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A convolutional neural network for impact detection and characterization of complex composite structures

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Title: A convolutional neural network for impact detection and characterization of complex composite structures
Authors: Iuliana, T
Fu, H
Sharif Khodaei, Z
Item Type: Journal Article
Abstract: This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts.
Issue Date: 12-Nov-2019
Date of Acceptance: 10-Nov-2019
URI: http://hdl.handle.net/10044/1/75240
DOI: 10.3390/s19224933
ISSN: 1424-8220
Publisher: MDPI AG
Journal / Book Title: Sensors
Volume: 19
Issue: 22
Copyright Statement: ©2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: structural health monitoring (SHM), convolutional neural network (CNN), deep-learning, passive sensing, impact detection, impact characterization, composite structures.
0502 Environmental Science and Management
0602 Ecology
0301 Analytical Chemistry
0906 Electrical and Electronic Engineering
Analytical Chemistry
Publication Status: Published
Article Number: ARTN 4933
Appears in Collections:Aeronautics