Statistical and Machine Learning-Based Imaging with Long Pulse Thermography for the Detection of Non-standardised Defects in CFRP Composites

Abstract

In the last few years, infrared long pulse thermography (LPT) has attained high reliability and accuracy in the non-destructive inspection of low-thermally conductive materials such as carbon fibre reinforced polymer (CFRP) composites. However, to date, research investigations of LPT have been conducted on standardised and controlled material flaws such as flat bottom holes. Non-standardised defects in CFRPs are more common in real-life operations and, because of different nature, dimensions and complex shapes, their detection poses a significant challenge. This paper provides an in-depth analysis of LPT combined to advanced statistical and machine learning-based image processing tools for detection of non-standardised damage in CFRP composites. Statistical methods such as skewness and kurtosis, and machine learning algorithms such as principal component analysis and Fuzzy-c clustering were used to post-process thermal LPT signals. Damage scenarios that are likely to occur during manufacturing and in-service operations were analysed in terms of defect mapping characteristics using the signal-to-noise ratio and the Tanimoto criterion. Experimental results revealed that Fuzzy-c and LPT produced superior damage inspection performance.