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Delamination Depth Detection in Composite Plates Using the Lamb Wave Technique Based on Convolutional Neural

Asaad Migot1, Ahmed Saaudi2, Victor Giurgiutiu3

  • 1Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary

This study introduces an automated system using convolutional neural networks (CNNs) and Lamb waves to detect delamination depth in composite plates. The system accurately identifies damage, enhancing structural health monitoring capabilities.

Keywords:
CNNGoogLeNetLamb wavesSLDVcompositesdelaminationwavefield imageswavenumber spectrum

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Computer Science

Background:

  • Delamination is a critical failure mode in composite plates, posing significant safety risks.
  • Structural Health Monitoring (SHM) techniques are vital for detecting delamination, but automation remains a challenge.
  • Convolutional Neural Networks (CNNs) show promise for automating Non-Destructive Testing (NDT) data analysis.

Purpose of the Study:

  • To develop an automated system for distinguishing pristine from damaged composite structures.
  • To classify delamination based on depth using CNNs and Lamb wave techniques.
  • To investigate the influence of delamination depth on guided wave propagation.

Main Methods:

  • A proposed CNN model integrated with the Lamb wave technique.
  • Numerical simulations and experimental validation using piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV).
  • Utilized three datasets: numerical wavefield images, experimental wavefield images, and experimental wavenumber spectrum images.

Main Results:

  • Both numerical and experimental studies confirmed that delamination depth directly impacts trapped wave energy and distribution.
  • The proposed CNN model accurately classified four classes (pristine and three delamination depths) across all three datasets.
  • Validation against GoogLeNet CNN showed excellent agreement, confirming the model's predictive capability.

Conclusions:

  • The developed automated system effectively detects and classifies delamination depth in composite plates.
  • Wavefield and wavenumber spectrum images are suitable input data for CNNs in delamination detection.
  • This research advances automated NDT for composite structural health monitoring.