Multi-objective optimization of the fiber-reinforced composite injection molding process using Taguchi method, RSM, and NSGA-II

Abstract

Warpage, volumetric shrinkage, and residual stress are three important indexes for the characterization of the overall quality of composite parts in the short fiber-reinforced composite injection molding process. In addition to process parameters, fiber parameters have an important influence on part quality. In this study, the product warpage, volumetric shrinkage and residual stress are considered as the quality objectives. The design parameters include fiber content, fiber aspect ratio, melt temperature, injection pressure and cooling time. The combined Taguchi, response surface methodology and NSGA-II approach is proposed to optimize the fiber-reinforced composite injection molding process. A case study of a part is presented. On the basis of orthogonal experiment design, Moldflow software is applied in the short fiber-reinforced composite injection molding process. The importance of various parameters on the influence of three quality objectives is researched by the analysis of variance (ANOVA). A combination of parameter settings is determined by the signal-to-noise ratio (S/N). Three response surface models are created on the basis of the simulation results to map the complex nonlinear relationship between design parameters and quality objectives. Based on the response surface model, the relationship between fiber parameters and quality objectives are further discussed. The nondominated sorting genetic algorithm II (NSGA-II) is interfaced with the predictive models to find the optimum design parameter values. The results show that the hybrid method of response surface method (RSM) and NSGA-II is an effective method to solve the multi-objective optimization problem for the quality optimization of fiber-reinforced composite injection molding.