Multi‐objective optimization of injection molding parameters, based on the Gkriging‐NSGA‐vague method

Abstract

ABSTRACTPlastic production quality, manufacturing cost, and molding efficiency are three important indices for a new product development. In addition to injection molding process parameters (IMPP), runner system also has an important role in the injection molding process. In this study, the plastic production quality, manufacturing costs, and molding efficiency are considered as the optimized objectives. The design parameters include runner diameters and IMPP. The improved Kriging surrogate model (Gkriging), nondominated sorting genetic algorithm (NSGA‐II), and multicriteria fuzzy decision‐making approach (vague sets) are combined, and the Gkriging‐NSGA‐vague scheme is proposed to optimize the runner diameters and the IMPP. Firstly, the Gkriging model is established to map the correlation between design parameters and optimized objectives. Based on the Gkriging model, the NSGA‐II is combined with predictive models to obtain the Pareto‐optimal solutions. Then, the optimal Pareto‐optimal solution is obtained by the vague approach. A multicavity mold with two different plastic parts is utilized as the design case. The optimization results indicate that the Gkriging‐NSGA‐vague method is a powerful method for solving the multi‐objective optimization problems. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 137, 48659.