Facundo Sosa-Rey, Yahya Abderrafai, Audrey Diouf Lewis, Daniel Therriault, Nicola Piccirelli et Martin Lévesque
Article de revue (2022)
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Abstract
From a modelling standpoint, the morphology of additively manufactured (AM) high-performance short fiber reinforced polymer (SFRP) is essential to characterize, yet this task poses great challenges. The method presented extracts individual fibers from tomographic scans and produces a segmentation that is 93.1% precise on average on a per-fiber basis across a large range of fiber filling ratios (5–40 wt.%), needs minimal human input and is scalable to full-sized datasets containing ∼ 10⁵ individual fibers. In addition, this tool allows the analysis of the correlated length and orientation distribution of fibers, and the quantification of shear-induced alignment and fiber breakage. The method is validated by successfully reproducing the segmentation of (continuous) fiber reinforced composites published in 2 separate studies and by predicting the fiber volume fraction and material density directly from the tomographic data of SFRPs. The output can serve as a basis for constituent-level mechanical modelling, and to gain insight into the relationship between processing parameters, morphology and mechanical behavior of SFRP. The full source code and imaging data are attached to this publication.
Mots clés
polymer-matrix composites (PMCs); porosity/voids; short-fibre composites; x-ray computed tomography; 3-D printing
Sujet(s): | 2100 Génie mécanique > 2100 Génie mécanique |
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Département: | Département de génie mécanique |
Centre de recherche: | LM2 - Laboratoire de Mécanique Multi-échelles |
Organismes subventionnaires: | Safran S.A. (FACMA Research Chair), NSERC / CRSNG |
Numéro de subvention: | CRDPJ514761 |
URL de PolyPublie: | https://publications.polymtl.ca/59635/ |
Titre de la revue: | Composites Science and Technology (vol. 226) |
Maison d'édition: | Elsevier |
DOI: | 10.1016/j.compscitech.2022.109497 |
URL officielle: | https://doi.org/10.1016/j.compscitech.2022.109497 |
Date du dépôt: | 08 nov. 2024 14:55 |
Dernière modification: | 09 nov. 2024 15:24 |
Citer en APA 7: | Sosa-Rey, F., Abderrafai, Y., Diouf Lewis, A., Therriault, D., Piccirelli, N., & Lévesque, M. (2022). OpenFiberSeg : open-source segmentation of individual fibers and porosity in tomographic scans of additively manufactured short fiber reinforced composites. Composites Science and Technology, 226, 109497 (13 pages). https://doi.org/10.1016/j.compscitech.2022.109497 |
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