Characterisation of the compaction response of reinforcement fabrics is an important component in the design of composite manufacturing processes. To standardise a best practice method, 22 international organisations participated in an exercise to assess the viability and reproducibility of the method discussed in this work. All participants were supplied with the same multiaxial E-glass fibre non-crimp fabric and instructed to measure the compaction stress as a function of the specimen thickness following a set of guidelines. The scatter in results between participants was quantified in terms of the coefficient of variation (CV). The CV of the maximum compaction stress determined at a target specimen thickness of 3 mm (for 10 fabric layers) was 42 % for dry specimens and 46 % for wet specimens, however this was influenced by scatter in the thickness values, which deviated from the target. The CV of the specimen thickness at a compaction stress of 105 Pa was 4 %. In addition, a power law model and a model based on bending of beams were fitted to the compaction curves. Both generally produced fits with high values of the coefficient of determination. The observed level of scatter is thought to be caused by issues with the implementation of the procedures and by variability in the specimen properties, as well as the very steep variation of the force/thickness curve at the required target. The guidelines used here aim to minimise inaccuracies in the test method and will be proposed as a test protocol for standardisation.

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.

Fused filament fabrication is a popular extrusion 3D printing technology because of its affordability and accessibility. However, the approach often suffers from printing errors that result in wasted time, materials and energy. Convolutional neural networks can be trained to recognise a wide spectrum of printing anomalies from image data in real time, but past work has been limited to a few defect classifications at a time. Here, we introduce a fault detection system, designed to identify a range of errors without interrupting the printing process. Real-time detection is achieved using a pre-trained image recognition and pattern recognition convolutional neural network (CNN) with two mounted cameras on the print bed and a nozzle camera. Two CNN models are developed to classify images into common 3D printing errors for the two camera systems. The nozzle camera model achieves a high validation accuracy of 97.7%. The side camera model achieves comparable performance with a validation accuracy of 97.6%. To integrate the two CNNs into one unified system, a logic-based priority framework was used to improve reliability beyond individual model accuracies by resolving conflicting predictions and leveraging complementary viewing angles from both camera types to detect a broader range of defects. The data fusion framework identifies 12 common errors and has significantly improved the robustness of error classification, in-situ and in real-time, with inference times as small as 220 milliseconds. The results demonstrate the feasibility of a robust multi-input fault detection system to advance the reliability of extrusion 3D printing.

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.

Microstructural analysis of resin flow in liquid composite molding is impeded by the absence of a characterization method that possesses both the required spatial and time resolution to capture the ongoing phenomena. An optimized UV-flow freezing methodology is presented to rapidly capture dynamic flow behavior, followed by high-resolution micro-computed tomography (μCT) imaging to extract the flow front morphology. Optimisation of the resin strongly enhances the photopolymerisation kinetics, reducing the gelation time by up to 56%, while an adequate postcuring procedure at moderate temperature is proposed by introducing radical induced cationic polymerization. Additives are identified to facilitate facile variation of the capillary number while distortions of the flow front morphology are minimized by finetuning the experimental procedure. μCT imaging allows for a micron-scale through-thickness assessment of unsaturated flow at range of flow regimes corresponding to both capillary- and viscous-dominated flow regimes while the corresponding saturation curves were derived by segmentation of the resulting images. Highlights: An optimized method for evaluating microstructural flow in fibrous preforms. Optimisation of the resin composition allows for fast UV-photopolymerisation. Additives identified for facile variation of the capillary number. Visualization of “frozen” microstructural flow by micro-CT analysis. Applicable to broad range of flow conditions that normally cannot be captured.

In the collaborative effort towards standardisation of out-of-plane permeability measurement, an international benchmarking exercise was carried out whereby 19 participants worldwide were instructed to measure the out-of-plane permeability following a number of strict guidelines, informed by the outcomes of the first international benchmarking exercise completed in 2021. This paper presents the results of the exercise and an assessment of the reproducibility of the data and the suitability of the proposed test method. The data returned were subjected to a number of statistical analysis methods, which showed that adherence to the test guidelines resulted in a high likelihood of a participant not being an outlier and therefore providing evidence that the test method proposed in this paper is a suitable way forward for a standardised test method.

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.

This paper presents an experimental investigation into the compaction behavior of roving-based unidirectional (UD) reinforcements. Understanding the complex dynamics of roving compaction is essential for ensuring the quality in composite manufacturing processes and in return to reach optimal performance. In continuous manufacturing processes like pultrusion or tape manufacturing, achieving proper compaction of rovings is crucial for reaching high fiber volume fractions and consequently, the desired mechanical properties in the final composite profile. However, high compaction in rovings poses challenges, particularly during the impregnation phase, where pressure buildup can occur, potentially leading to the formation of undesirable voids or to excessive forces on fiber, causing fuzz accumulation. Balancing the need for high compaction with the risk of defects is thus a delicate exercise, critical for ensuring the quality and integrity of the final product. Through experimental characterization and analysis, this work examines the influence of various factors to the compressibility of rovings, including the application of tension and different configurations in dry conditions. This study aims to provide valuable insights into characterizing the compaction behavior of roving-based reinforcements and the effect of applied tension, thereby advancing the state-of-the-art in continuous composite manufacturing processes.

Multiple phenomena occurring at the microscopic scale affect the final mechanical performance of composite parts manufactured through processes involving impregnation of dry fibers, such as resin transfer molding. Formation of fiber-poor areas in specific locations or air entrapment within the resin are issues that commonly arise during the impregnation. Such challenges have motivated the use of numerical simulations to understand the manufacturing processes better and to optimize the process design. However, the limitation imposed by their computational cost has encouraged the use of machine learning (ML) to replace them. Thus far, the state of the art has focused on predicting the permeability of fiber-reinforced microstructures. We expand the limits by proposing an ML-based surrogate for microscale steady-state velocity prediction of a fluid flowing through a fibrous microstructure. This model, inspired by the U-net architecture, takes as input the image representation of fiber-reinforced composite microstructures. It subsequently outputs the resin velocity field around the fibers based on prescribed boundary conditions. Those results are further used to estimate the permeability of the microstructures, thus encompassing previous works. We describe in this work the computational pipeline of our approach, starting from generation of the ground truth data to the optimization of the UNet hyperparameters.

Thin-ply carbon fiber reinforced polymers (CFRP) have claimed significant attention for their potential to surpass traditional composite materials in terms of performance metrics such as first-ply damage criteria, fatigue life, and ultimate strength. This study focuses on investigating the friction behavior of dry carbon fiber tow during mechanical bar spreading, a crucial process in the manufacturing of thinply CFRP. By systematically examining the interplay of wrap angle, tow pre-tension, and final tension, insights are provided into the frictional forces exerted on the carbon fibers. The study utilizes an experimental framework to analyze single-bar and multi-bar setups, considering both symmetric and asymmetric configurations. Results reveal non-linear friction behavior, with increasing wrap angles leading to decreased dynamic friction coefficients. Additionally, results seem to suggest that higher pretension reduces internal tow movement, thereby decreasing friction losses. Multi-bar setups exhibit distinct friction profiles compared to single-bar setups, especially for larger wrap angles and asymmetric cases, indicating the influence of superimposed wrap angles on friction. Recommendations for future research include further exploration of factors such as non-uniform normal loads and relaxation distances between spreader bars to enhance modeling accuracy and optimize friction performance.

Frontal polymerisation has the potential to bring unprecedented reductions in energy demand and process time to produce fibre reinforced polymer composites. Production of epoxy-based fibre reinforced polymer parts with high fibre volume content, commonly encountered in industry, is however hindered by the heat sink created by the fibres and the mould, overcoming the heat output of the chemical reaction, thus preventing front propagation. We propose a novel self-catalysed frontal polymerisation manufacturing method based on the integration of thin resin channels in thermal contact with the composite stack as a strategy for low-energy production of high fibre volume fraction polymer composites without the need for a continuous energy input. Frontal polymerisation inside the resin channel proceeds faster and preheats the fabric stack, thus catalysing the process. Parts with up to 60% fibre content are successfully produced independently of the sample thickness. Fillers added within the resin channels provide means to tailor the frontal polymerisation process kinetics. The parts have a significantly higher glass transition temperature than those produced in a conventional oven, and comparable mechanical properties while energy consumption is reduced by over 99.5%.

This study presents the first steps of a benchmarking exercise on image processing of composite materials. Employing three distinct imaging protocols and five different image processing algorithms, the research explains the variability in capturing microstructural features of common micrograph dataset. Results highlight the sensitivity of different methods to factors like illumination inhomogeneities and pixel density, influencing the accuracy and consistency of obtained results. By comparing methodologies from different researchers in a blind format, the study identifies strengths and limitations, laying the groundwork for future benchmarking activities. Moving forward, this research sets the stage for standardized protocols and guidelines, aiming to enhance the reproducibility and reliability of microstructural analysis in composite materials. Such efforts are crucial for advancing material design and development, ultimately creating tailored composite materials with enhanced performance and functionality.

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.

The aim of this study was to characterise the microstructural organisation of staple carbon fibre-reinforced polymer composites and to investigate their mechanical properties. Conventionally, fibre-reinforced materials are manufactured using continuous fibres. However, discontinuous fibres are crucial for developing sustainable structural second-life applications. Specifically, aligning staple fibres into yarn or tape-like structures enables similar usage to continuous fibre-based products. Understanding the effects of fibre orientation, fibre length, and compaction on mechanical performance can facilitate the fibres’ use as standard engineering materials. This study employed methods ranging from microscale to macroscale, such as image analysis, X-ray computed tomography, and mechanical testing, to quantify the microstructural organisations resulting from different alignment processing methods. These results were compared with the results of mechanical tests to validate and comprehend the relationship between fibre alignment and strength. The results show a significant influence of alignment on fibre orientation distribution, fibre volume fraction, tortuosity, and mechanical properties. Furthermore, different characteristics of the staple fibre tapes were identified and attributed to kinematic effects during movement of the sliver alignment unit, resulting in varying tape thicknesses and fuzzy surfaces.

Transverse permeability is a key parameter to construct accurate process modelling frameworks for composite manufacturing processes whereby dry reinforcements are impregnated by a resin. The transverse permeability of fibrous structures is strongly correlated with the spatial fiber distribution which is inevitably irregular through the cross section. This irregularity and possible resin channel formation through the cross section cause complex permeability field. In this study, we analysed the correlation between the microstructural features and the apparent transverse permeability. Multiple regions of interest were collected from a pultruded part’s cross section that exhibited apparent resin rich layers. Apparent transverse permeability values of these Regions of Interest (ROIs) were calculated by using OpenFOAM. The principal components of the transverse were calculated, and the statistical descriptors were captured for the corresponding ROIs. The principal permeability component analysis showed that principal directions are in-line with the visual predictions. The correlation between the permeability components and the statistical descriptors shows some traces of the capability of predicting the transverse permeability and the principal components based on the microstructural features. A larger dataset and more elaborate statistical descriptors are needed for accurate prediction of the flow behaviour and the apparent permeability components.

We seek to address how air entrapment mechanisms during infiltration are influenced by the wetting characteristics of the fluid and the pore network formed by the reinforcement. To this end, we evaluated the behavior of two model fluids with different surface tensions, infiltrating three carbon fiber reinforcements, by means of X-ray radiography. We also assessed initial (dry) and final (wetted) states for each experiment by performing X-ray CT scans. We found that the fluid characteristics strongly affect the flow front patterns and pore filling events for a given fabric architecture. Two main promoters of snap-off events are involved in capillary dominated flows: a very wetting system leading to corner flows and the fabric bundles oriented perpendicular to the flow acting as obstacles, specifically in fabric architectures prone to variations in nesting. Finally, we evaluated the applicability of a pore network model to further link preform architecture and void formation.

We investigated the debonding on-demand (DoD) of adhesively bonded hybrid dissimilar joints by applying electromagnetic induction heating to the joint overlap section, wherein the epoxy resin is reinforced with iron oxide (Fe₃O₄) particles. Ti-6Al-4 V adherends were bonded with CFRP or GFRP adherends using neat/modified epoxy adhesive. DoD tests revealed that eddy current heating of Ti-6Al-4 V was a dominant heating mechanism of the joints while both eddy current and magnetic hysteresis of CFRP and Fe₃O₄ acted as a secondary heating factor. A low content Fe₃O₄ and thinner composite adherend reduced the time to failure of the joints. Likewise, CFRP required a shorter time for debonding compared to GFRP due to its electromagnetic properties. Modifications with 2 and 5 wt.% Fe3O4 for CFRP and GFRP joints led to 31% and 37% time reduction which will be crucial for energy-saving when debonding large structures. Remarkably, sandblasting improved the electromagnetic induction capabilities of Ti-6Al-4 V, leading to a notable increase in the heating rate, which jumped from around 20°C/s to 80°C/s. Sandblasting enhanced the surface roughness of the adherends but only the water contact angle of GFRP decreased considerably. Fe₃O₄ modifications increased the epoxy residue on the Ti-6Al-4 V surface from 26% to 99%. DIC revealed the strain distribution of bulk materials to understand the thermomechanical mismatches between the materials and the adhesive joints exhibited high peel stresses at the overlap ends. The low weight content (2 and 5 wt.%) of Fe₃O₄ exhibited beneficial effects on the mechanical, thermal, thermomechanical, wettability and lap shear strength.

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.

Radical induced cationic frontal polymerisation (RICFP) is considered a promising low energy method for processing of fibre reinforced polymers (FRPs). Optimisation of the local heat balance between reinforcement, epoxy resin and the surrounding mould is required to pave the way for its adaptation to an industrial processing method for high volume fraction structural fibre reinforced composites. In this work, we investigate several methods to control the governing heat balance in RICFP-processing of FRPs. Heat generation was controlled by tuning the initiator concentration while limitation of heat losses using highly insulating moulds was found beneficial to the front characteristics and resulting curing degrees. An optimised mould configuration allowed for self-sustaining RICFP in FRPs with fibre volume fractions (V_fs) up to 45.8%, exceeding previously reported maxima of similar systems. A process window was moreover established relating the V_f and required heat generation to the potential formation of a self-sustaining or supported front.