Investigation of the Discrepancy Between Optically and Gravimetrically Calculated Fiber Volume Fraction in Flax-Fiber-Reinforced Polymer

  • The fiber volume fraction significantly influences the mechanical properties of fiber-reinforced composites. However, accurate measurements can be particularly challenging in natural-fiber-reinforced polymers. This study compared indirect methods using gravimetric and volumetric measurements with a U-Net-based direct method using micro-CT images for flax-fiber-reinforced polymers made via compression molding at 2.33–13.5 bar. A notable discrepancy was observed between the direct and indirect methods, with the latter yielding a fiber volume fraction approximately 25% lower than what could be determined optically. This difference arose from the matrix being absorbed by the fibers, resulting in a mixed region between dry fiber and pure matrix, further explained using a four-phase model. Our findings indicate that the volume fraction depended on the applied pressure. Specifically, we established a linear relationship between the fiber volume fraction and the pressure up to 9.4 bar, beyond which the fiber volume fraction plateaued. Furthermore, we examined the impact of void distribution in relation to pressure. At lower pressures, voids were distributed irregularly throughout the composite, whereas at higher pressures, the overall number of voids decreased, and they tended to concentrate primarily in the center.

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Author:Christoph MaierORCiD, Alexander BeckmannORCiD, Armin WittmannORCiD, Klaus P. KochORCiD, Georg FischerORCiD
URN:urn:nbn:de:hbz:tr5-10433
DOI:https://doi.org/10.3390/jcs9030103
Parent Title (English):Journal of Composites Science
Document Type:Article (specialist journals)
Language:English
Date of OPUS upload:2025/06/04
Date of first Publication:2025/02/24
Publishing University:Hochschule Trier
Release Date:2025/06/05
Tag:convolutional neural network; fiber volume fraction; flax fiber; natural-fiber-reinforced polymer
GND Keyword:Verbundwerkstoff; Faserverbundwerkstoff; Flachsfaser; Volumen; Druck; Convolutional Neural Network
Volume:9
Issue:3
Article Number:103
First Page:1
Last Page:25
Departments:FB Technik
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International

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