Mazumder Md Rahinul Hasan, Govindaraj Premika, Mehedi Hasan Muhammad, Antiohos Dennis, Salim Nisa, Konstantin Fuss Franz, Hameed Nishar
School of Engineering, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia.
Chair of Biomechanics, Faculty of Engineering Science, University of Bayreuth, Bayreuth, D-95447, Germany.
Chemphyschem. 2025 Feb 16;26(4):e202400189. doi: 10.1002/cphc.202400189. Epub 2024 Dec 19.
The location of defect formed in the final composite is identified using sensor data. 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. 5 electrode pairs were placed in the horizontal and vertical directions on the area of each coated fabric sensor before placing it inside the VARTM setup. 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. Most importantly, physical parameters such as infusion time, flow front location, race-tracking and dry spots were monitored in-situ.
利用传感器数据确定最终复合材料中形成缺陷的位置。在此，我们报告了一种用于真空辅助树脂传递模塑（VARTM）工艺的在线过程监测系统的开发，该系统使用大面积石墨烯原位包覆织物传感器。除了赋予最终复合材料优异的机械性能外，这些传感器在复合材料加工过程中还提供关键信息，包括检测缺陷和评估加工参数。所获得的信息可用于创建制造阶段的数字护照，以开发具有成本效益的生产技术并制造高质量的复合材料。织物传感器是通过可扩展的浸涂工艺生产的，根据织物浸入氧化石墨烯（GO）溶液的次数将1、3或5层热还原氧化石墨烯（rGO）涂覆在玻璃织物表面。在将每个涂覆的织物传感器放入VARTM装置之前，在其区域内沿水平和垂直方向放置5对电极。在VARTM工艺的不同阶段，同时连续记录所有电极对的电阻，以确定相对电导率。在真空循环期间，相对电导率的范围随着涂覆的rGO层数的增加而增加，5层rGO涂覆的传感器显示出最高的电导率范围，为16.9%。此外，观察到5层涂覆的传感器在灌注阶段由于流体流动压力主导树脂电导率而电导率持续下降。最重要的是，对灌注时间、流动前沿位置、跑道跟踪和干点等物理参数进行了原位监测。
