Deep Learning for Time Series-Based Acoustic Emission Damage Classification in Composite Materials
Source abstract
Abstract
Acoustic emission (AE) technology is of great importance for damage detection and classification in carbon fiber reinforced composite materials. In this study, state-of-the-art deep learning (DL) models for time series were employed to classify three types of damage data, namely fiber breakage, matrix cracking, and delamination, obtained from tensile damage tests. The raw AE time series data were used as inputs to compare the classification performance of eight different deep learning models including FCN, ResNet, XResNet, LSTM_FCN, InceptionTime, XceptionTime, mWDN, and LSTM. The evaluation and analysis showed that XceptionTime, InceptionTime, and ResNet models achieved better training, validation, and testing accuracy, enabling accurate classification of composite material damage. Notably, these three models also demonstrated ideal classification performance for imbalanced data in the composite material AE dataset, providing reference methods for imbalanced data classification problems with small sample sizes in practical engineering applications.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Ultrasonics and Acoustic Wave Propagation · Infrastructure Maintenance and Monitoring · Mineral Processing and Grinding
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
Show source-provided references
1. Chawla, K.K., Composite Materials, Cham: Springer, 2019, pp. 297–311.10.1007/978-3-030-28983-6 ↗
2. Gao, Y., Xiao, D., He, T., Lin, Y., Li, N., Ye, Q., and Wang, Y., Identification of damage mechanisms of carbon fiber reinforced silicon carbide composites under static loading using acoustic emission monitoring, Ceram. Int., 2019, vol. 45, pp. 13847–13858. https://doi.org/10.1016/j.ceramint.2019.04.08210.1016/j.ceramint.2019.04.082 ↗
3. Pinho, S.T., Robinson, P., and Iannucci, L., Fracture toughness of the tensile and compressive fibre failure modes in laminated composites, Compos. Sci. Technol., 2006, vol. 66, pp. 2069–2079. https://doi.org/10.1016/j.compscitech.2005.12.02310.1016/j.compscitech.2005.12.023 ↗
4. Padmaraj, N.H., Pai, D.K., Shreepannaga, S., and Kini, M.V., Fatigue behaviour and damage characterization of quasi-isotropic carbon/epoxy laminates, Cogent Eng., 2022, vol. 9, no. 1, p. 2077680. https://doi.org/:10.1080/23311916.2022.207768010.1080/23311916.2022.2077680 ↗
5. Turon, A., Camanho, P.P., Costa, J., and Davila, C.G., A damage model for the simulation of delamination in advanced composites under variable-mode loading, Mech. Mater., 2006, vol. 38, pp. 1072–1089. https://doi.org/10.1016/j.mechmat.2005.10.00310.1016/j.mechmat.2005.10.003 ↗
6. Chelliah, S.K., Kannivel, S.K., and Vellayaraj, A., Characterization of failure mechanism in glass, carbon and their hybrid composite laminates in epoxy resin by acoustic emission monitoring, Nondestr. Test. Eval., 2019, vol. 34, pp. 254–266. https://doi.org/10.1080/10589759.2019.159082910.1080/10589759.2019.1590829 ↗
7. Gul, S., Tabrizi, I.E., Okan, B.S., Kefal, A., and Yildiz, M., An experimental investigation on damage mechanisms of thick hybrid composite structures under flexural loading using multi-instrument measurements, Aerospace Sci. Technol., 2021, vol. 117, p. 106921. https://doi.org/10.1016/j.ast.2021.10692110.1016/j.ast.2021.106921 ↗
8. Jinachandran, S. and Rajan, G., Fibre Bragg grating based acoustic emission measurement system for structural health monitoring applications, Materials, 2021, vol. 14. https://doi.org/10.3390/ma1404089710.3390/ma14040897 ↗
9. Behnia, A., Ranjbar, N., Chai, H.K., and Masaeli, M., Failure prediction and reliability analysis of ferrocement composite structures by incorporating machine learning into acoustic emission monitoring technique, Constr. Build. Mater., 2016, vol. 122, pp. 823–832. https://doi.org/10.1016/j.conbuildmat.2016.06.13010.1016/j.conbuildmat.2016.06.130 ↗
10. Lissek, F., Haeger, A., Knoblauch, V., Hloch, S., Pude, F., and Kaufeld, M., Acoustic emission for interlaminar toughness testing of CFRP: Evaluation of the crack growth due to burst analysis, Compos. B Eng., 2018, vol. 136, pp. 55–62. https://doi.org/10.1016/j.compositesb.2017.10.01210.1016/j.compositesb.2017.10.012 ↗
11. Liu, H. and Zhang, Y., Image-driven structural steel damage condition assessment method using deep learning algorithm, Measurement, 2019, vol. 133, pp. 168–181. https://doi.org/10.1016/j.measurement.2018.09.08110.1016/j.measurement.2018.09.081 ↗
12. Krizhevskii, A., Sutskever, I., and Geoffrey E. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM, 2012, vol. 60, no. 6, pp. 84–90. https://doi.org/10.1145/306538610.1145/3065386 ↗
13. Ebrahimkhanlou, A., Schneider, M.B., Dubuc, B., and Salamone, S., A deep learning framework for acoustic emission sources localization and characterization in complex aerospace, Mater. Eval., 2021, vol. 79, pp. 391–400. https://doi.org/10.32548/2021.me-0417910.32548/2021.me-04179 ↗
14. Sathiyamurthy, R., Duraiselvam, M., and Sevvel, P., Acoustic emission based deep learning technique to predict adhesive bond strength of laser processed CFRP composites, FME Trans., 2020, vol. 48, pp. 611–619. https://doi.org/10.5937/fme2003611S10.5937/fme2003611S ↗
15. Haile, M.A., Zhu, E., Hsu, C., and Bradley, N., Deep machine learning for detection of acoustic wave reflections, Struct. Health Monit., 2020, vol. 19, pp. 1340–1350. https://doi.org/10.1177/147592171988164210.1177/1475921719881642 ↗
16. Sikdar, S., Liu, D., and Kundu, A., Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel, Compos. B Eng., 2022, vol. 228. https://doi.org/10.1016/j.compositesb.2021.10945010.1016/j.compositesb.2021.109450 ↗
17. Daugela, A., Chang, C.H., and Peterson, D.W., Deep learning based characterization of nanoindentation induced acoustic events, Mater. Sci. Eng. A, 2021, vol. 800, p. 140273. https://doi.org/10.1016/j.msea.2020.14027310.1016/j.msea.2020.140273 ↗
18. Ebrahim, S.A., Poshtan, J., Jamali, S.M., and Ebrahim, N.A., Quantitative and qualitative analysis of time-series classification using deep learning, IEEE Access, 2020, vol. 8, pp. 90202–90215. https://doi.org/10.1109/ACCESS.2020.299353810.1109/ACCESS.2020.2993538 ↗
19. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P., Deep learning for time series classification: a review, Data Min. Knowl. Discovery, 2019, vol. 33, pp. 917–963. https://doi.org/10.1007/s10618-019-00619-110.1007/s10618-019-00619-1 ↗
20. Guo, F., Li, W., Jiang, P., Chen, F., and Liu, Y., Deep learning approach for damage classification based on acoustic emission data in composite materials, Materials, 2022, vol. 15. https://doi.org/10.3390/ma1512427010.3390/ma15124270 ↗
21. Tu, N.D.K., Noh, M., Ko, Y., Kim, J., Kang, C.Y., and Kim, H., Enhanced electromechanical performance of P(VDF-TrFE-CTFE) thin films hybridized with highly dispersed carbon blacks, Compos. B Eng., 2018, vol. 152, pp. 133–138. https://doi.org/10.1016/j.compositesb.2018.06.03610.1016/j.compositesb.2018.06.036 ↗
22. Zeng, J., Gao, W., and Liu, F., Interfacial behavior and debonding failures of full-scale CFRP-strengthened H-section steel beams, Compos. Struct., 2018, vol. 201, pp. 540–552. https://doi.org/10.1016/j.compstruct.2018.06.04510.1016/j.compstruct.2018.06.045 ↗
23. Pennecchi, F.R., Kuselman, I., Di Rocco, A., Hibbert, D.B., Sobina, A., and Sobina, E., Specific risks of false decisions in conformity assessment of a substance or material with a mass balance constraint—A case study of potassium iodate, Measurement, 2021, vol. 173. https://doi.org/10.1016/j.measurement.2020.10866210.1016/j.measurement.2020.108662 ↗
24. Liu, W. and Chen, P., Theoretical analysis and experimental investigation of the occurrence of fiber bridging in unidirectional laminates under Mode I loading, Compos. Struct., 2021, vol. 257. https://doi.org/10.1016/j.compstruct.2020.11338310.1016/j.compstruct.2020.113383 ↗
25. Kundu, A., Sikdar, S., Eaton, M., and Navaratne, R., A generic framework for application of machine learning in acoustic emission-based damage identification, 13th Int. Conf. Damage Assess. Struct. (Porto, 2020), pp. 244–262.10.1007/978-981-13-8331-1_18 ↗
26. Li, H., Zhang, K., Cheng, H., Suo, H., Cheng, Y., and Hu, J., Multi-stage mechanical behavior and failure mechanism analysis of CFRP/Al single-lap bolted joints with different seawater ageing conditions, Compos. Struct., 2019, vol. 208, pp. 634–645. https://doi.org/10.1016/j.compstruct.2018.10.04410.1016/j.compstruct.2018.10.044 ↗
27. Aljazaeri, Z.R., Janke, M.A., and Myers, J.J., A novel and effective anchorage system for enhancing the flexural capacity of RC beams strengthened with FRCM composites, Compos. Struct., 2019, vol. 210, pp. 20–28. https://doi.org/10.1016/j.compstruct.2018.10.11010.1016/j.compstruct.2018.10.110 ↗
28. Jierula, A., Wang, S., Oh, T., Lee, J., and Lee, J.H., Detection of source locations in RC columns using machine learning with acoustic emission data, Eng. Struct., 2021, vol. 246, p. 112992. https://doi.org/10.1016/j.engstruct.2021.11299210.1016/j.engstruct.2021.112992 ↗
29. Muir, C., Swaminathan, B., Almansour, A.S., Sevener, K., Smith, C., Presby, M., Kiser, J.D., Pollock, T.M., and Daly, S., Damage mechanism identification in composites via machine learning and acoustic emission, NPJ Comput. Mater., 2021, vol. 7. https://doi.org/10.1038/s41524-021-00565-x10.1038/s41524-021-00565-x ↗
30. Nelon, C., Myers, O., and Hall, A., The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review, J. Compos. Mater., 2022, vol. 56, pp. 1417–1452. https://doi.org/10.1177/0021998321103704810.1177/00219983211037048 ↗
31. Wang, X.C.M.C., Impact damage detection of CFRP laminates using a convolutional neural network based on wavelet packet decomposition, Compos. Struct., 2021, p. 114665. https://doi.org/10.1016/j.compstruct.2021.11466510.1016/j.compstruct.2021.114665 ↗
32. Lissner, M., Erice, B., Alabort, E., Thomson, D., Cui, H., Kaboglu, C., Blackman, B.R.K., Gude, M., and Petrinic, N., Multi-material adhesively bonded structures: Characterisation and modelling of their rate-dependent performance, Compos. B Eng., 2020, vol. 195. https://doi.org/10.1016/j.compositesb.2020.10807710.1016/j.compositesb.2020.108077 ↗
33. Fang, C.H.Y.S., Structural health monitoring of composite laminates using an acoustic emission-based recurrent neural network, J. Intel. Mat. Syst. Struct., 2021, pp. 3–16. https://doi.org/10.1177/1045389X2092241910.1177/1045389X20922419 ↗
34. Han, J.Z.B.L., A convolutional neural network for classification of acoustic emission signals in composite delamination detection, Compos. Sci. Technol., 2021, p. 108795. https://doi.org/10.1016/j.compscitech.2020.10879510.1016/j.compscitech.2020.108795 ↗
35. Kumar, V., Yokozeki, T., Karch, C., Hassen, A.A., Hershey, C.J., Kim, S., Lindahl, J.M., Barnes, A., Bandari, Y.K., and Kunc, V., Factors affecting direct lightning strike damage to fiber reinforced composites: A review, Compos. B Eng., 2020, vol. 183. https://doi.org/10.1016/j.compositesb.2019.10768810.1016/j.compositesb.2019.107688 ↗
36. Xu, J.H.Q.L., A novel framework for identifying damage mechanisms in CFRP composites using deep learning techniques, Compos. B Eng., 2020, p. 107710. https://doi.org/10.1016/j.compositesb.2019.10771010.1016/j.compositesb.2019.107710 ↗
37. Arribasplata-Seguin, A., Quispe-Dominguez, R., Tupia-Anticona, W., and Acosta-Sullcahuaman, J., Rotational molding parameters of wood-plastic composite materials made of recycled high density polyethylene and wood particles, Compos. B Eng., 2021, vol. 217. https://doi.org/10.1016/j.compositesb.2021.10887610.1016/j.compositesb.2021.108876 ↗
38. Tinkloh, S., Wu, T., Troester, T., and Niendorf, T., A micromechanical-based finite element simulation of process-induced residual stresses in metal-CFRP-hybrid structures, Compos. Struct., 2020, vol. 238. https://doi.org/10.1016/j.compstruct.2020.11192610.1016/j.compstruct.2020.111926 ↗
39. Wang, Y., Chi, Z., and Liu, J., On buckling behaviors of a typical bending-dominated periodic lattice, Compos. Struct., 2021, vol. 258. https://doi.org/10.1016/j.compstruct.2020.11320410.1016/j.compstruct.2020.113204 ↗
40. Giordano, A., Mao, L., and Chiang, F., Full-field experimental analysis of a sandwich beam under bending and comparison with theories, Compos. Struct., 2021, vol. 255. https://doi.org/10.1016/j.compstruct.2020.11296510.1016/j.compstruct.2020.112965 ↗
41. Hamel, C.M., Kuang, X., and Qi, H.J., Modeling the dissolution of thermosetting polymers and composites via solvent assisted exchange reactions, Compos. B Eng., 2020, vol. 200. https://doi.org/10.1016/j.compositesb.2020.10836310.1016/j.compositesb.2020.108363 ↗
42. Huang, H.Z.X.S., A hybrid method of 1D-CNN and BiLSTM for acoustic emission signal classification and damage diagnosis of CFRP composites, Compos. Struct., 2021, p. 112836. https://doi.org/10.1016/j.compstruct.2020.11283610.1016/j.compstruct.2020.112836 ↗
43. Machine Learning, in: Encyclopedia of Animal Cognition and Behavior, Vonk, J., Shackelford, T.K., Eds., Cham: Springer, 2022, p. 4038.
44. Chollet, F., Deep Learning with Python, Shelter Island: Manning Publ. Co., 2018.
45. Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, Commun. ACM, 2017, vol. 60, pp. 84–90. https://doi.org/10.1145/306538610.1145/3065386 ↗
46. Tsai, L., Fast AI Deep Learning from the Foundations. https://www.fast.ai/. Accessed March 29, 2023.
47. Grandini, M., Bagli, E., and Visani, G., Metrics for multi-class classification: An overview, arXiv, 2020, vol. 17.