Neural networks for delamination flaw detection in FRP laminated composite plates

Abhinandan A. Bage; Pradipta Banerji

doi:10.1117/12.599810

9 May 2005 Neural networks for delamination flaw detection in FRP laminated composite plates

Abhinandan A. Bage, Pradipta Banerji

Proceedings Volume 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV; (2005) https://doi.org/10.1117/12.599810
Event: Nondestructive Evaluation for Health Monitoring and Diagnostics, 2005, San Diego, CA, United States

Abstract

In this study, the detection of delamination flaws in laminated composite plates is carried out using artificial neural networks (ANN) in a two-level cascading manner. The three damage parameters detected using ANN are the size of the delamination, its vertical location (across the plate thickness) and horizontal location (along the plate surface). The numerical data in the form of frequency domain Green's function for the displacement response on the surface of the plate containing the delamination flaw is generated first using an available numerical method. Pseudo-experimental data is generated adding artificial random noise into the numerical data. At the first level, a counterpropagation neural network (CPN) is trained for qualitatively classifying the damage parameters using the numerical data generated above. Next, a second level back-propagation network (BPN) is used for each subclass to quantify the damage parameters. An overlapping data set is used for the training of each class of the second level network. As a result, any pattern misclassified by the CPN due to its closeness to the boundary of any two classes is still quantified correctly. By feeding pseudo-experimental data to the trained networks, it is seen that the classification success rate and noise tolerance level of CPN is excellent. The quantification of damage by the second level BPN is also good. It is possible to stop after the first level if only a qualitative assessment of the damage and its approximate location is required. These cascaded networks show promise in providing a successful delamination damage detection tool.

Citation Download Citation

Abhinandan A. Bage and Pradipta Banerji "Neural networks for delamination flaw detection in FRP laminated composite plates", Proc. SPIE 5768, Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV, (9 May 2005); https://doi.org/10.1117/12.599810

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Composites

Neurons

Damage detection

Neural networks

Interference (communication)

Artificial neural networks

Interfaces

Show All Keywords

Keywords/Phrases

Search In:

Publication Years