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<p>This study investigates the impact of subsurface defects on surface temperature distributions through Pulsed Infrared Thermography (PIRT), employing a connection between MATLABl software and COMSOL Multiphysics for the simulation of 300 distinct defect depths, culminating in the analysis of 450,000 thermal images. The Hough Circle Transform (HCT) was applied to a specific time frame from the infrared video sequence, where defect signatures were pronounced, to precisely localize defect regions. For defect quantification, a normalization method was implemented, calculating the ratio of the thermal norm within defect regions to that of non-defective areas. A Long Short-Term Memory (LSTM) network was then trained to map the temporal evolution of this norm ratio to a specific defect detection time. A Photopolymer Resin sample, fabricated with varying depths of defects via Stereolithography Apparatus (SLA) 3D printing, underwent Non-destructive testing (NDT) using Pulsed Infrared Thermography (PIRT). An integrated algorithm was employed for the identification of defect regions and the subsequent calculation of the times at which defects were identified. The depths of unknown defects were predicted employing a least square fitted curve, derived from four samples with known defect depths. This methodology achieved a notable degree of precision. This research delineates a relationship between the depth of a defect and its detection time, introducing an innovative approach for the accurate prediction of defect depths within the realm of Non-destructive testing (NDT).</p>

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A Novel Approach for Subsurface Defect Depth Prediction Based on Detection Time in Pulsed Infrared Thermography

  • 期刊论文

  • 发表日期：

    2025年11月15日

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  • Yinuo Ding,
  • Stefano Sfarra,
  • Rubén Usamentiaga,
  • Hai Zhang

摘要

This study investigates the impact of subsurface defects on surface temperature distributions through Pulsed Infrared Thermography (PIRT), employing a connection between MATLABl software and COMSOL Multiphysics for the simulation of 300 distinct defect depths, culminating in the analysis of 450,000 thermal images. The Hough Circle Transform (HCT) was applied to a specific time frame from the infrared video sequence, where defect signatures were pronounced, to precisely localize defect regions. For defect quantification, a normalization method was implemented, calculating the ratio of the thermal norm within defect regions to that of non-defective areas. A Long Short-Term Memory (LSTM) network was then trained to map the temporal evolution of this norm ratio to a specific defect detection time. A Photopolymer Resin sample, fabricated with varying depths of defects via Stereolithography Apparatus (SLA) 3D printing, underwent Non-destructive testing (NDT) using Pulsed Infrared Thermography (PIRT). An integrated algorithm was employed for the identification of defect regions and the subsequent calculation of the times at which defects were identified. The depths of unknown defects were predicted employing a least square fitted curve, derived from four samples with known defect depths. This methodology achieved a notable degree of precision. This research delineates a relationship between the depth of a defect and its detection time, introducing an innovative approach for the accurate prediction of defect depths within the realm of Non-destructive testing (NDT).