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Composite Laminate Delamination Detection Using Transient Thermal Conduction Profiles and Machine Learning Based Data

David I Gillespie1,2, Andrew W Hamilton3, Robert C Atkinson1

  • 1Department of Electronic and Electrical Engineering, Royal College Building, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK.

Sensors (Basel, Switzerland)
|December 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel thermography method for detecting delaminations in Carbon Fibre Reinforced Polymer (CFRP) composites. The technique accurately identifies internal defects using transient thermal conduction profiles and a Support Vector Classification algorithm.

Keywords:
aerospacecompositedelaminationinspectionmachine learningmaintenance repair overhaulnon destructive inspectionquality assurancere-manufacturesupport vector classificationthermography

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Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Aerospace Engineering

Background:

  • Delaminations in aerospace composites lack visible surface indicators, posing a significant safety concern.
  • Conventional thermography can detect large disbonds but struggles with inter-ply delaminations in Carbon Fibre Reinforced Polymer (CFRP) structures.
  • Existing methods require improvements for early-stage detection of subtle internal defects.

Purpose of the Study:

  • To investigate the impact of inter-laminate delaminations on the transient thermal conduction profile of multi-ply bi-axial CFRP laminates.
  • To develop and validate a thermography-based method for detecting internal delaminations in CFRP composites.
  • To assess the effectiveness of a Support Vector Classification algorithm in identifying these defects.

Main Methods:

  • Zonal heating of CFRP laminates to induce transient thermal conduction profiles.
  • Utilizing contact temperature sensors to record thermal data during a 140-second heating period to 80 °C.
  • Applying a supervised Support Vector Classification (SVC) algorithm to temperature data for defect detection.

Main Results:

  • Transient thermal conduction profiles differ measurably when delaminations are present, even at varying distances from the heat source.
  • The SVC algorithm achieved over 99% accuracy in classifying delaminations versus non-delaminations.
  • High accuracy was maintained for both training and independent test data sets.

Conclusions:

  • The developed thermography technique effectively detects inter-ply delaminations in CFRP composites.
  • Transient thermal conduction analysis combined with SVC offers a highly accurate non-destructive method for composite integrity assessment.
  • This approach enhances the safety and reliability of aerospace composite structures.