Data enriched lubrication force modeling for a mechanistic fiber simulation of short fiber-reinforced thermoplastics

Abstract

The simulative prediction of fiber orientation for injection-molded short fiber-reinforced thermoplastics is an important step in prediction warpage and failure of injection molded parts. There exists a variety of phenomenological macroscopic fiber orientation models, which are computationally very efficient but strongly dependent on phenomenological parameters. This research focuses on a mechanistic fiber orientation model for concentrated short fiber-reinforced thermoplastics. A fully coupled computational fluid dynamics particle simulation is used to estimate the lubrication forces between two fibers in different configurations (angles between fibers, velocities) with varying fiber length and surrounding fluid viscosity. Based on these data, a calibrated lubrication model is developed and implemented in a mechanistic fiber orientation simulation. In addition, the fiber orientation estimated by the enhanced mechanistic fiber model is compared to experimental fiber orientation data obtained with a glass fiber-reinforced thermoplastic industrial grade, which showed an improvement over a simulation that did not include the lubrication force.