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A convolutional neural network for impact detection and characterization of complex composite structures
File | Description | Size | Format | |
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sensors-19-04933-v2.pdf | Published version | 4.91 MB | Adobe PDF | View/Open |
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 |