Facundo Sosa-Rey, Yahya Abderrafai, Audrey Diouf Lewis, Daniel Therriault, Nicola Piccirelli, Martin Lévesque
Article (2022)
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Restricted to: Repository staff only until 20 May 2024 Accepted Version Terms of Use: Creative Commons Attribution Non-commercial No Derivatives Request a copy |
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Abstract
From a modelling standpoint, the morphology of additively manufactured (AM) high-performance short fiber reinforced polymer (SFRP) are essential to characterize, yet this task poses great challenges. The method presented extracts individual fibers from tomographic scans and produces a segmentation that is accurate 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.
Uncontrolled Keywords
Polymer-matrix composites (PMCs); Porosity/Voids; Short-fibre composites; X-ray computed tomography; 3-D Printing
Subjects: |
1700 Design and manufacturing > 1702 Advanced manufacturing 2000 Materials science and technology > 2001 Materials structure, properties and testing 2000 Materials science and technology > 2007 Composites 2100 Mechanical engineering > 2100 Mechanical engineering |
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Department: | Department of Mechanical Engineering |
Research Center: | LM2 - Laboratory for Multi-scale Mechanics |
Funders: | Safran S.A. (FACMO Research Chair), CRSNG/NSERC |
Grant number: | CRDPJ514761 |
PolyPublie URL: | https://publications.polymtl.ca/10435/ |
Journal Title: | Composites Science and Technology (vol. 226) |
Publisher: | Elsevier |
DOI: | 10.1016/j.compscitech.2022.109497 |
Official URL: | https://doi.org/10.1016/j.compscitech.2022.109497 |
Date Deposited: | 23 Aug 2022 10:06 |
Last Modified: | 19 May 2023 18:02 |
Cite in 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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