Modeling as-manufactured fiber-reinforced microstructures based on X-ray microcomputed tomography

A new approach is presented to convert X-ray microcomputed tomography (μCT) image data of as-manufactured fiber-reinforced microstructures into high-fidelity finite element (FE) meshes. The mesh-generation approach leverages existing image segmentation and meshing tools to generate FE meshes of a wo...

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Veröffentlicht in:Composites science and technology 2021-09, Vol.214 (C), p.109004, Article 109004
Hauptverfasser: Creveling, Peter J., Fisher, John, Dahlkamp, Chris, Czabaj, Michael W.
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Sprache:eng
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Zusammenfassung:A new approach is presented to convert X-ray microcomputed tomography (μCT) image data of as-manufactured fiber-reinforced microstructures into high-fidelity finite element (FE) meshes. The mesh-generation approach leverages existing image segmentation and meshing tools to generate FE meshes of a woven-fabric and fiber-reinforced composite (FRCs), where constituents in each microstructure are represented explicitly. Segmentation of fibers from X-ray μCT images was performed using a template-matching/Kalman-filter estimation algorithm, and flaws were segmented using Trainable Weka Segmentation. Segmentation data were converted into reduced-order images, which were subsequently imported into Simpleware™ for mesh generation. To demonstrate advancements of the mesh-generation process, biaxial and uniaxial FE simulations were performed in Abaqus® for the woven- and FRC microstructures, respectively. Results reveal that the mesh-generation approach allows for modeling of distinct interactions between constituents, including contact/friction between fibers and interactions at the fiber-matrix interfaces. Results from this work demonstrate that X-ray μCT images of complex fiber-reinforced microstructures can be converted into high-fidelity meshes, but the high number of elements required for discretization necessitates additional work to improving mesh efficiency. [Display omitted]
ISSN:0266-3538
1879-1050
DOI:10.1016/j.compscitech.2021.109004