Optimizing the in-situ process monitoring of VARTM using graphene/PVA-coated fabric sensor

• Developed an efficient data acquisition method for capturing information from several locations of sensor during the process. • Visualizing the recorded sensor data in an informative and systematic manner. • Quantified various processing parameters (e.g., total infusion time, clamping time) by assessing the real-time sensing data obtained during manufacturing phase. • Demonstrated the effect of resin or sensor conductivity on the collected sensor data by varying the number of Graphene layers on coated fabric sensors. The recent trend in composite manufacturing, driven by Industry 4.0, necessitates the acquisition of a substantial amount of data to develop process control, thereby reducing processing time and enhancing product quality. In this study, a cost-effective online process monitoring system for collecting data during various stages of the vacuum-assisted resin transfer molding (VARTM) process is proposed using a piezoresistive fabric sensor. The sensor was prepared by dip-coating glass fabric (GF) into a novel graphene/polyvinyl alcohol (PVA) coating formulation with varying graphene compositions (3, 5, and 7 wt%) and the number of dips (3 and 5) to optimize the sensor’s functionality. The real-time electrical response obtained from 5 wt% graphene/PVA-coated GF sensor with 5 dips showed the best results for detecting applied vacuum pressure by properly depicting the hysteresis effect between each vacuum cycle. By visualizing data from 10 electrode pairs of the sized GF sensor placed along and perpendicular to the direction of resin flow, the location of the flow front can be precisely monitored. Moreover, the 5 wt% graphene/PVA-coated (5-dip) GF sensor can accurately evaluate processing parameters such as time for total infusion and clamping point. The unique scalability and non-invasiveness of the novel graphene/PVA-coated GF sensor make it highly valuable for monitoring composites production as well as for lifelong structural health monitoring (SHM) applications.