Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring

F.C. Gul*, M. Moradi, R. Benedictus, RAFIK HADJRIA, YEVGENIYA LUGOVTSOVA, D. Zarouchas

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time. Ultrasonic Guided Waves (UGW) have a strong capability to interrogate and monitor the structure in real-time and thus track the growth of damage, which may occur during the flight cycles. Although various types of damage affect the monitored UGW signals, it is challenging to determine from the UGW signals what types of damage and at what rate of growth are occurring within the structure. UGW signals can be acquired at defined intervals and then analysed to possibly detect different types of damages, such as delamination, and to quantify the rate of damage growth over fatigue cycles. However, correlating the UGW-based Damage Indicators (DIs) with the specific type of damage, such as delamination, and damage growth is a challenging task as the relation between these DIs and the actual damage state is very complex. Therefore, in this study, a supervised Deep Neural Network-based (DNN) prediction model is proposed aiming to diagnose the delamination size of the composite structure by correlating the UGW-based DIs with the quantified time-varying delamination size. UGW data is collected through a network of permanently installed piezoelectric transducers (PZTs). The delamination size is obtained through ultrasonic C-Scan technique at defined cycles. DIs are extracted in time, frequency, and time-frequency domains and used as the input for the DNN-based regression model. Each sensor-actuator path is considered as an independent set of indicators, which are separated for training, validation, and testing purposes. The effect of the different paths on the delamination size prediction is presented along with the model performance on measured delamination growth in woven type composite sample.
Original languageEnglish
Title of host publicationProceedings of the Fourteenth International Workshop on Structural Health Monitoring (IWSHM)
Place of PublicationUnited States of America
PublisherDEStech publications, Inc.
Pages1217-1224
Number of pages8
Volume14
ISBN (Electronic)978-1-60595-693-0
DOIs
Publication statusPublished - 2023
Event14th International Workshop on Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability - Stanford University, USA, Stanford, United States
Duration: 12 Sept 202314 Sept 2023
https://iwshm2023.stanford.edu/

Conference

Conference14th International Workshop on Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability
Abbreviated title14th IWSHM 2023
Country/TerritoryUnited States
CityStanford
Period12/09/2314/09/23
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Ultrasonic guided waves
  • Damage Indicators
  • piezoelectric transducers
  • PZT
  • Deep neural network
  • Structural health monitoring (SHM)
  • Carbon fiber reinforced polymer (CFRP)
  • Compression after impact
  • Wavelet transform (WT)
  • Signal Processing

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