In order to predict the shrinkage and mechanical properties of the injection molded parts, a coupled numerical simulation of filling and post-filling process is needed. To simulate the real molding conditions, the effects of phase change and compressibility of the resin were considered in the present investigation. A modified Cross model with either an Arrhenius-type or WLF-type functional form was used for modeling viscosity of the resin. A double-domain Tait equation of state was employed to describe the compressibility of the resin during molding. The energy balance equation including latent-heat dissipation for semi-crystalline materials was solved in order to predict the solidified layer and temperature profile.
Injection molding experiments were carried out using polypropylene(PP) in the present study. Based on the comparison between experiments and simulations, it was found out the predicted pressure distributions and melt front propagations were accurate. Thus it was concluded that the program developed in this study was proved to be useful in simulations of injection molding process.
For production of lightweight parts with good mechanical properties, injection molding of short fiber reinforced polymers is also widely used. The anisotropy caused by the fiber orientation, which is inevitably generated by the flow during injection molding of short fiber reinforced polymers, greatly influences dimensional accuracy, mechanical properties, and other quality of the final product. Since the filling stage of the injection molding process plays a vital role in determining fiber orientation, an accurate analysis of flow field for the filling stage becomes a necessity. Unbalanced filling occurs when a complex or a multi-cavity mold is used leading to development of regions where the fiber suspension is under compression. It is impossible to make an accurate calculation of the flow field during filling with the analysis assuming incompressible fluid.
In this study, a FEM/FDM hybrid scheme with consideration of compressibility and phase change was developed to calculate the flow field. At the moment of complete filling, the three-dimensional fiber orientation field was estimated by solving the equation of orientation change for the second-order orientation tensor with the fourth-order Runge-Kutta method. A mold with four cavities with different filling time was produced to compare the numerical analysis results with the experimental data. There was a good agreement between the experimental and theoretical results when the compressibility of the polymer melt was considered for the numerical simulation. Also, qualitative and quantitative comparisons of fiber orientation states for compressible and incompressible fluids were made.
To predict the shrinkage using numerical simulation, the mathematical model should be simplified to overcome the difficulties of formulation due to non-linearity of problems. It is hard to predict the shrinkage exactly because of the simplification.
In the present work, the neural network is used to predict the shrinkage which can implement complicated nonlinear models very well. Comparison between the result of the neural network and that of the commercial analysis software, ABAQUS, shows that the result of the neural network is in better agreement with that of the experiments.
