Infrared Thermography Detection of Defects in CFRP Based on a Time-Domain Nonlinear Regression Algorithm

Abstract

Carbon fiber reinforced polymer (CFRP) has been extensively utilized in the aerospace industry due to their light weight and high strength, however, they are susceptible to defects such as delamination and debonding during service. To enhance material safety, reliability and defect detection efficiency in infrared non-destructive testing (NDT), this study treats each pixel in the thermal image of the specimen surface as an independent entity. Temporal thermal wave signal features are extracted, and after non-dimensional processing, the features are mapped back to each pixel to reconstruct the characteristic distribution on the specimen surface, leading to the proposal of the dynamic thermal regression (DTR) algorithm. The DTR technology, along with the dynamic thermal tomography (DTT) and thermal signal reconstruction (TSR) techniques, were applied to the original infrared image sequences. The results demonstrate that applying these image processing techniques significantly enhances defect detection in CFRP. Furthermore, the DTR technique effectively reduces the acquisition time for infrared NDT image sequences, shortens the sequence length, and thereby improves the efficiency of infrared NDT.