Laser & Optoelectronics Progress, Volume. 61, Issue 23, 2312002(2024)
Near-Surface Defect Detection Using Convolutional Neural Network and Laser Ultrasound Testing
Fig. 1. Time spectrum diagram based on db4 wavelet transform. (a) Defect depth 0.5 mm, angle 20°; (b) defect depth 1.5 mm, angle 40°; (c) defect depth 3.5 mm, angle 140°; (d) defect depth 10 mm, angle 100°
Fig. 4. Time-domain signal and locally enlarged image of reflected wave with different depth defects. (a) Time-domain signal; (b) locally enlarged image
Fig. 5. Time-domain signal and locally enlarged image of reflected wave with different angle defects. (a) Time-domain signal; (b) locally enlarged image
Fig. 6. Schematic diagrams of experimental platform. (a) Principle diagram of laser non-destructive testing; (b) physical image of laser ultrasonic testing system; (c) top view of metal sample; (d) detailed diagram of laser parameter settings
Fig. 7. Laser ultrasonic signals obtained on specimens with different defect widths, depths and angles and comparison of experimental and simulation data. (a)‒(c) Laser ultrasonic signals; (d) comparison of experimental and simulation data
Fig. 8. Defect depth and angle prediction of validation set and test set. (a) VGG19 network loss function change diagram; (b) ResNet101 network loss function change diagram; (c) DenseNet169 network loss function change diagram; (d) VGG19 model validation set prediction result diagram; (e) ResNet101 model validation set prediction result diagram; (f) DenseNet169 model validation set prediction result diagram; (g) VGG19 model test set prediction result diagram; (h) ResNet101 model test set prediction result diagram; (i) DenseNet169 model test set prediction result diagram; (j) prediction results of VRD+SVR model on simulation data; (k) prediction results of VRD+SVR model on experimental + simulation data
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Mingze Guo, Xingyuan Zhang, Zhenyue Jin. Near-Surface Defect Detection Using Convolutional Neural Network and Laser Ultrasound Testing[J]. Laser & Optoelectronics Progress, 2024, 61(23): 2312002
Category: Instrumentation, Measurement and Metrology
Received: Jan. 9, 2024
Accepted: Mar. 29, 2024
Published Online: Nov. 19, 2024
The Author Email: Xingyuan Zhang (zxy_sues@163.com)
CSTR:32186.14.LOP240477