This study investigates the feasibility of using Near-Infrared Hyperspectral Imaging (HSI) for monitoring the curing state in Liquid Resin Infusion (LRI) manufacturing. By capturing spectral reflectance data throughout the heating and curing phases of the process, we explore the potential of HSI as a non-destructive technique for assessing resin polymerization. Compared to conventional sensor-based methods, HSI offers two key advantages: it eliminates the need to embed sensors within the manufactured part and provides spatially resolved information across the entire surface, enabling the detection of cure inhomogeneities. First, an in-depth study was conducted on the spectral features captured by NIR-HSI and their temporal evolution throughout the process, comparing them with key absorption bands related to the epoxy resin used in LRI. Afterwards, spectral data was correlated with the curing state inferred from commercial dielectric sensors using regression models, including Partial Least Squares Regression (PLS-R) and Support Vector Regression (SVR). These regression models were trained using the full spectral range of a specific spatial region of interest, as well as wavelength subsets selected through feature selection techniques such as Mutual Information (MI) and Random Forest Regressor (RF). The results indicate a strong correlation between the spectral data and the inferred curing state, with the SVR model trained on the full spectral range achieving the highest performance (R²=0.9983, RMSE = 0.0114, MAE = 0.0086). Moreover, the predicted curing maps display consistent trends across all defined regions of interest (ROIs), further demonstrating the capability of HSI to capture spatial variations in the curing process. These findings reinforce the potential of Near-Infrared Hyperspectral Imaging as a powerful, non-destructive tool for real-time monitoring of resin polymerization in LRI manufacturing.

The FLASH-COMP project aims to introduce novel inspection and monitoring technologies to develop a digital solution to predict defects during manufacturing, aiming to reach a zero-waste approach in composites manufacturing. Particularly, it’s studied the integration of two different Fiber Optic Sensor (FOS) technologies: Fiber Bragg Grating (FBG) and distributed All Grating Fiber (AGF^®), to retrieve relevant data during the preforming stage and later resin infusion process for aero-space materials. During the study, both FOS technologies were introduced into the materials, varying process conditions and the introduction of some artificial defects to evaluate the sensors response to correlate them after with their signals. Both systems can retrieve relevant information during the process such as vacuum, leaks and temperature changes, presence of voids and air bubbles, detection of dry zones, and resin flow monitoring. Further developments have to be focused on the scalability in the implementation, since FOS are fragile to handle and need specific training to use it in a more industrial field.

AFP process has the advantage of producing high-performance components and reducing the manufacturing time and defects introduced in the final material thanks to the highly automated process, compared with more traditional methods. Selecting inappropriate AFP process parameters can influence the permeability of the preforms being manufactured and the later mechanical performance of the final component. This paper reviews in detail the influence of the main AFP process parameters (deposition velocity, compaction force and temperature) over the adhesion properties between carbon fiber tapes. Later, three parameter combinations are selected to evaluate their influence over preform permeability and the mechanical performance of the composite after the resin injection process (RTM).

The CAELESTIS project aims to promote the development and design of innovative aircraft and engine structures through an integrated ecosystem of simulations and digital tools, enabling synergy across all stages of the manufacturing process. The component selected was an Outlet Guide Vane (OGV), a static engine part composed of a central composite section and titanium inserts at both ends, joined in a single manufacturing step. A detailed investigation of the joints between these materials was carried out using surface treatments of different natures to evaluate properties that directly influence the final joint quality. Optical analysis techniques were employed to characterize the morphology, roughness and surface free energy (SFE), complemented by mechanical tests to determine the adhesion and shear strength. All specimens were manufactured using the Resin Transfer Molding (RTM) “one-shot” process.

The combination of the dry fiber AFP preforming process and RTM injection process brings new possibilities with regard to automation, high-quality manufacturing, and high-performance characteristics for out-of-autoclave composite manufacturing, particularly in aerospace industry. This paper describes the manufacturing of an aircraft engine Outlet Guide Vane (OGV), made with a dry carbon fiber preform manufactured with Automated Fiber Placement (AFP) and co-injected, co-cured, and co-bonded with titanium fittings through the Resin Transfer Molding (RTM) Process. The details of the assembly process and necessary steps are described. Parts of the digitalization process behind the manufacturing are described, including information about integrated sensors and data management.

To ensure consistent quality in composite aerostructures, advanced non-invasive monitoring techniques are needed to detect global and local deviations during manufacturing. This study presents a real-time, spatially resolved method for monitoring the curing stage of Liquid Resin Infusion (LRI) using Near-Infrared Hyperspectral Imaging (NIR-HSI). Unlike traditional point-based tools such as disposable dielectric sensors, NIR-HSI enables full-field, non-contact assessment of the chemical evolution of the resin, providing valuable spatial information for detecting inhomogeneities caused by temperature gradients or uneven resin flow, factors known to affect the final mechanical properties of composites. Previous investigations demonstrated that hyperspectral data acquired during LRI correlate with the degree of cure estimated from a dielectric sensor. In the present study, we extend this analysis through a new experimental campaign designed to validate our earlier findings and strengthen the predictive model. To improve robustness and generalizability, the curing temperature, a key driver of cure kinetics, was systematically varied to introduce controlled changes in cure behavior. This increased variability enhances model reliability and supports more accurate prediction of curing progression under realistic manufacturing conditions.