• Mazumder, Md Rahinul Hasan
• Fuss, Franz Konstantin
• Hasan, Muhammad Mehedi
• Govindaraj, Premika
• Antiohos, Dennis
• Salim, Nisa
• Hameed, Nishar

Abstract

<jats:p>Herein, we report the development of an online process monitoring system for vacuum‐assisted resin transfer molding (VARTM) process using large area graphene coated in‐situ fabric sensor. Besides imparting excellent mechanical properties to the final composites, these sensors provide critical information during the composite processing including detecting defects and evaluating processing parameters. The obtained information can be used to create a digital passport of the manufacturing phase to develop a cost‐effective production technique and fabricate high‐quality composites.  The fabric sensor was produced using a scalable dip‐coating process by coating 1‐, 3‐ or 5‐layers of thermally reduced graphene oxide (rGO) onto glass fabric surface according to the number of dips of the fabrics into GO solution. The electrical resistances from all electrode pairs were simultaneously and continuously recorded during distinct stages of the VARTM process to determine the relative conductance. During the vacuum cycle, the range of relative conductance increased with the number of coated rGO layers, with the 5‐layer rGO‐coated sensor showing the highest conductance range of 16.9 %. Additionally, it was observed that the 5‐layer coated sensor showed a consistent decrease in conductance during the infusion phase due to the fluid flow pressure dominating the resin electrical conductivity.</jats:p>

Topics

• surface
• polymer
• phase
• glass
• glass
• composite
• defect
• resin
• electrical conductivity