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Artificial Neural Network Applied to Eddy Current Nondestructive Quantitative Testing

机译：人工神经网络在涡流无损定量检测中的应用

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摘要

Artificial neural network (ANN) is applied to eddy current nondestructive testing (ECNT) for detecting defects. After establishing the correlation between the amplitude or phase with the defect size, phase value was found to correlate better with the depth of defect. Wavelet edge detecting method can be used finding free edges. The results were used as inputs to ANN for analysis.

机译：人工神经网络（ANN）被应用于涡流无损检测（ECNT）以检测缺陷。在建立振幅或相位与缺陷尺寸之间的相关性之后，发现相位值与缺陷深度之间的相关性更好。小波边缘检测方法可用于寻找自由边缘。结果用作ANN的输入以进行分析。

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来源
《Applied electromagnetics(III)》|2001年|p.321-324|共4页
会议地点 Hangzhou(CN);Hangzhou(CN)
作者
Sun Xiaoyun; Liu Donghui; Wang Shuhong; Sun Huiqin; Sheng Jianni;
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作者单位

Hebei Science Technology University, Shijiazhuang, CHINA;

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原文格式 PDF
正文语种 eng
中图分类物理学;
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3. Nondestructive evaluation of loose assemblies using multi-frequency eddy currents and artificial neural networks [J] . Eric Vourch, Pierre-Yves Joubert, Guillaume Le Gac, Measurement Science & Technology . 2013,第12期

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4. Artificial Neural Network Applied to Eddy Current Nondestructive Quantitative Testing [C] . Sun Xiaoyun, Liu Donghui, Wang Shuhong, Asian symposium on applied electromagnetics . 2001

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Artificial Neural Network Applied to Eddy Current Nondestructive Quantitative Testing

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