Defect Detection of GFRP Composites through Long Pulse Thermography Using an Uncooled Microbolometer Infrared Camera

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-08, Vol.24 (16), p.5225
• Main Authors: Anwar, Murniwati, Mustapha, Faizal, Abdullah, Mohd Na'im, Mustapha, Mazli, Sallih, Nabihah, Ahmad, Azlan, Mat Daud, Siti Zubaidah
• Format: Article
• Language: English
• Subjects: Accuracy; Cameras; Composite materials; defect; Defects; GFRP; impact; Indium antimonide; IR camera; long pulse thermography (LPT); Mercury cadmium telluride; Methods; Radiation; Thermography; uncooled microbolometer
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24165225

The detection of impact and depth defects in Glass Fiber Reinforced Polymer (GFRP) composites has been extensively studied to develop effective, reliable, and cost-efficient assessment methods through various Non-Destructive Testing (NDT) techniques. Challenges in detecting these defects arise from varying responses based on the geometrical shape, thickness, and defect types. Long Pulse Thermography (LPT), utilizing an uncooled microbolometer and a low-resolution infrared (IR) camera, presents a promising solution for detecting both depth and impact defects in GFRP materials with a single setup and minimal tools at an economical cost. Despite its potential, the application of LPT has been limited due to susceptibility to noise from environmental radiation and reflections, leading to blurry images. This study focuses on optimizing LPT parameters to achieve accurate defect detection. Specifically, we investigated 11 flat-bottom hole (FBH) depth defects and impact defects ranging from 8 J to 15 J in GFRP materials. The key parameters examined include the environmental temperature, background reflection, background color reflection, and surface emissivity. Additionally, we employed image processing techniques to classify composite defects and automatically highlight defective areas. The Tanimoto Criterion (TC) was used to evaluate the accuracy of LPT both for raw images and post-processed images. The results demonstrate that through parameter optimization, the depth defects in GFRP materials were successfully detected. The TC success rate reached 0.91 for detecting FBH depth defects in raw images, which improved significantly after post-processing using Canny edge detection and Hough circle detection algorithms. This study underscores the potential of optimized LPT as a cost-effective and reliable method for detecting defects in GFRP composites.