The objective of this research is to develop a predictive capability that evaluates the mechanical properties of short and long fiber reinforced polymer composites from higher-order orientation tensors. The approach is to develop an automated three-dimensional finite element procedure for predicting the effective mechanical properties of a fiber suspension from its orientation distribution and the mechanical properties of the constituent materials. The work also includes measuring fiber orientations with micro-CT imaging techniques and developing a computational approach for generating fiber samples numerically from polymer melt flow simulation results. As part of this research, a Monte Carlo simulation procedure will be used to assess the statistical nature of the predicted properties, and the simulation methodology will be demonstrated on industrially-relevant products.
Fiber reinforced polymer composites are the material of choice in numerous engineering applications, due in part to their superior strength to weight ratio. This is especially true for long and short fiber composite products, which also benefit from extremely versatile manufacturing methods such as injection molding. As a result, there continues to be a major effort to incorporate more fiber reinforced polymers in commercial products, particularly in the US automotive industry where future vehicles must have reduced weight, emissions, and fuel consumption. Indeed, a specific objective of the 2010 FreedomCAR program at the US Department of Energy, a co-sponsor and collaborator of this research, is to reduce the weight of an automotive structure by 50 percent for the same cost and durability as seen in today's products. Their aggressive goal is supported under this research project by its focus on a critical link between a fiber reinforced polymer product and its manufacturing process. The research also includes the development of educational visualization tools that will provide a clearer understanding of fiber suspension mechanics for future engineering students.
