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.

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.