Surrogate-based process optimization for reducing warpage in injection molding

Abstract

In this paper, an adaptive optimization method based on Kriging surrogate model is proposed to minimize the warpage of injection molded parts. Kriging surrogate model combining design of experiment (DOE) methods is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by an infilling sampling criterion named expected improvement (EI). This criterion can balance local and global search and tend to find the global optimal design, even though the DOE size is small. As an example, a cellular phone cover is investigated, where mold temperature, melt temperature, injection time, packing time and packing pressure are selected to be the design variables. The results show that the proposed adaptive optimization method can effectively decrease the warpage of injection molded parts.