Automated crack detection in laminated composites by optical flow measurements

Abstract

In this paper, an automated crack detection method for laminated composites of carbon fiber reinforced polymers (CFRP) is presented. Image correlation is performed on micrographs of the unspeckled specimen edge using DeepFlow, an algorithm for optical flow estimation. Cracks are then detected from local maxima of the displacement gradient evaluated along the midline of the plies. The detection strategy is applied to 3 different materials, with increasingly irregular microstructures, and on quasi-isotropic layups (a challenging case for crack detection). The crack density evolution in the 90∘ and ±45∘ plies is presented. The proposed method proves its capacity to detect cracks, even in complex configurations where other methods fail, and can provide an extensive amount of data that are useful for the identification of damage models for laminates. The procedure is largely automated in order to make it suitable for industrial applications.