A UNet++-Based Approach for Delamination Imaging in CFRP Laminates Using Full Wavefield
Abstract
1. Introduction
- (1)
- UNet++ is employed to segment the energy distribution in 2D FDS, achieving end-to-end artifact free delamination imaging.
- (2)
- The model trained with multi-frequency FDS demonstrates accurate delamination imaging, even in scenarios with input frequency offsets, while also exhibiting good noise resistance.
- (3)
- The model trained on pure simulation dataset can be used to predict experimental samples with high accuracy. It provides a solution for quantitative detection of invisible delamination damage in CFRP.
2. Dataset Preparation
2.1. Material Properties of Specimen
2.2. Numerical Simulation
2.3. Frequency Domain Spectra Extraction
2.4. Flipping Augmentation
2.5. Dataset Labeling and Splitting
3. Delamination Imaging Model Construction, Training, and Evaluation Methods
3.1. Neural Network
3.2. Training Strategy
3.3. Performance Evaluation Methods
- (1)
- Penalty for displacement: If there is a spatial offset in the predicted area, the IoU will decrease significantly, even if the size of the predicted area is correct.
- (2)
- Penalty for redundancy: If the prediction includes areas that do not correspond to the target (i.e., non-target areas), the IoU will decrease significantly, even if the target area is correctly covered.
4. Results and Discussion
4.1. Generalization in the Simulation Test Set
4.2. Generalization on Frequency Offset FDS
4.3. Robustness Under Noise Interference
4.4. Experimental Test
4.5. Generalization on Another Dataset
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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ρ (kg/m3) | E1 (GPa) | E2 (GPa) | E3 (GPa) | G12 (GPa) | G13 (GPa) | G23 (GPa) | ν12 | ν13 | ν23 |
---|---|---|---|---|---|---|---|---|---|
1600 | 172 | 11.6 | 11.6 | 7.8 | 7.8 | 3.9 | 0.36 | 0.36 | 0.55 |
Composition | Train Set | Validation Set | Test Set | Total |
---|---|---|---|---|
Sample size | 1260 | 360 | 180 | 1800 |
Method | IoU | |||
---|---|---|---|---|
Mean | Max | Min | SD | |
WF | 0.4245 | 0.7400 | 0.1085 | ±0.1425 |
UNet++ | 0.9449 | 0.9842 | 0.7595 | ±0.0292 |
Frequency of FDS | 140 kHz | 160 kHz | 180 kHz | 200 kHz | 220 kHz | 240 kHz | 260 kHz | Total |
---|---|---|---|---|---|---|---|---|
Sample size | 178 | 179 | 184 | 186 | 170 | 178 | 185 | 1260 |
Frequency of FDS | 40 kHz | 45 kHz | 50 kHz | 55 kHz | 60 kHz | Total |
---|---|---|---|---|---|---|
Sample size | 243 | 233 | 250 | 252 | 238 | 1216 |
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Yan, Y.; Yang, K.; Gou, Y.; Tang, Z.; Lv, F.; Zeng, Z.; Li, J.; Liu, Y. A UNet++-Based Approach for Delamination Imaging in CFRP Laminates Using Full Wavefield. Sensors 2025, 25, 4292. https://doi.org/10.3390/s25144292
Yan Y, Yang K, Gou Y, Tang Z, Lv F, Zeng Z, Li J, Liu Y. A UNet++-Based Approach for Delamination Imaging in CFRP Laminates Using Full Wavefield. Sensors. 2025; 25(14):4292. https://doi.org/10.3390/s25144292
Chicago/Turabian StyleYan, Yitian, Kang Yang, Yaxun Gou, Zhifeng Tang, Fuzai Lv, Zhoumo Zeng, Jian Li, and Yang Liu. 2025. "A UNet++-Based Approach for Delamination Imaging in CFRP Laminates Using Full Wavefield" Sensors 25, no. 14: 4292. https://doi.org/10.3390/s25144292
APA StyleYan, Y., Yang, K., Gou, Y., Tang, Z., Lv, F., Zeng, Z., Li, J., & Liu, Y. (2025). A UNet++-Based Approach for Delamination Imaging in CFRP Laminates Using Full Wavefield. Sensors, 25(14), 4292. https://doi.org/10.3390/s25144292