The exploitation of nondestructive eddy current testing (NDT-EC) has become a capital necessity. Therefore, the development of a fast tool for the eddy-current signal inversion is necessary. This paper proposes an inversion of signals coming from the NDT-EC sensor response using machine learning methods, to reconstruct the length and depth of the defect and obtain its geometric characterization by solving the inverse problem. In this context, a database comprising the impedance of the sensor-cracked part system (constituting the crack signature) was constructed from a 3D finite element simulation and validated by an experimental companion. The machine learning algorithms were trained using this database. The results show that the defect can be quantified using these developed approaches. The numerical approach can replace the expensive experimental investigation or the optimization algorithm that has a prohibitive computing time. The results show that the approaches developed estimated the desired parameters of the crack with good precision. These different machine learning methods for solving the inverse problem were implemented using the MATLAB software.

中文翻译：

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使用机器学习方法表征涡流测试中的地下裂纹

无损涡流检测（NDT-EC）的开发已成为资本的必需品。因此，有必要开发一种快速的涡流信号反演工具。本文提出使用机器学习方法对来自 NDT-EC 传感器响应的信号进行反演，以重建缺陷的长度和深度，并通过解决反演问题获得其几何特征。在这种情况下，由 3D 有限元模拟构建了一个包含传感器破裂部分系统的阻抗（构成裂纹特征）的数据库，并由实验伙伴进行验证。机器学习算法是使用这个数据库训练的。结果表明，可以使用这些开发的方法来量化缺陷。数值方法可以替代昂贵的实验研究或计算时间过长的优化算法。结果表明，所开发的方法以良好的精度估计了所需的裂纹参数。这些用于解决逆问题的不同机器学习方法是使用 MATLAB 软件实现的。