Comptes Rendus
Model reduction, data-based and advanced discretization in computational mechanics
Diffuse manifold learning of the geometry of woven reinforcements in composites
Comptes Rendus. Mécanique, Volume 346 (2018) no. 7, pp. 532-538.

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.

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DOI: 10.1016/j.crme.2018.04.008
Keywords: Model reduction, Diffuse Approximation, Composites, X-ray microtomography

Anna Madra 1; Piotr Breitkopf 2; Balaji Raghavan 3; François Trochu 4

1 Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 Laboratoire Roberval, UMR 7337, UTC–CNRS, Université de technologie de Compiègne, Centre de recherches de Royallieu, CS 60319, 60203 Compiègne cedex, France
3 Laboratoire de génie civil et génie mécanique, EA 3913, INSA Rennes, 35708 Rennes, France
4 Chair on Composites of High Performance (CCHP), Mechanical Engineering Department, Center of Research on Polymers and Composites (CREPEC), École polytechnique de Montréal, Montréal H3C 3A7, Canada
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Anna Madra; Piotr Breitkopf; Balaji Raghavan; François Trochu. Diffuse manifold learning of the geometry of woven reinforcements in composites. Comptes Rendus. Mécanique, Volume 346 (2018) no. 7, pp. 532-538. doi : 10.1016/j.crme.2018.04.008. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2018.04.008/

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