Development of An Advanced Analysis Tool for Characterization, Simulation and Remedy of Molding Induced Defects in Liquid Composite Molding

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9414287 Lee Liquid composite molding processes such as resin transfer molding (RTM) and structural reaction injection molding (SRIM) are one of the fastest growing new manufacturing technologies in aerospace and automotive industries. They have been recognized by many researchers and engineers as the most feasible approach to mass produce light weight, high strength and low cost structural components. The major manufacturing technique in today's aerospace industry for light weight components is prepreg/autoclave process. This process is expensive, slow and labor intensive. It can not compete well in the cost driven business such as the civil aero- transportation industry. This is why many companies, particularly in Europe, are developing RTM processes. The major manufacturing techniques in today's automotive industry for light weight components are thermoplastic injection molding and SMC compression molding. These processes can only handle low volume short fiber reinforcement, therefore, they can not produce high strength products which are needed for structural applications. SRIM and RTM, on the other hand, are based on high volume fractions of continuous fiber reinforcement. They can potentially produce parts stronger than steel made parts. The new experimental techniques for flow analysis include measurements of capillary forces and fiber wetting for fabric reinforcements with different fiber architecture and the three- dimensional microscopic flow visualization and image analysis. The experimental techniques for curing analysis include rheological and kinetic analyses and reactive dilatometry of matrix resins. Computer modelling will be carried out at two levels; a model simple enough to be combined with existing computer codes for quick and easy introduction to the manufacturing sector and a sophisticated model addressing the full range of mold filling to final product property prediction will be developing using supercomputing capabilit ies.