An Innovative Surrogate-Based Searching Method for Reducing Warpage and Cycle Time in Injection Molding

Abstract

This paper presents a hybrid multiobjective optimization system for minimizing the warpage and cycle time of the plastic injection molding process. During optimization, an innovative adaptive Kriging surrogate modeling strategy is applied to substitute the computationally intensive numerical simulation of the injection molding process. The system consists of two main stages. In the first stage, the Taguchi optimization method, the analysis of variance, and the signal-to-noise ratio are employed to find the significant process parameters. The most influential factors’ domains will be divided into two subregions equally in the next step. The Kriging surrogate model is developed to obtain the mathematical relationship between the warpage and process parameters based on the fractional factorial design. A parallel efficient global optimization, named subregional efficient global optimization (SEGO), is proposed. SEGO algorithm is continued till the convergence accuracy is satisfied, and the sufficiently accurate Kriging surrogate model is established. In the second stage, the nondominated sorting-based genetic algorithm II is used to search for the pareto-optimal solutions for two objectives. Numerical result shows that the pareto-frontier is well identified with a small number of simulation runs. The proposed approach can help engineers obtain optimal process conditions and achieve competitive advantages of product quality and efficiency.