journal article·2025-03

CNN-Based Similar Microwave Reflection Signals for Improved Detectability and Intelligent Characterization of Internal Defects in Composite Materials

Mingyu Gao, Liang Huo, Fei Wang, Peng Song, Yulong Gao, Guohui Yang, Junyan Liu, Zhipeng Liang, Yunji Xie, Yinghao Song

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OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Non-Destructive Testing Techniques · Industrial Vision Systems and Defect Detection

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References (33)

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1. Meng, X., Wang, F., Liang, Z., Zhang, X., Weng, J., Li, Z., Liu, J., Chen, M., Wang, Y., Yue, H.: Research on the detection of silica/phenolic composite surface cracks using instantaneous high-power xenon lamp-induced chirp-pulsed radar thermography. Measurement 15(209), 112494 (2023)10.1016/j.measurement.2023.112494 ↗
2. Wang, C., Cheng, H., Hong, C., Zhang, X., Zeng, T.: Lightweight chopped carbon fibre reinforced silica-phenolic resin aerogel nanocomposite: facile preparation, properties and application to thermal protection. Compos. A Appl. Sci. Manuf. 1(112), 81–90 (2018)10.1016/j.compositesa.2018.05.026 ↗
3. Liu, Y., Zhao, G., Xin, L., Nian, Z., Liu, T., Iqbal, A.: Analytical modeling of cutting forces and investigation of the material removal mechanism in milling of HSF/PR composite. Mater. Today Commun. 1(37), 107331 (2023)10.1016/j.mtcomm.2023.107331 ↗
4. Quattrocchi, A., Freni, F., Montanini, R.: Comparison between air-coupled ultrasonic testing and active thermography for defect identification in composite materials. Nondestructive Test. Evalu. 36(1), 97–112 (2021)10.1080/10589759.2019.1699084 ↗
5. Wang, F., Liu, J., Dong, B., Liu, G., Chen, M., Wang, Y.: Optimization of thermal-wave radar thermography by transverse heat flow suppression technique for accurate defect detection of CFRP laminates. IEEE Trans. Instrum. Meas. 12(70), 1 (2021)
6. Wang, J., Xu, T., Zhang, L., Chang, T., Zhang, J., Yan, S., Cui, H.L.: Nondestructive damage evaluation of composites based on terahertz and X-ray image fusion. NDT and E Int. 1(127), 102616 (2022)10.1016/j.ndteint.2022.102616 ↗
7. Ghandourah, E.E., Hamidi, S.H., Mohd Salleh, K.A., Wahab, M.N., Banoqitah, E.M., Alhawsawi, A.M., Moustafa, E.B.: Evaluation of welding imperfections with X-ray computed laminography for NDT inspection of carbon steel plates. J. Nondestr. Eval. 42(3), 77 (2023)10.1007/s10921-023-00989-z ↗
8. Yang, H., Yang, L., Yang, Z., Shan, Y., Gu, H., Ma, J., Zeng, X., Tian, T., Ma, S., Wu, Z.: Ultrasonic detection methods for mechanical characterization and damage diagnosis of advanced composite materials: a review. Compos. Struct. 10, 117554 (2023)10.1016/j.compstruct.2023.117554 ↗
9. Andleeb, Z., Malik, S., Hussain, G., Khawaja, H., Roemer, J., Boiger, G.K., Moatamedi, M.: Multiphysics study of infrared thermography (IRT) applications. Int. J. Multiphys. 14(3), 249–272 (2020)
10. Song, H.J., Lee, N.: Terahertz communications: challenges in the next decade. IEEE Trans. Terahertz Sci. Technol. 12(2), 105–117 (2021)10.1109/TTHZ.2021.3128677 ↗
11. Hussain, S., Mubeen, I., Ullah, N., Shah, S.S., Khan, B.A., Zahoor, M., Ullah, R., Khan, F.A., Sultan, M.A.: Modern diagnostic imaging technique applications and risk factors in the medical field: a review. Biomed. Res. Int. 6, 2022 (2022)
12. Shrifan, N.H., Jawad, G.N., Isa, N.A., Akbar, M.F.: Microwave nondestructive testing for defect detection in composites based on K-means clustering algorithm. IEEE Access. 30(9), 4820–4828 (2020)
13. Akbar MF, Jawad GN, Danoon LR, Sloan R. Delamination detection in glass-fibre reinforced polymer (GFRP) using microwave time domain reflectometry. In2018 15th European Radar Conference (EuRAD) (2018), (pp. 253–256). IEEE.10.23919/EuRAD.2018.8546540 ↗
14. Sutthaweekul, R., Tian, G., Wang, Z., Ciampa, F.: Microwave open-ended waveguide for detection and characterisation of FBHs in coated GFRP pipes. Compos. Struct. 1(225), 111080 (2019)10.1016/j.compstruct.2019.111080 ↗
15. Wang, R., Fang, Y., Gao, Q., Li, Y., Yang, X., Chen, Z.: Quantitative visualization of buried defects in GFRP via microwave reflectometry. Sensors. 23(14), 6629 (2023)10.3390/s23146629 ↗
16. Sun, H., Ramuhalli, P., Jacob, R.E.: Machine learning for ultrasonic nondestructive examination of welding defects: a systematic review. Ultrasonics 1(127), 106854 (2023)10.1016/j.ultras.2022.106854 ↗
17. Dang, H.V., Tran-Ngoc, H., Nguyen, T.V., Bui-Tien, T., De Roeck, G., Nguyen, H.X.: Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Trans. Autom. Sci. Eng. 18(4), 2087–2103 (2020)10.1109/TASE.2020.3034401 ↗
18. Xu, Y., Zhou, H., Cui, Y., Wang, X., Citrin, D.S., Zhang, L., Yan, R., Chen, X.: Full scale promoted convolution neural network for intelligent terahertz 3D characterization of GFRP delamination. Compos. B Eng. 1(242), 110022 (2022)10.1016/j.compositesb.2022.110022 ↗
19. Liu W, Wang Q, Zhang H, Li Z, Liu Q, She R, Zhang R. Automatic terahertz recognition of hidden defects in layered polymer composites based on a deep residual network with transfer learning. In2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz) (2021), (pp. 1–1). IEEE.10.1109/IRMMW-THz50926.2021.9567008 ↗
20. Huang, L., Hong, X., Yang, Z., Liu, Y., Zhang, B.: CNN-LSTM network-based damage detection approach for copper pipeline using laser ultrasonic scanning. Ultrasonics 1(121), 106685 (2022)10.1016/j.ultras.2022.106685 ↗
21. Cao, J., Li, Y., Sun, M., Chen, Y., Lischinski, D., Cohen-Or, D., Chen, B., Tu, C.: Do-conv: depthwise over-parameterized convolutional layer. IEEE Trans. Image Process. 20(31), 3726–3736 (2022)10.1109/TIP.2022.3175432 ↗
22. Yang C, Meng Y, Huo K, Xi J, Mei K. (2022) A sparse CNN accelerator for eliminating redundant computations in intra-and inter-convolutional/pooling layers. Trans. Very Large Scale Integ. (VLSI) Syst. 30(12):1902-191510.1109/TVLSI.2022.3211665 ↗
23. Roy AG, Navab N, Wachinger C. Concurrent spatial and channel ‘squeeze & excitation’in fully convolutional networks. InMedical image computing and computer assisted intervention–MICCAI 2018: 21st International Conference, Granada, Spain, Proceedings, Part I, (2018) (pp. 421–429). Springer, New York10.1007/978-3-030-00928-1_48 ↗
24. Li, Y., Wang, N., Shi, J., Hou, X., Liu, J.: Adaptive batch normalization for practical domain adaptation. Pattern Recogn. 1(80), 109–117 (2018)10.1016/j.patcog.2018.03.005 ↗
25. Hsiao, T.Y., Chang, Y.C., Chou, H.H., Chiu, C.T.: Filter-based deep-compression with global average pooling for convolutional networks. J. Syst. Architect. 1(95), 9–18 (2019)10.1016/j.sysarc.2019.02.008 ↗
26. Shang W, Sohn K, Almeida D, Lee H. Understanding and improving convolutional neural networks via concatenated rectified linear units. Ininternational conference on machine learning, (2016) (pp. 2217–2225). PMLR.
27. Liu W, Wen Y, Yu Z, Yang M. Large-margin softmax loss for convolutional neural networks. Preprint at arXiv:1612.02295. (2016)
28. Zhang Z. Improved adam optimizer for deep neural networks. In 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS) 2018 (pp. 1–2). IEEE.10.1109/IWQoS.2018.8624183 ↗
29. Wang Y, Ma X, Chen Z, Luo Y, Yi J, Bailey J. Symmetric cross entropy for robust learning with noisy labels. In Proceedings of the IEEE/CVF international conference on computer vision 2019 (pp. 322–330).10.1109/ICCV.2019.00041 ↗
30. Chen C, Mo J, Hou J, Wu H, Liao L, Sun W, Yan Q, Lin W. Topiq: A top-down approach from semantics to distortions for image quality assessment. IEEE Transactions on Image Processing. (2024)10.1109/TIP.2024.3378466 ↗
31. Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, (2018) (pp. 7132–7141).10.1109/CVPR.2018.00745 ↗
32. Abdulhameed AA, Kubík Z. Design and modeling of spiral circular magnetic probe for near field measurements in CST microwave studio. In 2021 International Conference on Applied Electronics (AE) (2021), (pp. 1–4). IEEE.10.23919/AE51540.2021.9542914 ↗
33. Huang, X., Peng, L., Mukherjee, S., Hamilton, C., Shi, X., Srinivasan, V., Davis, E., Deng, Y.: Fast VGG: an advanced pre-trained deep learning framework for multi-layered composite NDE via multifrequency near-field microwave imaging. Res. Nondestr. Eval. 22, 1–7 (2024)