Analytical and Numerical Methods for Optimizing Screw Geometries of an Injection Molding Plasticizing Unit with Focus on Standard Three-Section Screws

摘要

At present, there are only few established simulation programs for the estimation of processes in an injection molding plasticizing unit. Their results are quite often inaccurate and troubleshooting has proved to be difficult. Therefore, a new simulation software was developed to predict the optimum screw geometries and process parameters for the respective applications. This work focuses on standard three-section screws, which are the most common. At first, extensive material characterization and appropriate material models are the basis for high quality process simulations. Hence, several observations with different measurement devices were conducted for determining thermodynamic and rheological material parameters. Secondly, the three-dimensional helix shape of the screw channel consisting of feed, compression and metering section can be approximated via an unrolled screw channel. Afterwards, a 2D grid generator is used to divide the unrolled channel in numerous small grid cells. The screw pitch, channel height and flight number as well as further geometric parameters can be variable along the axial screw length. Finally, the simulation can be examined by using a clear separation of the melt and solid fraction, several mathematical and physical models, especially the finite difference method, the melting model based on Tadmor and Gogos, White and Potente and a numerical temperature calculation based on Miethlinger and Aigner. The axial screw motion during the plasticizing process and the idle time due to the cooling cycle are considered as well. The simulation output includes the pressure/throughput behavior, melting profile, temperature development and power consumption. In addition, by the use of different optimization methods and a special correction factor relating to the solid-bed velocity, the obtained results have been compared and verified with suitable experiments.