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Volume 12
Issue 16
10.3390/app12168124
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Article

Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters

by
Haiyang Li
1,*,
Yihao Liu
2,
Jin Deng
3,
Zhiwu An
4 and
Qianghua Pan
3
1
Key Laboratory of Information Detection and Processing, North University of China, Taiyuan 030051, China
2
School of Basic Medical Sciences, Central South University, Changsha 410028, China
3
China Special Equipment Inspection and Research Institute, Beijing 100029, China
4
Institute of Acoustics of the Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(16), 8124; https://doi.org/10.3390/app12168124
Submission received: 15 July 2022 / Revised: 8 August 2022 / Accepted: 9 August 2022 / Published: 13 August 2022
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Abstract

To intelligently and quickly verify the depths and angles of oblique surface cracks using the laser ultrasound technique, a crack geometry information evaluation method using a support vector machine (SVM) is proposed. The relationships between several parameters—which are based on transmitted waves and reflected waves containing crack geometry information in the time domain, frequency domain and time–frequency domain, and crack size—are presented in detail. To decide which feature parameters are involved in the SVM model, two selection standards are proposed according to the correlations between feature parameters and crack size and between the feature parameters themselves. Finally, a seven-feature-parameter SVM model which has an excellent ability to classify oblique crack depths and angles is built. The results of simulations using the finite element method (FEM) and experimental data show that seven parameters based on the SVM model are available for the oblique surface crack depth and angle evaluation and can be used to nondestructively detect natural cracks.
Keywords: laser ultrasound; support vector machine method; two selection standards; oblique crack laser ultrasound; support vector machine method; two selection standards; oblique crack

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MDPI and ACS Style

Li, H.; Liu, Y.; Deng, J.; An, Z.; Pan, Q. Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters. Appl. Sci. 2022, 12, 8124. https://doi.org/10.3390/app12168124

AMA Style

Li H, Liu Y, Deng J, An Z, Pan Q. Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters. Applied Sciences. 2022; 12(16):8124. https://doi.org/10.3390/app12168124

Chicago/Turabian Style

Li, Haiyang, Yihao Liu, Jin Deng, Zhiwu An, and Qianghua Pan. 2022. "Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters" Applied Sciences 12, no. 16: 8124. https://doi.org/10.3390/app12168124

APA Style

Li, H., Liu, Y., Deng, J., An, Z., & Pan, Q. (2022). Depth and Angle Evaluation of Oblique Surface Cracks Using a Support Vector Machine Based on Seven Parameters. Applied Sciences, 12(16), 8124. https://doi.org/10.3390/app12168124

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