Alignment of 3D woven textile composites towards their ideal configurations

Abstract

The objective of the present study is to create a parametric model of an ideal 3D woven textile from a computed tomography at mesoscale without prior knowledge of the fabric architecture. The model is constructed by identifying a minimal number of parameters from the tomography and includes further assumptions about the textile properties (e.g., equally-spaced vertical yarn columns). A novel registration procedure called Model-based Digital Image Correlation (MDIC) is introduced for mapping the whole textile image onto its own model. It leads to a realignment of the yarn columns after deforming the textile image, from which the model is updated. Model extraction and registration steps are iterated up to a stationary solution. The final result is a perfect textile geometry with straight and orthogonal yarn columns and its mapping onto the original tomography image. The proposed procedure is applied successfully to a 3D woven textile and a 3D-injected woven composite. This novel technique is useful as a pre-processing step to image segmentation procedures or to ease the visual inspection performed by operators in correcting the yarn paths and yarn column deformations occurring during composite material manufacturing. Additionally, this alignment procedure could be used to deform a numerical ideal model to better fit the geometry of a real weave.