Abstract
Composite structures are prone to internal defects such as delamination. Due to this, it is vital to recognize internal flaws in composite materials accurately because there is possibility that these internal defects can severely degrade the composite structure’s strength. This work aims to develop an intelligent complex composite defect severity classification which will contribute to efficient monitoring of composite structures during their service life. Firstly, the behavior of guided ultrasonic waves is processed and transformed into image database using continuous wavelet transform method. Then, a defect classification framework is proposed by using convolutional neural network to classify six types of defect sizes. A total of 798, 342, and 90 images are used for training, validation, and testing, respectively. The results present that the proposed system achieved approximately above 86% of precision and recall for all six defects classes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
A.B. Hossein Towsyfyan, R. Boardman, T. Blumensath, Successes and challenges in non-destructive testing of aircraft composite structures. Chin. J. Aeronaut. 33(3), 771–791 (2020). https://doi.org/10.1016/j.cja.2019.09.017
M. Kersemans, E. Verboven, J. Segers, S. Hedayatrasa, W.V. Paepegem, Non-destructive testing of composites by ultrasound, local defect resonance and thermography. Multidiscipl. Digit. Publ. Inst. Proc. 2(8), 554 (2018)
K. Virupakshappa, E. Oruklu, Multi-class classification of defect types in ultrasonic NDT signals with convolutional neural networks, in 2019 IEEE International Ultrasonics Symposium (IUS) (IEEE, 2019), pp. 1647–1650
J. Miranda, J. Veith, S. Larnier, A. Herbulot, M. Devy, Machine learning approaches for defect classification on aircraft fuselage images acquired by an UAV, in Fourteenth International Conference on Quality Control by Artificial Vision, vol. 11172 (International Society for Optics and Photonics, 2019), p. 1117208
L. Xu, S. Yuan, J. Chen, Y. Ren, Guided wave-convolutional neural network based fatigue crack diagnosis of aircraft structures. Sensors 19(16), 3567 (2019)
D.A. Clayton, H. Santos-Villalobos, J. Baba, Evaluation of Advanced Signal Processing Techniques to Improve Detection and Identification of Embedded Defects (Oak Ridge National Lab. (ORNL), Oak Ridge, TN, 2016)
S. Barrios, D. Buldain, M.P. Comech, I. Gilbert, Partial discharge identification in MV switchgear using scalogram representations and convolutional AutoEncoder. IEEE Trans. Power Deliv. (2020)
X. Chen, Z. Feng, C. Zhao, Reassigned wavelet scalogram for gear fault detection under nonstationary operational conditions, in 2016 Prognostics and System Health Management Conference (PHM-Chengdu) (IEEE, 2016), pp. 1–5
J.-A. Liu, K. Wei, J. Tan, J. Huang, X. Wu, A signal analysis method based on reassigned scalogram, in 2010 3rd International Congress on Image and Signal Processing, vol. 6 (IEEE, 2010), pp. 2901–2904
H. Dai, F. Ge, Q. Li, W. Zhang, T. Liu, Optimize CNN model for FMRI signal classification via Adanet-based neural architecture search, in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (IEEE, 2020), pp. 1399–1403
S. Li, H. Feng, EEG signal classification method based on feature priority analysis and CNN, in 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) (IEEE, 2019), pp. 403–406
A. Khumaidi, E.M. Yuniarno, M.H. Purnomo, Welding defect classification based on convolution neural network (CNN) and Gaussian kernel, in 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA) (IEEE, 2017), pp. 261–265
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wilson, L., Khairuddin, A.S.M., Khairuddin, U., Murat, B.I.S. (2022). Defect Severity Classification of Complex Composites Using CWT and CNN. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-16-8484-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8483-8
Online ISBN: 978-981-16-8484-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)