Tensile Deformation Damage and Clustering Analysis of Acoustic Emission Signals in Three-Dimensional Woven Composites

  • W. Zhou
  • Y. N. Zhang
  • W. Z. Zhao
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 218)


To study the deformation and damage behaviors of three-dimensional (3D) woven composites, uniaxial tensile tests of the composites were conducted and real-time acoustic emission (AE) signals and speckle images were simultaneously obtained. A k-means clustering algorithm combined with principal component analysis (PCA) was used to analyze the AE signals, and the deformation fields on the surface of composite specimens were measured by the digital image correlation (DIC) method. It was found that the corresponding AE signals were divided into three typical clusters. On the basis of the frequency range of each type, the relationships between the resulting clusters and damage mechanisms were established. The results showed that different damage mechanisms in 3D woven composites are well determined by the frequency rather than the peak amplitude. The characteristics of AE signals such as the high frequency, the medium frequency, and most of the low frequency are associated with fiber damage, fiber/matrix debonding, and matrix cracking, respectively. The change in the displacement distribution can effectively enable visual inspection of damage accumulation in composites.

Key words

Three-dimensional woven composites Clustering analysis Acoustic emission Digital image correlation 



The authors gratefully acknowledge financial support received from the National Natural Science Foundation of China (grant no. 11502064) and the Natural Science Foundation of Hebei Province (grant no. E2016201019).


  1. 1.
    Z. Hu, R. Karki, Prediction of mechanical properties of three-dimensional fabric composites reinforced by transversely isotropic carbon fibers. J. Compos. Mater. 49(12), 1513–1524 (2015)ADSCrossRefGoogle Scholar
  2. 2.
    J.K. Kim, Y.W. Mai, High strength, high fracture toughness fibre composites with interface control—a review. Compos. Sci. Technol. 41(4), 333–378 (1991)CrossRefGoogle Scholar
  3. 3.
    T.C. Henry, C.E. Bakis, Compressive strength and stiffness of filament-wound cylinders. J. Reinf. Plast. Comp. 35(21), 1543–1553 (2016)CrossRefGoogle Scholar
  4. 4.
    Q. Wu, F. Yu, Y. Okabe, S. Kobayashi, Application of a novel optical fiber sensor to detection of acoustic emissions by various damages in CFRP laminates. Smart Mater. Struct. 24(1), 015011 (2015)ADSCrossRefGoogle Scholar
  5. 5.
    S.K. Al-Jumaili, K.M. Holford, M.J. Eaton, J.P. McCrory, M.R. Pearson, Classification of acoustic emission data from buckling test of carbon fibre panel using unsupervised clustering techniques. Struct. Health Monit. 14(3), 241–251 (2015)CrossRefGoogle Scholar
  6. 6.
    F. Cesari, V.D. Re, G. Minak, A. Zucchelli, Damage and residual strength of laminated carbon–epoxy composite circular plates loaded at the centre. Compos. Part A Appl. Sci. Manuf. 38(4), 1163–1173 (2007)CrossRefGoogle Scholar
  7. 7.
    M. Gresil, M.N. Saleh, C. Soutis, Transverse crack detection in 3D angle interlock glass fibre composites using acoustic emission. Materials 9(8), 699 (2016)ADSCrossRefGoogle Scholar
  8. 8.
    C. Liu, L. Cheng, X. Luan, B. Li, J. Zhou, Damage evolution and real-time non-destructive evaluation of 2D carbon-fiber/SiC-matrix composites under fatigue loading. Mater. Lett. 62(24), 1163–1173 (2008)Google Scholar
  9. 9.
    M. Shateri, M. Ghaib, D. Svecova, D. Thomson, On acoustic emission for damage detection and failure prediction in fiber reinforced polymer rods using pattern recognition analysis. Smart Mater. Struct. 26(6), 065023 (2017)ADSCrossRefGoogle Scholar
  10. 10.
    J.P. McCrory, S.K. Al-Jumaili, D. Crivelli, M.R. Pearson, M.J. Eaton, Damage classification in carbon fibre composites using acoustic emission: a comparison of three techniques. Compos. Part B Eng. 68(5), 424–430 (2015)CrossRefGoogle Scholar
  11. 11.
    S.V. Lomov, M. Karahan, A.E. Bogdanovich, I. Verpoest, Monitoring of acoustic emission damage during tensile loading of 3D woven carbon/epoxy composites. Text. Res. J. 84(13), 1373–1384 (2014)CrossRefGoogle Scholar
  12. 12.
    L. Li, S.V. Lomov, Y. Xiong, V. Carvelli, Cluster analysis of acoustic emission signals for 2D and 3D woven glass/epoxy composites. Compos. Struct. 116(1), 286–299 (2014)CrossRefGoogle Scholar
  13. 13.
    L. Li, S.V. Lomov, Y. Xiong, Correlation of acoustic emission with optically observed damage in a glass/epoxy woven laminate under tensile loading. Compos. Struct. 123(123), 45–53 (2015)CrossRefGoogle Scholar
  14. 14.
    X. Wang, S.P. Ma, Y.T. Zhao, Z.B. Zhou, P.W. Chen, Observation of damage evolution in polymer bonded explosives using acoustic emission and digital image correlation. Polym. Test. 30(8), 861–866 (2011)CrossRefGoogle Scholar
  15. 15.
    J. Cuadra, P.A. Vanniamparambil, K. Hazeli, I. Bartoli, A. Kontsos, Damage quantification in polymer composites using a hybrid NDT approach. Compos. Sci. Technol. 83(15), 11–21 (2013)CrossRefGoogle Scholar
  16. 16.
    C.R. Ramirez-Jimenez, N. Papadakis, N. Reynolds, T.H. Gan, P. Purnell, Identification of failure modes in glass/polypropylene composites by means of the primary frequency content of the acoustic emission event. Compos. Sci. Technol. 64(12), 1819–1827 (2004)CrossRefGoogle Scholar
  17. 17.
    R. Gutkin, C.J. Green, S. Vangrattanachai, S.T. Pinho, P. Robinson, On acoustic emission for failure investigation in CFRP: pattern recognition and peak frequency analyses. Mech. Syst. Signal Process. 25(4), 1393–1407 (2011)ADSCrossRefGoogle Scholar
  18. 18.
    Y.J. Ma, X.F. Yao, D. Wang, Experimental investigation on mechanical properties of CNT film using digital speckle correlation method. Opt. Laser Eng. 50(11), 1575–1581 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • W. Zhou
    • 1
  • Y. N. Zhang
    • 1
  • W. Z. Zhao
    • 1
  1. 1.Non-destructive Testing LaboratoryHebei UniversityBaodingChina

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