Abstract
The increased use of composite materials in engineering applications, and their susceptibility to damage means that it’s imperative that robust testing technique are developed to help in the detection of damage. Many of the detection techniques currently available are highly complex, difficult to conduct and rely on human interpretation of data. Simple testing methods are available but are too unreliable to be used effectively. This investigation explores the development of simple testing methods which use classification and novelty detection methods to detect the presence of damage in composite materials, making the process of damage detection much quicker, simpler and more versatile.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hsu, D.K.: Nondestructive Testing of Composite Structures. In: 17th World Conference on Nondestructive Testing
Bray, D.E., Stanley, R.K.: Nondestructive Evaluation – A Tool inDesign, Manufacturing, and Service. CRC Press (1997)
Halmshaw, R.: Non-Destructive Testing. Butterworth-Heinemann (1991)
Falk, J.P., Steck, J.E., Smith, B.L.: A Nondestructive Testing Technique for Composite Panels Using Tap Test Acoustic Signals and Artificial Neural Networks. Journal of Smart Engineering System Design 5(4), 491–506 (2003)
Amali, R., Hughes, B.J.: Development of a novel NDT technique for damage classifications of composites. In: FRP Bridges Conference 2012, pp. 89–100. NetComposites, London (2012)
Smith, S.W.: Digital Signal Processing. Newnes (2003)
Worden, K.: Structural Fault Detection Using a Novelty Measure. Journal of Sound and Vibration 201, 85–101 (1997)
Farrar, C.R., Worden, K.: An Introduction to Structural Health Monitoring. Phil. Trans. R. Soc. 303, 303–315 (2007)
Giurgiutiu, V., Soutis, C.: Enhanced Composites Integrity Through Structural Health Monitoring. Applied Composite Materials 19, 813–829 (2012)
Worden, K., Manson, G.: The Application of Machine Learning to Structural Health Monitoring. Phil. Trans. R. Soc. 365, 515–537 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amali, R., Hughes, B.J. (2013). Detection of Damage in Composite Materials Using Classification and Novelty Detection Methods. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-41013-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41012-3
Online ISBN: 978-3-642-41013-0
eBook Packages: Computer ScienceComputer Science (R0)