Variational segmentation of textile composite preforms from X-ray computed tomography

Abstract

Prediction of the thermo-mechanical behavior of woven composites necessitates a reliable knowledge of their inner structure. A sufficiently accurate description of the fabric geometry could be obtained using X-ray computed microtomography (μCT) at the mesoscopic scale. However, systematic construction of numerical models from μCT remains a difficult task. To address this challenge, we propose a variational segmentation approach that combines μCT with a prior geometric model that is iteratively improved thanks to a heuristic optimization process. The fidelity of the models with respect to the input μCT is evaluated using a measure of similarity including both gray levels and local directions. Our method allowed to build realistic numerical models of woven fabrics that preserve the prescribed weaving pattern, and that are free of interpenetration, which makes them compatible with further numerical simulations. Using our approach, models of complex woven fabrics, but also of woven composites, could be consistently generated from μCT and can serve as reference models, e.g. to analyze in situ tests by providing digital twins.