The goal of this research is to simulate the mold filling process for thermoplastic(TP) melts reinforced with short and long fibers and eventually nano-particles of high aspect ratio using constitutive relations (i.e. stress tensors coupled with an orientation generation expression) which allow coupling between the flow and particle orientation and to include kinematics of frontal flow. The predicted particle orientation distribution will be used along with micro-mechanical models to predict the stiffness variation throughout an injection molded part. A key element is the development of the rheological methodology which will provide basic material flow properties to the numerical simulation package. An experimental evaluation of the predicted particle orientation distribution for basic flows associated with injection molding will be carried out to assess the performance of the simulation.
There are numerous societal benefits to this work. By incorporating models in which the flow and structure are coupled along with viscoelasticity, it is expected that the design of molds and selection of injection molding conditions will be significantly improved and accelerated for reinforced TP's. This will help improve the ability of US automakers to compete in the world-wide automotive market as design changes can be made more rapidly and cheaply. Once a simulation package is developed for injection molding, it can easily be extended to other processes such as compression molding and injection-compression molding. From an educational view graduate students will learn that the properties of composite parts are highly dependent on processing conditions and the potential to predict this relation exists. Furthermore, the students will learn the importance of a cooperative effort in solving complex problems as the engineering student will learn about numerical simulation using the finite element method while the mathematics student will learn about transport processes and the rheology of structured fluids. This project is a cooperative effort between Virginia Tech and DOE laboratories (ORNL and PNWL) and is co-funded by NSF and DOE's FreedomCAR program.
