Carbon fiber reinforced plastics (CFRPs) have been widely applied in various fields such as aerospace, vehicles, and civil infrastructure, even though their manufacturing time, labor, and cost are burdensome compared to other materials. However, studies on monitoring systems for composite manufacturing are limited. The spotlight is biased toward CFRP design and functional nanocomposites. Therefore, in this study, an in-situ monitoring system of a CFRP is proposed to investigate the vacuum-assisted resin infusion process in real time using electrical resistance changes. Specifically, two electrode types namely Cu tape and silver paste, were comparatively analyzed under several vacuum bag pressures. Resin impregnation was then investigated on the scale of a carbon fiber single tow and laminae. Impregnated resin increases the thickness of the laminae, thereby increasing the electrical resistance. Moreover, the curing behavior of the resin was correlated with changes in its electrical resistance so that the viscosity and degree of curing of the resin could be identified. The proposed method enables the detection of not only the resin front, but also the degree of curing.

The expanding use of fiber reinforced composites in aerospace automotive and energy sectors demands manufacturing routes that deliver consistent quality at higher production rates. Yet defects formed during processing and post processing remain major barriers, including porosity fiber misalignment weak interfacial bonding and machining induced damage that can reduce strength fatigue life and assembly reliability. This review consolidates recent progress across the full processing chain for thermoset and thermoplastic composites, covering conventional fabrication and high rate forming methods, followed by key post processing operations such as drilling trimming and joining with emphasis on defect mechanisms and mitigation strategies. Emerging directions are highlighted that enable smarter and more robust production, including robotic forming and machining platforms, multi sensor in situ monitoring, and artificial intelligence models for quality prediction parameter optimization and defect detection. The complementarity between data driven learning and physical process understanding is emphasized to support interpretable decision making and closed loop control in complex thermo mechanical environments. Sustainability considerations are discussed through recyclable thermoplastic pathways and circular manufacturing concepts that reduce waste and energy consumption. By integrating advances in manufacturing post processing intelligent control and sustainability, this review provides a unified perspective and identifies priorities for future research toward automated defect resilient and digitally enabled composite production. • Integrated review of FRP and FRTP manufacturing and post-processing advances • AI-based process modeling enables real-time void and defect prediction in composites • Robotic drilling and trimming improve accuracy for CFRP and CFRTP machining • Sustainable thermoplastic routes enable recyclability and circular lifecycle design • Digital-twin and sensor-fusion systems drive intelligent composite manufacturing

This study compares the residual stresses in steam generator tubes used in nuclear reactors—specifically, a Utube in conventional pressurized water reactor (PWR) and a helical tube in small modular reactors (SMR). With the U-bend radius held constant, tube thickness and helical geometry were varied. Finite element analysis, employing the Yoshida-Uemori kinematic hardening model with Swift isotropic strain hardening law, was conducted to simulate the forming processes and evaluate residual stress distributions. The analysis shows that while the effective strains predicted by pure bending theory closely match the simulation results, differences in plastic deformation and stress evolution arise from the distinct deformation histories. The helical tube exhibits similar residual stresses compared to the U-tube for all thickness and helical geometry, implying that the same heat treatment process may be applicable to both designs.

This study aims to optimize the thermoforming process of Glass Fiber Reinforced Thermoplastics (GFRTP) by investigating the effects of temperature, pressure, stacking angle, and heating time. Systematic adjustments of these parameters enable a detailed microstructural analysis using optical microscopy. Python-based image analysis is employed to extract key quantitative features, such as void fraction, to support process optimization. Furthermore, an Artificial Neural Network (ANN) model is developed to predict optimal processing conditions. The ANN results identify conditions that minimize void fraction, demonstrating the effectiveness of the proposed optimization approach. This study provides a theoretical foundation for GFRTP manufacturing and introduces an innovative combination of image analysis and ANN modeling to enhance production efficiency and product quality, promoting broader composite applications.