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Reconstructions of Inner and Outer Defects in Ferromagnetic Materials from Experimental Remanent Magnetic Measurements by using Neural Networks

机译:利用神经网络从实验剩磁测量重建铁磁材料的内部和外部缺陷

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摘要

The present paper presents the application of remanent field analysis for detecting defects in ferromagnetic materials. The remanent signal from outer and inner defects in magnetized ferromagnetic samples is computed using a FEM-BEM nonlinear code. Also, an experiment is set-up to measure the remanent field around defects. The parameterized defect shape is reconstructed using Neural Networks. Numerical results for the inversion are presented.
机译:本文介绍了剩磁分析在检测铁磁材料中的缺陷中的应用。使用FEM-BEM非线性代码计算磁化铁磁样品中外部和内部缺陷的剩余信号。此外,还建立了一个实验来测量缺陷周围的剩余磁场。使用神经网络重建参数化的缺陷形状。给出了反演的数值结果。

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