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Analyse et visualisation de la géométrie des matériaux composites à partir de données d’imagerie 3D

Abstract : The subject of the thesis project between Laboratoire Roberval at Université de Technologie Compiègne and Center for High-Performance Composites at Ecole Polytechnique de Montréal considered the design of a deep learning architecture with semantics for automatic generation of models of composite materials microstructure based on X-ray microtomographic imagery. The thesis consists of three major parts. Firstly, the methods of microtomographic image processing are presented, with an emphasis on phase segmentation. Then, the geometric features of phase elements are extracted and used to classify and identify new morphologies. The method is presented for composites filled with short natural fibers. The classification approach is also demonstrated for the study of defects in composites, but with spatial features added to the process. A high-level descriptor "defect genome" is proposed, that permits comparison of the state o defects between specimens. The second part of the thesis introduces structural segmentation on the example of woven reinforcement in a composite. The method relies on dual kriging, calibrated by the segmentation error from learning algorithms. In the final part, a stochastic formulation of the kriging model is presented based on Gaussian Processes, and distribution of physical properties of a composite microstructure is retrieved, ready for numerical simulation of the manufacturing process or of mechanical behavior.
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Anna Madra. Analyse et visualisation de la géométrie des matériaux composites à partir de données d’imagerie 3D. Génie mécanique [physics.class-ph]. Université de Technologie de Compiègne, 2017. Français. ⟨NNT : 2017COMP2387⟩. ⟨tel-02008389⟩

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