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Automated Impact Damage Detection Technique for Composites Based on Thermographic Image Processing and Machine

Muflih Alhammad1, Nicolas P Avdelidis1,2, Clemente Ibarra-Castanedo2

  • 1School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

Sensors (Basel, Switzerland)
|December 11, 2022
PubMed
Summary
This summary is machine-generated.

This study used pulsed thermography (PT) and machine learning to detect impact damage in carbon fibre-reinforced plastic (CFRP) composite panels. The methods accurately identified defects, paving the way for automated structural health monitoring.

Keywords:
composite materialsdamage diagnosisimpact damageinfrared thermographymachine learningprincipal component thermographypulsed phase thermographysupport vector machinethermographic images

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Area of Science:

  • Materials Science
  • Aerospace Engineering
  • Non-Destructive Testing

Background:

  • Composite materials are crucial in transportation, especially aerospace.
  • Monitoring composite material health is vital for safety.
  • Existing damage detection methods require improvement for efficiency.

Purpose of the Study:

  • To evaluate pulsed thermography (PT) for detecting impact damage in composite materials.
  • To compare statistical analysis with machine learning for damage classification.
  • To develop an automated, non-destructive testing (NDT) technique for composite diagnostics.

Main Methods:

  • Pulsed thermography (PT) was used to collect thermal data from CFRP panels with varying thicknesses (1.6 and 3.8 mm).
  • Impact damage was induced with energies ranging from 4 to 12 J.
  • Statistical analysis and a Cube Support Vector Machine (SVM) algorithm were applied to thermal images for damage detection and classification.

Main Results:

  • Statistical analysis successfully detected damaged areas in most cases.
  • The Cube SVM algorithm achieved classification accuracies between 78.7% and 93.5%.
  • Machine learning demonstrated superior accuracy for damage classification compared to statistical methods.

Conclusions:

  • Pulsed thermography is effective for diagnosing impact damage in CFRP composites.
  • Machine learning, specifically Cube SVM, offers a highly accurate method for classifying composite damage.
  • The findings support the development of automated NDT systems for composite structural health monitoring.