A Warpage Optimization Method for Injection Molding Using Artificial Neural Network Combined Weighted Expected Improvement

Abstract

Abstract A surrogate-based warpage optimization for injection molding is proposed in this study. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters, i.e., the mold temperature, melt temperature, injection time, packing time, packing pressure, and cooling time are the design variables. The warpage values are reduced by optimizing the process parameters. A new optimization iteration scheme based on artificial neural network (ANN) combined weighted expected improvement (WEI) is employed to speed up the optimization process and to ensure very rapid and steady convergence. The ANN is used to build an surrogate warpage function for the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is executed by the WEI function, which is an infilling sampling criterion. Although the design of experiment (DOE) size is small, this criterion can precisely balance the local and global search and tend to find the global optimal design. As examples, a TV cover and a scanner are investigated. The results show that the proposed approach can effectively reduce the warpage of the injection molding parts.