Abstract
We propose a real-time non-destructive evaluation technique for defect detection in composites using highly nonlinear solitary waves (HNSWs) and a deep learning algorithm based on the convolution neural network (CNN). This technique implements deep learning to identify the presence of defects and classify the defect locations in the thickness direction of composites through HNSWs with strong energy intensity and non-distortive nature. To collect HNSW datasets for training and validation of the deep learning algorithm, AS4/PEEK composite specimens with artificial delamination are fabricated and HNSW datasets are generated from the experimental setup of a granular crystal sensor. Testing pretrained CNN based algorithms verifies the performance of detecting and classifying defects by location in composite plates.
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References
Hahn, H.T., Tsai, S.W.: Introduction to Composite Materials. Technomic Publishing Co., Inc., Lancaster (1980)
Daniel, L.M., Ishai, O.: Engineering Mechanics of Composite Materials. Oxford University Press, New York (1994)
Sohn, H., et al.: A review of structural health monitoring literature: 1996–2001. Report. LA-13976-MS: Los Alamos National Laboratory, NM (2003)
Salawu, O.S.: Detection of structural damage through changes in frequency: a review. Eng. Struct. 19(9), 718–723 (1997)
Giurgiutiu, V.: Structural Health Monitoring of Aerospace Composites (2015)
Ramakrishnan, M., Rajan, G., Semenova, Y., Farrell, G.: Overview of fiber optic sensor technologies for strain/temperature sensing applications in composite materials. Sensors 16(1), 99 (2016)
Yang, J., Silvestro, C., Khatri, D., De Nardo, L., Daraio, C.: Interaction of highly nonlinear solitary waves with linear elastic media. Phys. Rev. E 83, 046606 (2001)
Khatri, D., Rizzo, P., Daraio, C.: Highly nonlinear waves’ sensor technology for highway infrastructures. In: Proceedings of SPIE Smart Structures/Nondestructive Evaluation and Health Monitoring, San Diego, CA, vol. 6934, p. 225 (2008)
Xianglei, N., Rizzo, P., Daraio, C.: Actuators for the generation of highly nonlinear solitary waves. Rev. Sci. Instrum. 82, 034902 (2011)
Daraio, C., Nesterenko, V.F., Herbold, E.B., Jin, S.: Strongly nonlinear waves in a chain of Teflon beads. Phys. Rev. E 72(1), 016603 (2005)
Sen, S., Hong, J., Bang, J., Avalos, E., Doney, R.: Solitary waves in the granular chain. Phys. Rep. 462, 21–66 (2008)
Ni, X., Rizzo, P., Yang, J., Khatri, D., Daraio, C.: Monitoring the hydration of cement by means of highly nonlinear solitary waves. NDT E Int. 52, 76–85 (2012)
Schiffer, A., Alia, R.A., Cantwell, W., Kim, E., Lee, D., Kim, T.-Y.: Elastic interaction between nonlinear solitary waves in granular chains and composite beams: experiments and modelling. Int. J. Mech. Sci. 170, 105350 (2020)
Schiffer, A., Kim, T.-Y.: Modelling of the interaction between nonlinear solitary waves and composite beams. Int. J. Mech. Sci. 151, 181–192 (2019)
Yang, J., Khatri, D., Anzel, P., Daraio, C.: Interaction of highly nonlinear solitary waves with thin plates. Int. J. Solids Struct. 49, 1463–1471 (2012)
Schiffer, A., Kim, T.-Y.: Interaction of highly nonlinear solitary waves with rigid polyurethane foams. Int. J. Solids Struct. 152–152, 39–50 (2018)
Kim, E., Restuccia, F., Yang, J., Daraio, C.: Solitary wave-based delamination detection in composite plates using a combined granular crystal sensor and actuator. Smart Mater. Struct. 24, 125004 (2015)
Singhal, T., Kim, E., Kim, T.-Y., Yang, J.: Weak bond detection in composites using highly nonlinear solitary waves. Smart Mater. Struct. 26, 055011 (2017)
Schiffer, A., Alkhaja, A.I., Yang, J., Esfahani, E.N., Kim, T.Y.: Interaction of highly nonlinear solitary waves with elastic solids containing a spherical void. Int. J. Solids Struct. 118–119, 204–212 (2017)
Yoon, S., Schiffer, A., Cantwel, W.J., Kim, T.-Y.: Detection of core-skin disbands in honeycomb composite sandwich structures using highly nonlinear solitary wave. Compos. Struct. 256, 113071 (2021)
Nasrollahi, A., Lucht, R., Rizzo, P.: Solitary waves to assess the internal pressure and the rubber degradation of tennis balls. Exp. Mech. 59, 65–77 (2019)
Ni, X., Rizzo, P.: Highly Nonlinear solitary waves for the inspection of adhesive joints. Exp. Mech. 52, 1493–1501 (2012)
Yang, J., et al.: Site-specific quantification of bone quality using highly nonlinear solitary waves. J. Biomech. Eng. 134, 101001 (2012)
Yoon, S., Schiffer, A., Kim, J.J., Jang, I.G., Lee, S., Kim, T.-Y.: Numerical predictions of the interaction between highly nonlinear solitary waves and the microstructure of trabecular bone in the femoral head. J. Mech. Behav. Biomed. Mater. 109, 103805 (2020)
Yoon, S., Schiffer, A., Jang, I.G., Lee, S., Kim, T.-Y.: Predictions of the elastic modulus of trabecular bone in the femoral head and the intertrochanter: a solitary wave-based approach. Biomech. Model. Mechanobiol. 20(5), 1733–1749 (2021). https://doi.org/10.1007/s10237-021-01473-1
Kim, T.-Y., Yoon, S., Schiffer, A., Jang, I.G., Lee, S.: Site-specific quality assessment of trabecular bone using highly nonlinear solitary waves. Lect. Notes Civ. Eng. 127, 893–901 (2021)
Yang, J., et al.: Nondestructive evaluation of orthopaedic implant stability in THA using highly nonlinear solitary waves. Smart Mater. Struct. 21, 012002 (2012)
Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53(8), 5455–5516 (2020). https://doi.org/10.1007/s10462-020-09825-6
Shrestha, A., Mahmood, A.: Review of deep learning algorithms and architectures. IEEE Access 7, 53040–53065 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Acknowledgements
The authors acknowledge support from Khalifa University Competitive Internal Research Award (CIRA) (No. CIRA-2019-009), Award No. RCII-2019-003, and the Center for Cyber-Physical Systems, Khalifa University, under Grant Number 8474000137-RC1-C2PS-T3. This research was also performed as part of the Aerospace Research and Innovation Center (ARIC) program which is jointly-funded by STRATA Manufacturing PJSC (a Mubadala company) and Khalifa University of Science and Technology.
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Kim, TY., Yoon, S., Yeun, C.Y., Cantwell, W.J., Cho, CS. (2023). Site-Specific Defect Detection in Composite Using Solitary Waves Based on Deep Learning. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-031-07322-9_45
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