit), were compared and studied on the prediction effects of dynamic magnetic domains evolution images. Finally, based on the experimentally observed and model-predicted magnetic domains images of electrical steel, a method was proposed to characterize the dynamic magnetization process and magnetic domains state of the sample by extracting the area of the magnetic domains as the characteristic parameter.
The observation of magneto-optical Kerr microscopy shows that the evolution of internal magnetic domains of electrical steel tends to be more complicated as the angle of deviation from the easy magnetization axis increases. In the process of applied magnetic field deviating from the easy magnetization axis of the sample from 0° to 60°, the magnetic field strength required to saturate the sample increases with the increasing angle of deviation, which shows a significant anisotropy of magnetization. The saturation magnetic field required to magnetize the transverse direction is smaller than that along the 60°, and the analysis of internal grains of electrical steel shows that it is related to the internal grains distribution and the rolling surface of the electrical steel. The results of two neural network models for predicting the domains structure of electrical steel show that the performance of two models is not quite different, and both neural networks can predict the basic orientation of electrical steel domains with the same number of training times, but the ConvLSTM has better prediction results. During the magnetization process of electrical steel sheets, the internal magnetic domains are reorganized, annihilated and nucleated, and the change of domains area is observed in the observation results. Therefore, the magnetization process of the sample was characterized by extracting domains area, and the results show that the proposed characterization method can effectively characterize the magnetization degree and the corresponding domains state of the sample.
The characterization method using the domains area as the feature quantities helps to build a bridge between the domains observation and the hysteresis model study, which lays a foundation for the further realization of the study of the macroscopic hysteresis model based on the mechanism of magnetic domains magnetization.
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Received: 21 December 2021
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2023, Vol. 38 
