Diffuse manifold learning of the geometry of woven reinforcements in composites

Abstract

When attempting to build mesoscale geometric models of woven reinforcements in composites based on X-ray microtomography data, we frequently run into ambiguous situations due to noise, particularly in contact zones between fiber tows, resulting in inadmissible cross-sectional shapes. We propose here a custom-built shape-manifold approach based on kernel PCA, k-means classification and Diffuse Approximation to identify, “repair” such badly segmented shapes in the feature space, and finally recover admissible shapes in the original space.