Eddy current testing is widely used for the automatic detection of defects in conductive materials. However, this method is strongly affected by probe scanning conditions and requires signal analysis to be carried out by experienced inspectors. In this study, back-propagation neural networks were used to predict the depth and length of unknown slits by analyzing eddy current signals in the presence of noise caused by probe lift-off and tilting. The constructed neural networks were shown to predict the depth and length of defects with relative errors of 4.6% and 6.2%, respectively.

中文翻译：

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反向传播神经网络在涡流测试中表征缺陷的应用

涡流测试广泛用于自动检测导电材料中的缺陷。但是，这种方法受探针扫描条件的强烈影响，并且需要有经验的检查员进行信号分析。在这项研究中，使用反向传播神经网络通过分析由探头抬起和倾斜引起的噪声中的涡流信号来预测未知缝的深度和长度。构造的神经网络可以预测缺陷的深度和长度，相对误差分别为4.6％和6.2％。