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This paper presents the physical phenomena and equations governing resin infusion under a flexible cover. This composite manufacturing process known as vacuum assisted resin infusion (VARI) is analyzed here when the reinforcement is covered by a thin plastic film and resin is injected by gravity after a partial level of vacuum has been achieved in the cavity. In this process, the plastic cover is deformed as the resin fills up the mold cavity. Coupling between the mechanical deformation of the flexible cover and the resin flow inside the mold cavity is described by a set of mathematical equations with boundary conditions. Based on this model, a general methodology is developed to simulate numerically the resin flow during the infusion process. Validation of the numerical results is performed by comparison with a series of experiments. POLYM. COMPOS. 26:417–427, 2005. © 2005 Society of Plastics Engineers

Liquid Composite Molding (LCM) regroups a number of manufacturing techniques of polymer composites based on the impregnation of dry fibrous reinforcements by a liquid resin. It involves several complex phenomena: fibre impregnation, resin gellification and cure, thermal and rheological variations, etc. The combination of such phenomena and the wide range of processing parameters often lead to non-optimum, sometimes inappropriate, processing setups. In this work, an approach is proposed to assist manufacturing specialists in reducing process development time and improving process robustness. A software interface was developed to enable users to define and quickly compare different processing scenarios. Using fuzzy logic inference and different levels of mathematical simplification, the proposed software is able to sketch the moldability diagram of the part and perform basic process optimizations. One original feature of the proposed approach consists of integrating into the optimization loop the feedback of process engineers which helps in correcting the numerical solution. An application example is conducted in order to demonstrate the capabilities of the approach for understanding process behaviour.