Geometric Characterisation of Stochastic Fibrous Networks: A Comprehensive Review
Abstract
:Highlights
- This study provides a comprehensive review of the geometrical characterization of stochastic fibrous networks, combining results from the literature with original research findings.
- This review focuses on the main parameters used to describe such networks, including basis weight, orientation distribution function, crimp, porosity, spatial distribution of fibres, and fibre intersections.
Abstract
1. Introduction
2. Regular Versus Random Microstructures
3. Geometric Characterisation Parameters
3.1. Basis Weight
3.2. Orientation Distribution Function
3.3. Crimp
3.4. Spatial Distribution of Fibres
- The uniformity index based on grey-value images developed by Amirnasr et al. has challenges with consistency in the medium range of the index.
- Similar to the uniformity index developed by Amirnasr et al., Chhabra’s method, i.e., the standardised index, is also inconsistent in the medium range.
- On the other hand, the uniformity index developed by Pourdeyhimi and Kohel has a resolution problem; for example, it cannot differentiate between highly uniform fibrous networks.
- The quadrant-based coefficient of variation developed by Tascan and Nohut is the most reliable method compared with the other alternatives owing to its consistently high resolution and robustness, which prevent errors generated due to the influence of basis weight, thickness, and fibre-material-related parameters.
3.5. Fibre Diameter
3.6. Porosity and Pore Size Distribution
3.7. Fibre-to-Fibre Interactions
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods | Maturity | Accuracy | Robustness | Efficiency |
---|---|---|---|---|
Coefficient of variation () | ++++ | + | ++ | ++ |
Quadrant | ++++ | ++ | ++ | ++++ |
Fourier power spectrum | + | ++ | + | ++ |
Co-occurrence matrix | ++ | ++ | ++ | + |
Fractal dimension | ++ | ++ | ++ | +++ |
Reference | Study | Method |
---|---|---|
[112] | Distribution of capillary size | Theoretical |
[113] | Distributions of pore area, perimeter, hydraulic radii | Theoretical |
[114] | Distribution of pore size derived from basic manufacturing parameters | Theoretical |
[115] | Assessment of filtration and opening size | Theoretical |
[116] | Distribution of pore size for geotextiles with bubble point method | Experimental |
[117] | Relationship between structure of nonwoven filter and its permeability and opening size | Semi-empirical |
[118] | Distribution of pore radius in paper | Image processing |
[119] | Distribution of pore size in nonwovens and effects of fibre and fabric parameters on pore size | Theoretical |
[120] | Effect of microstructure on porosity of random fibrous networks | Image processing |
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Kayali, Y.; Gleadall, A.; Silberschmidt, V.V. Geometric Characterisation of Stochastic Fibrous Networks: A Comprehensive Review. Fibers 2025, 13, 27. https://doi.org/10.3390/fib13030027
Kayali Y, Gleadall A, Silberschmidt VV. Geometric Characterisation of Stochastic Fibrous Networks: A Comprehensive Review. Fibers. 2025; 13(3):27. https://doi.org/10.3390/fib13030027
Chicago/Turabian StyleKayali, Yagiz, Andrew Gleadall, and Vadim V. Silberschmidt. 2025. "Geometric Characterisation of Stochastic Fibrous Networks: A Comprehensive Review" Fibers 13, no. 3: 27. https://doi.org/10.3390/fib13030027
APA StyleKayali, Y., Gleadall, A., & Silberschmidt, V. V. (2025). Geometric Characterisation of Stochastic Fibrous Networks: A Comprehensive Review. Fibers, 13(3), 27. https://doi.org/10.3390/fib13030027