An enhanced global optimization method based on Gaussian process and its application of warpage control in injection molding

Abstract

In this paper, an adaptive optimization method based on Gaussian process (GP) surrogate model is proposed to minimize the warpage of injection molding parts. GP surrogate model combining design of experiment (DOE) methods is used to build an approximate function relationship between warpage and process parameters, replacing the expensive simulation analysis in the optimization iterations. First, establish an approximation function of the relationship between warpage and process parameters by a small size of design of experiment with GP surrogate model. And then, an enhanced probability improvement criterion is used to determine how additional training samples could be added to optimize the surrogate model. Comparing with expected improvement criterion, proposed enhanced probability improvement criterion can switch to global optima more swiftly. Finally, a front grille molding processing is taken as an example to illustrate the criterion. The results show that the proposed optimization method can effectively decrease the warpage of injection molding parts.