Smart Integration of Process Monitoring in Composite Manufacturing Using Electromechanical Behavior and Artificial Intelligence

Abstract

As carbon neutrality becomes a global imperative, the demand for advanced technologies that mitigate greenhouse gas emissions has increased significantly. Among various strategies, reducing structural weight in key sectors such as transportation, aerospace, and renewable energy has emerged as a highly effective method to reduce carbon dioxide (CO₂) emissions. This shift has accelerated the adoption of lightweight materials with high specific mechanical properties. In this context, carbon fiber- reinforced plastics (CFRPs) have attracted considerable attention due to their superior strength -to- weight ratio, excellent corrosion resistance, and design flexibility—qualities that make them highly suitable for high-performance structural applications. With growing environmental and performance demands, CFRPs are increasingly used in the manufacturing of aircraft fuselages, automotive body panels, wind turbine blades, and marine structures. However, as applications scale up, so does the complexity of their production, particularly in ensuring uniform resin distribution and consistent quality. Among the various manufacturing techniques, resin transfer molding (RTM) stands out as a widely adopted process due to its capability to produce large, geometrically complex components with high fiber volume fraction, low void content, and fine surface finishes. Nonetheless, RTM faces persistent challenges, including the formation of internal defects such as dry spots, voids, and incomplete resin impregnation. These defects can significantly degrade the mechanical properties and long-term reliability of the final products. Traditionally, ensuring product quality in manufacturing has relied on destructive testing or post- process inspections, both of which are time-consuming, labor-intensive, and costly. These methods also limit real-time corrective actions. As a result, in-situ process monitoring techniques have been studied using several types of sensors. Among these, self-sensing approaches, based on the inherent electromechanical behavior of carbon fibers, have been gaining popularity due to their cost- effectiveness and ease of sensor installation. However, most previous studies remain limited to small- scale specimens and focus primarily on identifying the current status of resin flow (e.g., flow-front tracking or dry-spot detection), without extending toward predictive or quantitative quality evaluation. With the growing trend of automation and active control in manufacturing, quality control strategies that provide real-time feedback and construct digital twins are becoming increasingly important. Recent advances in sensor technologies, combined with artificial intelligence (AI) and machine learning (ML), have opened new possibilities for intelligent, adaptive manufacturing frameworks capable of monitoring, predicting, and optimizing composite production processes in real time . This dissertation proposes a comprehensive, data-driven approach to improve RTM-based CFRP manufacturing through the integration of electrical resistance-based sensing and AI-driven analysis. The research is structured around three key areas: (1) real-time process parameter decoupling, (2) flow-front identification and prediction, and (3) mechanical property estimation using process signals. Each stage is interconnected, forming a robust framework for real-time quality assurance and intelligent process control. The first part of the study presents the development of a multi-stage monitoring system using electrical resistance measurements. Electrodes embedded on the fiber surface capture resistance variations during resin infusion, enabling accurate, in-situ detection of resin arrival and flow progression. The data were analyzed using two types of decoupled strategies involving classification and quantitative comparison. The system demonstrated the ability to detect resin arrival within 10 seconds and achieved high agreement with simulation results, with flow front prediction errors maintained within 4%. The approach allowed for visualization of resin movement in both the in -plane and through-thickness directions, enabling early detection of flow irregularities and potential defects. Building upon the monitored data, the second phase applies artificial intelligence to predict flow- front evolution and configurations under different scenarios. The electromechanical behavior captured by electrical resistance data was used to classify flow patterns and generate flow-front configurations. A robust framework was developed, combining a decision tree algorithm for real-time scenario classification and a generative adversarial network (GAN) to produce spatially accurate flow-front images. The model successfully identified 17 distinct scenarios across three mold regions and effectively captured complex resin behaviors such as racetracking—without requiring explicit permeability data. This approach significantly reduced computational time and improved prediction robustness under varying process conditions. In the third part, the study investigates the correlation between real-time monitoring data and the mechanical properties of the final CFRP products. Full-scale CFRP panels (400 mm × 600 mm) were manufactured and segmented into 26 test specimens. Mechanical performance was assessed through three-point bending tests, and results were correlated with a novel monitoring index derived from electrical resistance data. This index accounted for impregnation quality based on increase in electrical resistance during the infusion and curing. The model achieved low root-mean-square error (RMSE) and mean absolute error (MAE), demonstrating high accuracy in estimating mechanical properties directly from process data. Microscopic analysis of specimens validated the influence of internal defects on mechanical behavior and supported the reliability of the monitoring index. In conclusion, this study introduces an integrated framework that combines real-time sensing, data interpretation, and artificial intelligence to enhance the quality, efficiency, and sustainability of RTM- based CFRP manufacturing. Three key contributions of this research are as follows: the development of a real-time electrical resistance monitoring system for full-scale RTM processes, AI-based flow front prediction using hybrid models for enhanced spatial and temporal accuracy, a quantitative monitoring index for in-situ mechanical property estimation, enabling non-destructive quality control. Collectively, these innovations contribute to the advancement of intelligent composite manufacturing and offer a practical pathway toward digitalized, real-time process optimization. The findings have broad implications for industries such as aerospace, automotive, and renewable energy, where high-performance composites are essential. This work supports the transition to smarter, more sustainable manufacturing systems and underscores the importance of data-driven methodologies in next-generation composite production.