电工钢
涡流
磁场
人工神经网络
波形
领域(数学)
磁滞
磁滞
物理
算法
计算机科学
材料科学
电气工程
电压
凝聚态物理
人工智能
磁化
数学
工程类
复合材料
量子力学
纯数学
作者
Zhiwei He,Jung-Seop Kim,Chang-Seop Koh
标识
DOI:10.1109/tmag.2022.3182157
摘要
This article investigates the behavior of anomalous magnetic field in electrical steel sheet (ESS). For this, anomalous magnetic field is extracted from measured magnetic field by excluding static hysteresis and eddy current fields. To overcome the shortcomings of the Bertotti’s and artificial neural network (ANN) models for anomalous magnetic field and loss, an improved model is proposed by introducing a new parameter, time shifting, to modify the phase of $B$ -waveform. Through comparisons of Bertotti’s model, ANN model, and experimental measurements over non-oriented, highly grain-oriented, and domain-refined highly grain-oriented ESSs, the proposed model is validated.
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