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

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

Gul, F.C. (TU Delft Structural Integrity & Composites)
Moradi, M. (TU Delft Structural Integrity & Composites)
Benedictus, R. (TU Delft Structural Integrity & Composites)
HADJRIA, RAFIK (Safran Aircraft Engines)
LUGOVTSOVA, YEVGENIYA (BAM Federal Institute for Materials Research and Testing)
Zarouchas, D. (TU Delft Structural Integrity & Composites)

2023

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.

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

http://resolver.tudelft.nl/uuid:e0793ab9-18f3-4fd4-83f8-15734ea4eb52

https://doi.org/10.12783/shm2023/36863

DEStech publications, Inc., United States of America

2024-06-30

978-1-60595-693-0

Proceedings of the Fourteenth International Workshop on Structural Health Monitoring (IWSHM), 14

14th International Workshop on Structural Health Monitoring 2023: Designing SHM for Sustainability, Maintainability, and Reliability, 2023-09-12 → 2023-09-14, Stanford University, USA, Stanford, United States

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.

Institutional Repository

conference paper

© 2023 F.C. Gul, M. Moradi, R. Benedictus, RAFIK HADJRIA, YEVGENIYA LUGOVTSOVA, D. Zarouchas