Simulation of a regression-model and PCA based searching method developed for setting the robust injection molding parameters of multi-quality characteristics

This article proposes an advanced searching method for setting the robust process parameters for injection molding based on the principal component analysis (PCA) and a regression model-based searching method. This method could effectively reduce the influence of environmental noise on molded parts’ multi-quality characteristics in the injection molding process. Firstly, the PCA is utilized to construct a composite quality indicator to represent the quality loss function of multi-quality characteristics. The design of experiment and ANOVA methods are then used to choose the major parameters, which affect parts quality and are called as adjustment factors. Secondly, a two-level statistically designed experiment with the least squared error method was used to generate a regression model between part quality and adjustment factors. Based on this mathematical model, the steepest decent method is used to search for the optimal process parameters. To verify the performance, computer simulations and experiment of the light-guided plate molding were investigated in this work. By comparing the robust qualities using Taguchi method and our proposed method, it is found that our proposed method yields a better uniform production quality.