Optimization of the injection molding process of short glass fiber reinforced polycarbonate composites using grey relational analysis

This paper presents a fast and effective methodology for the optimization of the injection molding process parameters of short glass fiber reinforced polycarbonate composites. Various injection molding parameters, such as filling time, melt temperature, mold temperature and ram speed were considered. The methodology combines the use of the GRA (grey relational analysis) method and a CAE flow simulation software, to simulate the injection molding process and to predict the fiber orientation. This method can replace the traditional “change-one-parameter-at-a-time” approach, which is very inefficient, costly, time consuming and almost impracticable to yield an optimum solution. At the same time, the fiber orientation was examined by CAE simulation to forecast the shear layer thickness, and simultaneously to check the accuracy of the GRA. The results indicated that three distinct layers (a frozen layer, a shear layer and a core layer) are observed from the surface to the core for various injection molding conditions. The fiber orientation is perpendicular to the melt flow direction in the frozen layer and the core layer, but it has the opposite direction in the shear layer. From the CAE analysis, the optimum process parameters to obtain the thickest shear layer are found, which is the target of the present research.