A Multivariate Statistical Approach to Wrinkling Detection in Composites

Nondestructive inspection using ultrasound in materials such as carbon-fiber reinforced polymers (CFRPs) is challenging as the ultrasonic wave will scatter from each ply in the structure of the component. This may be improved 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 fiber 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 article, 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 characterizing the 2-D 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.