Damage Detection of Composite Plates by Lamb Wave Ultrasonic Tomography with a Sparse Hexagonal Network Using Damage Progression TrendsReport as inadecuate




Damage Detection of Composite Plates by Lamb Wave Ultrasonic Tomography with a Sparse Hexagonal Network Using Damage Progression Trends - Download this document for free, or read online. Document in PDF available to download.

Shock and Vibration - Volume 2014 2014, Article ID 949671, 8 pages -

Research Article

Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada V8W 3P6

Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli-Tuzla, 34956 Istanbul, Turkey

Received 24 July 2013; Accepted 15 November 2013; Published 4 March 2014

Academic Editor: Gyuhae Park

Copyright © 2014 C. J. Keulen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lamb wave based structural health monitoring shows a lot of potentialfor damage detection of composite structures. However, currently there is noagreement upon optimal network arrangement or detection algorithm. The objective of this research is to develop a sparse network that can be expandedto detect damage over a large area. To achieve this, a novel technique basedon damage progression history has been developed. This technique gives anamplification factor to data along actuator-sensor paths that show a steadyreduction in transmitted power as induced damage progresses and is implemented with the reconstruction algorithm for probabilistic inspection ofdamage RAPID technique. Two damage metrics are used with the algorithm and a comparison is made to the more commonly used signal difference coefficient SDC metric. Best case results show that damage is detected within 12 mm. The algorithm is also run on a more sparse network with no damage detection, therefore indicating that the selected arrangement is the most sparse arrangement with this configuration.





Author: C. J. Keulen, M. Yildiz, and A. Suleman

Source: https://www.hindawi.com/



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