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

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journal contribution
posted on 19.11.2019, 11:04 by Iuliana Tabian, Hailing FuHailing Fu, Zahra Sharif Khodaei
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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Sensors

Volume

19

Issue

22

Publisher

MDPI AG

Version

VoR (Version of Record)

Rights holder

© The Authors

Acceptance date

10/11/2019

Publication date

2019-11-12

Copyright date

2019

eISSN

1424-8220

Language

en

Depositor

Dr Hailing Fu. Deposit date: 18 November 2019

Article number

4933