Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Federal Aviation Administration (FAA): Aviation Maintenance Technician Handbook, vol. 1. Aviation Supplies & Academics (ASA), Oklahoma (2012)
Perez, M., Gil, L., Oller, S.: Impact damage identification in composite laminates using vibration testing. Compos. Struct. 108, 267–276 (2014)
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)
Adams, R.D., Cawley, P.: Defect types and non-destructive testing techniques for composites and bonded joints. Constr. Build. Mater. 3, 170–183 (1989)
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)
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)
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)
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)
Valdes, S.H.D., Soutis, C.: Delamination detection in composite laminates from variations of their modal characteristics. J. Sound Vib. 228, 1–9 (1999)
Perez, M., Gil, L., Oller, S.: Impact damage identification in composite laminates using vibration testing. Compos. Struct. 108, 267–276 (2014)
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)
Mitrevski, T., Marshalla, H., Thomson, R.: The influence of impactor shape on the damage to composite laminates. Compos. Struct. 76, 116–122 (2006)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mahmod, M.F. et al. (2019). Artificial Neural Network Application for Damages Classification in Fibreglass Pre-impregnated Laminated Composites (FGLC) from Ultrasonic Signal. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_72
Download citation
DOI: https://doi.org/10.1007/978-981-13-6447-1_72
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6446-4
Online ISBN: 978-981-13-6447-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)