Numerical simulations are commonly used to predict resin flow in fibrous reinforcements but exhibit a trade-off between accuracy and computational cost. As an alternative, machine learning (ML) based models pose as a potential tool to accelerate or replace such costly simulations. This work proposes an open-source image-based deep learning framework to estimate the permeability of unidirectional microstructures in arbitrarily sized domains. This presents a scalable step towards estimating the permeability of large meso-domains. First, we present two robust and accurate surrogate models capable of predicting microstructure velocity and pressure fields with varying physical dimensions, fiber diameter, and volume fraction. These predictions achieve 5% error on the training set and 8% error on unseen microstructures. Secondly, based on those predicted flow fields, we infer the permeability of the microstructures with respectively 4% and 6% deviation for the training and validation sets. Third, opposed to previous works limited to microstructures with a fixed aspect ratio, we propose a so-called sliding window procedure, based on physics-based principles to predict the resin velocity and pressure field in microstructures with different aspect ratios. The method is validated against high-fidelity numerical simulations, and its predictive performance and computational efficiency are confirmed with μ-CT scans of real microstructures. Finally, the presented code and surrogate model are open-sourced to promote further exploration by the scientific community.

A new and sustainable membrane manufacturing method is 3D printing, which reduces the number of fabrication steps, waste production, and the corresponding CO₂emissions. It further enables fabricating membranes with well-defined pore size, shape, and configuration. Here, we study 3D printing of microfiltration membranes using a novel dual-wavelength microstereolithography method. Via the gradient descent method, we are able to calculate and control a printable membrane with micrometer precision, enabling the possibility of printing membranes directly. Hydrophilic porous membranes with cylindrical microscale pores (≈10 μm in diameter) are printed from polyethylene glycol diacrylate (PEGDA). Membrane printing procedure and postprocessing steps are thoroughly investigated to print consistent membranes with uniform thickness. The membranes are fully characterized using SEM, FTIR, contact angle, and surface roughness measurements. The pure water permeability and separation performance of the 3D-printed membrane are further investigated and compared with those of commercial hydrophilic PTFE membranes. The 3D-printed membranes show similar permeability values to those of commercial membranes and could successfully separate oil droplets from oil-in-water emulsions. The membranes’ permeability is further predicted using a 1D tube model and numerical modeling. The effect of material’s property (e.g., swelling) and pore deformation during pressurization are studied to understand the discrepancy between the calculated and the experimental permeability values. The results provide valuable insights into the permeability prediction of 3D-printed membranes and the corresponding design optimization.

Translaminar fracture toughness is pivotal for notch sensitivity and damage tolerance of fibre-reinforced composites. Hybridisation offers a promising pathway for enhancing this parameter in thin-ply composites. Three novel mini-compact tension specimen geometries were investigated for their competence in microscale characterisation of translaminar fracture using in-situ synchrotron radiation computed tomography (SRCT). Only “mini-protruded” design resulted in stable crack propagation with adequate crack increments. Based on this design, five baseline and hybrid cross-ply configurations incorporating low- and high-strain carbon fibres were studied. Crack propagation in low- and high-strain baseline configurations was stable. For interlayer and intrayarn fibre-hybrid configurations, a correlation between load–displacement curves and delamination is observed. The SRCT data confirmed that 90° ply-blocks cushion the interaction between 0° plies, enabling independent fracture. Additionally, crack fronts in 90° plies advance further than those in 0° plies. Moreover, mechanical interlocking and bundle bending within 0° plies serve as supplementary mechanisms for energy dissipation.

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.

Permeability measurements of engineering textiles exhibit large variability as no standardization method currently exists; numerical permeability prediction is thus an attractive alternative. It has all advantages of virtual material characterization, including the possibility to study the impact of material variability and small-scale parameters. This paper presents the results of an international virtual permeability benchmark, which is a first contribution to permeability predictions for fibrous reinforcements based on real images. In this first stage, the focus was on the microscale computation of fiber bundle permeability. In total 16 participants provided 50 results using different numerical methods, boundary conditions, permeability identification techniques. The scatter of the predicted axial permeability after the elimination of inconsistent results was found to be smaller (14%) than that of the transverse permeability (∼24%). Dominant effects on the permeability were found to be the boundary conditions in tangential direction, number of sub-domains used in the renormalization approach, and the permeability identification technique.

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.

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.

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.