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Non-Destructive Evaluation of the Magnetic Quality of Electrical Steel Sheets by Magnetic Barkhausen Noise (MBN) Analysis CanmetMATERIALS

SAE Technical Papers (1906-current) Available online

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Format:
Book
Conference/Event
Author/Creator:
He, Youliang, author.
Conference Name:
WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)
Language:
English
Physical Description:
1 online resource cm
Place of Publication:
Warrendale, PA SAE International 2022
Summary:
Soft magnetic lamination core is a major component of electric motors. The magnetic quality of the lamination has a significant effect on the energy efficiency of the motor. The magnetic properties of electrical steel sheets, which are important design parameters for the manufacturing of electric motors, are normally measured on cut steel strips by standard Epstein frame method, which is destructive and not suitable for the evaluation of magnetic anisotropy. In this paper, magnetic Barkhausen noise (MBN) analysis is used to evaluate the magnetic quality of electrical steel sheets. This method is featured by non-destructive, simple measurement, short measuring time, and offline/online measurements, et cetera In addition, it can be readily used to estimate the magnetic anisotropy in all directions of the electrical steel sheet. In this study, non-oriented electrical steel sheets at different stages of thermomechanical processing were evaluated by MBN analysis, and the textures of the materials were characterized by electron backscatter diffraction (EBSD). The angular MBNs measured in all directions of the sheet plane were compared to the magnetocrystalline anisotropy energy (MAE) in these directions (calculated from the measured texture). The correlation between the MBN and MAE was discussed with regard to the material's stress state, microstructure and crystallographic texture. The effect of the material processing history (e.g., different cold rolling schemes used) on the MBN and MAE is also discussed
Notes:
Vendor supplied data
Publisher Number:
2022-01-0235
Access Restriction:
Restricted for use by site license

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