A multivariate statistical approach to wrinkling detection in composites.

Abstract

Non-destructive inspection using ultrasound in materials such as carbon-fibre reinforced polymers (CFRP) is challenging as the ultrasonic wave will scatter from each ply in the structure of the component. This may be improved by using image processing algorithms such as the total focusing method (TFM), however the high level of backscattering within the sample is very likely to obscure a signal arising from a flaw. Detection of wrinkling, or out-of-plane fibre waviness, is especially difficult to automate as no additional scattering is produced (as might be the case with delaminations). Instead, wrinkling changes how a signal is scattered due to the physical displacement of ply layers from their expected location. In this paper, we propose a method of detecting wrinkling by examining the regional variations in image intensity, which are expected to be highly correlated between similar ply layers in the structure. By characterising the 2-dimensional spatial autocorrelation of an area surrounding a given location in the image of pristine components, the distribution of acceptable values is estimated. Wrinkling is observed to correspond with a significant deviation from this distribution, which is readily detected. A comparison is made with an alternative image processing approach identified from the literature, finding that the proposed method has equivalent performance for large wrinkling amplitudes, and better performance for low wrinkling amplitudes.