Process optimization and effects of material properties on numerical prediction of warpage for injection molding

This study aims to illustrate the importance of accurate material property data in the numerical simulation of injection molding by elucidating the effects of variation in material properties on warpage prediction. In addition, it presents an adaptive optimization method based on the Kriging surrogate model that has been developed to intelligently determine the optimal processing parameters for minimizing warpage in a box-shape part with different and stepwise wall thicknesses. The Kriging surrogate model is a statistics-based interpolation technique that provides the approximate functional relationship between the performance variables of interest, like warpage, and the factors affecting them, such as the processing parameters. Based on optimal processing parameters, the sensitivity of warpage prediction to two of the most important material properties - shear viscosity and the pvT (pressure-specific volume-temperature) relationship - were analyzed. It is well known that the material property model constants used for numerical simulation can sometimes vary significantly due to inherent experimental measurement errors, the resolution of the testing device, and/or the way the curve fitting is performed to determine the model constants. The results showed that hypothetical and yet reasonable variations in the shear viscosity model constants and in the pvT relationship could significantly affect the magnitude of warpage prediction.