Artificial Neural Network Application for Damages Classification in Fibreglass Pre-impregnated Laminated Composites (FGLC) from Ultrasonic Signal

  • M. F. MahmodEmail author
  • Elmi Abu Bakar
  • Raiminor Ramzi
  • Mohd Azhar Harimon
  • N. Abdul Latif
  • Mohammad Sukri Mustapa
  • Al Emran Ismail
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)


Ultrasonic testing (UT) is a major Non-Destructive Test (NDT) technique used in composite laminates inspection. The traveling ultrasonic waves in various mode display is used to detect any damage. A qualified NDT inspector who complies with ISO 9712 is required to interpret the damages form the ultrasonic signal. However, the inspection performance is subjected to human factors due to fatigue and lack of concentration. Therefore, a study of a damages detection system is carried out to detect and classify the damages. In this study, the damage detection of pre-impregnated laminated composites has been made using ultrasonic prototype machine namely ISI i-InspeX TWO and the classification from the extracted features of A-scan mode display has been performed using Back Proportional Network (BPN). The classification employs two classification stages which is CLASS-1 and CLASS-2 for the first and the second phase respectively. The results of the average performance of CLASS-1 concluded that the proposed approach attained reliable results with the accuracy of 99.99% while the performance result of CLASS-2 was 94.21%. Thus, these promising classification performances showed that the proposed system is applicable to assist NDT inspectors in their quality inspection process.


Non-destructive testing Ultrasonic testing Fibre-glass pre-impregnated laminated composites Artificial neural network (ANN) 


  1. 1.
    Federal Aviation Administration (FAA): Aviation Maintenance Technician Handbook, vol. 1. Aviation Supplies & Academics (ASA), Oklahoma (2012)Google Scholar
  2. 2.
    Perez, M., Gil, L., Oller, S.: Impact damage identification in composite laminates using vibration testing. Compos. Struct. 108, 267–276 (2014)CrossRefGoogle Scholar
  3. 3.
    Ambu, R., Aymerich, F., Ginesu, F., Priolo, P.: Assessment of NDT interferometric techniques for impact damage detection in composite laminates. Compos. Sci. Technol. 66, 199–205 (2006)CrossRefGoogle Scholar
  4. 4.
    Adams, R.D., Cawley, P.: Defect types and non-destructive testing techniques for composites and bonded joints. Constr. Build. Mater. 3, 170–183 (1989)CrossRefGoogle Scholar
  5. 5.
    Sayer, M., Bektas, M.B., Demir, E., Callioğlu, F.: The effect of temperatures on hybrid composite laminates under impact loading. Compos. Part B Eng. 43, 2152–2160 (2012)Google Scholar
  6. 6.
    Liu, J., Zhu, X., Li, T., Zhou, Z., Wu, L., Ma, L.: Experimental study on the low velocity impact responses of all-composite pyramidal truss core sandwich panel after high temperature exposure. Compos. Struct. 116, 670–681 (2014)CrossRefGoogle Scholar
  7. 7.
    Watkins, S.E., Akhavan, F., Dua, R., Chandrashekhara, K., Wunsch, D.C.: Impact-induced damage characterization of composite plates using neural networks. Smart Mater. Struct. 16, 515–524 (2007)CrossRefGoogle Scholar
  8. 8.
    Jang, B.W., Kim, C.G.: Real-time detection of low-velocity impact-induced delamination onset in composite laminates for efficient management of structural health. Compos. Part B 123, 124–135 (2017)CrossRefGoogle Scholar
  9. 9.
    Valdes, S.H.D., Soutis, C.: Delamination detection in composite laminates from variations of their modal characteristics. J. Sound Vib. 228, 1–9 (1999)CrossRefGoogle Scholar
  10. 10.
    Perez, M., Gil, L., Oller, S.: Impact damage identification in composite laminates using vibration testing. Compos. Struct. 108, 267–276 (2014)CrossRefGoogle Scholar
  11. 11.
    Mitrevski, T., Marshalla, H., Thomson, R., Jones, R., Whittingham, B.: The effect of impactor shape on the impact response of composite laminates. Compos. Struct. 67, 139–148 (2005)CrossRefGoogle Scholar
  12. 12.
    Mitrevski, T., Marshalla, H., Thomson, R.: The influence of impactor shape on the damage to composite laminates. Compos. Struct. 76, 116–122 (2006)CrossRefGoogle Scholar
  13. 13.
    Aymerich, F., Dore, F., Priolo, P.: Prediction of impact-induced delamination in cross-ply composite laminates using cohesive interface elements. Compos. Sci. Technol. 68, 2383–2390 (2007)CrossRefGoogle Scholar
  14. 14.
    Meola, C., Boccardi, S., Carlomagno, G.M., Boffa, N.D., Monaco, E., Ricci, F.: Nondestructive evaluation of carbon fibre reinforced composites with infrared thermography and ultrasonics. Compos. Struct. 134, 845–853 (2015)CrossRefGoogle Scholar
  15. 15.
    Dua, R., Watkins, S.E., Wunsch, D.C., Chandrashekhara, K., Akhavan, F.: Detection and classification of impact-induced damage in composite plates using neural networks. In: IEEE International Joint Conference on Neural Networks (2001)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Mechanical Engineering and ManufacturingUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  2. 2.School of Aerospace EngineeringUniversiti Sains MalaysiaNibong Tebal, Seberang Perai SelatanMalaysia
  3. 3.School of Mechanical EngineeringUniversiti Sains MalaysiaNibong Tebal, Seberang Perai SelatanMalaysia

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