The transverse permeability of roving/tow-based fiber reinforcement is of great importance for accurate flow modeling in the pultrusion process. This study proposes an experimental approach to characterize the roving-based fiber beds' permeability under different compaction conditions. The experimental permeability results of thick roving-based preforms were reported and compared with the permeability values of roving-based preforms in the literature. A representative preform was infused under vacuum conditions. Its thickness was varied to replicate the different compaction values observed in permeability tests. Micrographs were then collected from it and analyzed to highlight the microscale transformations caused by processing/compaction on the fiber arrangement. The analysis revealed that compaction resulted in the reorganization of filaments along the direction of the applied compaction. Overall, the uniformity of the spatial filament distribution, i.e., the homogeneity within the fibrous domain, increased with increasing compaction. Furthermore, the microstructural analysis demonstrated transverse anisotropy within the tested domains, indicating that the obtained permeability results represented an upper boundary. In addition to the experimental analyses, various transverse permeability models, which were developed based on recently introduced statistical descriptors of fiber distribution, were evaluated by using the statistical descriptors extracted from the analyzed cross-sections. Among these models, the one correlating the second neighbor fiber distance with apparent permeability exhibited good agreement with the experimental results. Highlights: Transverse permeability measurement of a roving-based reinforcement was presented. The influence of compaction on the microstructure was investigated at the filament level. Filament distribution in a pultruded profile was analyzed by using statistical descriptors. The results of the experiments and the models in the literature were compared. The correlation between microstructural features and apparent permeability was discussed.

Three different J-integral formulations to derive the experimental translaminar toughness of composites from compact tension tests with a large-scale fracture process zone are implemented and discussed. They improve the existing approaches by taking advantage of stereo-digital image correlation to acquire full-field displacement fields. A field fitting procedure based on robust and efficient piecewise cubic smooth splines addresses noise-related issues reported in previous studies. Additionally, the paper proposes a novel crack tip extraction procedure to report the energy release rate as a function of the crack increment, even if knowledge of the crack tip is not required for the proposed J-integral method. The three methods are discussed in light of a parametric study conducted on synthetic and experimental data, including artificially noisy data. The study reveals that the proposed J-integral methods are suitable for translaminar toughness evaluation of a wide range of materials without the need for restrictive assumptions. However, variations in propagation values were observed when applied to experimental data. Finally, guidelines are drawn to chose the most suitable parameters for the algorithms that are proposed as a Python package.

We propose a methodology to monitor the progressive saturation of a non-translucent unidirectional carbon fabric stack through its thickness by means of X-ray radiography and extract the dynamic saturation curves using image analysis. Four constant flow rate injections with increasing flow speed were carried out. These were simulated by a numerical two-phase flow model for both capillary and viscous leading flow conditions. The hydraulic functions describing pressure and relative permeability versus saturation were determined by fitting the saturation curves using a heuristic optimization routine. As the fluid velocity increases and the flow regime at the flow front shifts from capillary to hydrodynamically driven, the resulting capillary pressure curves for a given saturation level are shifted to higher values, from negative to positive. These as well as the capillary pressure calculated from the pressure drop within the unsaturated region of the fabric correlate well with a corresponding change in the averaged dynamic contact angle.

Permeability of fibrous microstructures is a key material property for predicting the mold fill times and resin flow path during composite manufacturing. In this work, we report an efficient approach to predict the permeability of 3D microstructures from deep learning based permeability predictions of 2D cross-sections combined via a circuit analogy. After validating the network's predictions in 2D and extending it to 3D, we investigate its capabilities for handling images of various sizes obtained from virtual and real microstructures. More than 90% of 2D predictions is within ± 30% of their counterparts obtained via flow simulations, similarly for 3D transverse permeability predictions, while in 3D case computational time is reduced from several thousands of seconds to less than 10 s. This work provides a robust and efficient framework for characterizing the permeability of fibrous microstructures and paves the way for extending this capability to estimate the permeability of fabric mesostructures.