Abstract:

Injection molding is the most widely used process for producing plastic products.In general, injection processing can be divided into three stages: filling, packing and cooling，in which warpage defect is one of the most important quality problems.Since the quality of the injection-molded parts are mostly influenced by process conditions, how to determine the optimum process conditions to reduce warpage becomes the key to improving the quality of parts.In this study, the mold temperature, melt temperature, injection time, packing time, packing pressure and cooling time were regarded as process parameters(design variables).Moldflow Plastic Insight software was used to analyze the warpage of the injection molding parts.BP neural network model was used to construct an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations.The adaptive process was performed by improved Expected Improvement(EI)which was a weighted infill sample criterion.This criterion could balance local and global search and tend to find the global optimal design.Numerical results showed that the proposed adaptive optimization method could effectively reduce the warpage of the injection molding parts.