Multiobjective optimization of injection molding parameters based on the GEK-MPDE method

Abstract In plastic injection molding (PIM), the process parameters determine the quality and productivity of molded parts. The traditional injection molding process analysis method mainly relies on production experience. It is lack of advanced and rationality and seriously increases production costs. In this paper, a hybrid multiobjective optimization method is proposed to minimize the warpage, volumetric shrinkage and cycle time. The method integrates orthogonal experimental design, numerical simulation, and the metamodel method with multiobjective optimization. The orthogonal experiment chooses seven parameters as the design variables to generate sampling data and determines key factors that affect product quality by the numerical simulation. A gradient-enhanced Kriging (GEK) surrogate model strategy is introduced to construct the response predictors to calculate objective responses in the global design space. Multipopulation differential evolution (MPDE) is conducted to locate the Pareto-optimal solutions, where the response predictors are taken as the fitness functions. This study shows that the proposed GEK-MPDE method can reduce warpage, volumetric shrinkage and cycle time by 5.7 %, 4.7 %, and 18.1 %, respectively. It helps plastic industry to realize collaborative scheduling of multiple tasks between different production lines by providing a low-cost and effective dynamic control method.