Abstract
In the actual production environment, the eddy current imaging inspection of titanium plate defects is prone to scan shift, scale distortion, and noise interference in varying degrees, which leads to the defect false detection and even missed inspection. In view of this problem, a novel image recognition and classification method based on convolutional neural network (CNN) for eddy current detection of titanium plate defects is proposed. By constructing a variety of experimental conditions and collecting defect signals, the characteristics of eddy current testing (ECT) signals for titanium plate defects are analyzed, and then the convolution structure and learning parameters are set. The structural characteristics of local connectivity and shared weights of CNN have better feature learning and characterization capabilities for titanium plate defect images under scan shift, scale distortion, and strong noise interference. The results prove that, compared with other deep learning and classical machine learning methods, the CNN has a higher recognition and classification accuracy for the defect eddy current image of the titanium plate in the complex detection environment.
Highlights
Titanium and titanium alloy materials have been widely used in aerospace, deep-sea exploration, and petrochemical and high-end equipment manufacturing because of their advantages of low density, high strength, and good corrosion resistance [1, 2]
E original image of the real part of the eddy current detection of the defect of the titanium plate is shown in Figure 9, and the defect corresponds to the schematic diagram of the defect shown in Figure 6. e scanning direction is perpendicular to the edge of the defect, and the defect position is at the center of the scanning area
From the analysis of the actual imaging results, the length and width of the defect are reflected in the contour of the scanning image, and the amplitude of the real part is related to the depth of the defect, that is, the deeper the defect is, the smaller the amplitude of the real part is
Summary
Titanium and titanium alloy materials have been widely used in aerospace, deep-sea exploration, and petrochemical and high-end equipment manufacturing because of their advantages of low density, high strength, and good corrosion resistance [1, 2]. Eddy current testing (ECT) has been widely used in the defect detection and performance evaluation of conductive materials due to its advantages such as accuracy, efficiency, and convenience [4,5,6,7]. Erefore, defect imaging has received extensive attentions from researchers in recent years. He et al used pulsed eddy current C-scan to detect the surface and subsurface of metal plate specimen and realized the recognition and classification of simple defects based on defect images [8]. Xu et al detected the hardness distribution of metal plate welding seam through eddy current scanning and judged the hardness distribution of specimen welding surface from the actual scanning image [9].
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