Depth prediction of GFRP composite using long pulse thermography

Abstract

Infrared thermography is a useful technique for detecting defects within composites and estimating their sizes and depths. Previous studies have reported various depth prediction methods, primarily focusing on pulse thermography or lock-in thermography. The core innovation of this study is to explore the applicability of four representative methods including peak contrast time (PCT), peak slope time (PST), log second derivative (LSD) and absolute peak slope time (APST) for defect depth prediction using long pulse thermography. The 1D thermal conduction of square-pulsed heating was analyzed using numerical method to establish the linear relationship between the specific characteristic time and defect depth for these methods. The depths of the flat-bottom holes were predicted using experimental cooling data obtained from glass fiber reinforced plastics panels. Based on the experimental results, the PST, LSD and APST methods exhibited promise for use in defect depth prediction with average measurement errors of 5.77%, 8.96% and 6.25%.