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Sub-Surface Defect Depth Approximation in Cold Infrared Thermography.

Siavash Doshvarpassand1,2, Xiangyu Wang1,3

  • 1Australasian Joint Research Centre for Building Information Modelling, Curtin University, Bentley, WA 6102, Australia.

Sensors (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study demonstrates cold infrared thermography for detecting hidden corrosion in metallic structures. It accurately characterizes defect dimensions, showing potential for predicting significant metal loss with simple equipment.

Keywords:
cold infrared thermographydefect depth predictionmetal loss defect characterisationnon-destructive testingstructural health monitoringvision-based sensors

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Area of Science:

  • Materials Science
  • Non-Destructive Testing
  • Infrared Thermography

Background:

  • Hidden corrosion detection is critical for industrial plant safety and structural integrity.
  • Infrared (IR) thermography is a proven method for inspecting sub-surface anomalies in metallic materials.
  • Active thermography, using external excitation, is the basis for IR-based defect detection.

Purpose of the Study:

  • To explore the characterization of defect dimensions (depth and diameter) using cold infrared thermography.
  • To develop and validate a method for approximating defect sizes based on thermal contrast.
  • To assess the feasibility of using a non-radiometric thermal device for corrosion characterization.

Main Methods:

  • Utilized cold thermography as an alternative to traditional heating-based active thermography.
  • Developed a simple analytical model for thermal contrast over defects.
  • Employed Dynamic Time Warping (DTW) to measure time-series similarity between model and experimental data.
  • Conducted experiments to validate the analytical model and DTW approach.

Main Results:

  • Successfully approximated defect depth and diameter by comparing experimental and model thermal contrast time-series.
  • Demonstrated the potential to predict metal loss up to 50% in mid-thickness substrates.
  • Achieved good prediction accuracy even with a less accurate, non-radiometric thermal device and without advanced image processing.

Conclusions:

  • Cold infrared thermography offers a viable approach for characterizing hidden corrosion dimensions.
  • The proposed analytical model and DTW similarity measurement are effective for defect characterization.
  • This method shows promise for reliable, cost-effective structural health monitoring in industrial applications.