Validation and Implementation of Control Strategies for Liquid Composite Molding Processes Available to Purchase

The manufacturing of polymeric composites ranges from using a rudimentary hand lay-up to the use of automated processes such as Liquid Composite Modeling (LCM) developed over the past decades in order to increase the yield of manufactured composite parts. In these processes, fiber preforms are placed in a closed mold and resin is infused into the mold to saturate the preform. After the resin cures, the mold is opened and the net shape composite part is demolded. However, by introducing more complexity into the part, one also introduces higher probability of flow disturbances, such as race tracking along preform edges, into the molding system. This can lead to incomplete saturation of fiber performs resulting in flaws such as dry spots in the composite part. The strength and existence of race-tracking is a function of the fabric type, perform manufacturing method, and its placement in the mold. It can vary from one part to the next in the same production run, and therefore it is not repeatable. In this work, after illustrating experimentally the unpredictability of variation of race-tracking and its influence on the flow, two approaches have been investigated and validated to address this issue associated with the variation of inherent disturbances in LCM processes. An active control strategy method using process models and simulations along with sensing and control to address flow disturbances during the impregnation stage of the process was shown to be reliable and effective for Resin Transfer Molding (RTM) process. In an attempt to improve the automation of RTM process, a modular RTM workstation including all hardware and software necessary to implement active control strategies for various part geometries and a novel injection system was designed and tested. In addition, a passive control method for Vaccum Assisted RTM (VARTM) aimed at optimizing the placement of distribution media for a given set-up in order to reduce dry spot formation and filling time was developed and validated experimentally. The optimization method employs numerical flow simulations and global optimization search techniques (Genetic algorithm) to generate the design for strategic flow control system.