Generation of Virtual Microstructures of Energetic Materials Based on Micro-computed Tomography Images Analysis

Abstract : Impact experiments were performed at the french-german research Institute of Saint-Louis on three energetic materials composed of 70 % in weight of RDX particles embedded in a wax matrix. These materials differ by the microstructural properties of the explosive particles. The experimental results reveal that the detonation thresholds, and so the sensitivity to shock, are different for each sample. To better understand these results, we characterize the microstructural properties of these compositions. The microstructures of the three materials are imaged with micro-computed tomography (µCT) at CEA Gramat. First, the 3D µCT images are filtered and segmented. Then, an analysis of the segmented volume is performed (granulometry, covariance and weight fraction). Each labeled grain is extracted and characterized by several descriptors such as volume, surface, angularity, sphericity or intra-granular porosity. Virtual microstructures are generated with the extracted grains. Each grain is precisely selected depending on its properties to generate a controlled microstructure. This allows to modify only one microstructural parameter at a time. The virtual microstructures are built to be consistent with the global analysis of the µCT segmented volumes. They respect both morphological granulometry and spatial covariance of the µCT segmented volumes. These numerical materials are intended to be used in mesoscale finite element simulations to study the effects of the microstructure on the shockwave propagation.
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Élodie Kaeshammer, Petr Dokladal, François Willot, Steve Belon, Lionel Borne. Generation of Virtual Microstructures of Energetic Materials Based on Micro-computed Tomography Images Analysis. 50th International Annual Conference of the Fraunhofer ICT, Jun 2019, Karlsruhe, Germany. ⟨hal-02139566⟩

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