Multi-objective optimization of GFRP injection molding process parameters, using GA-ELM, MOFA, and GRA-TOPSIS

Abstract

Owing to the influence of the injection molding process, warpage and volume shrinkage are two common quality defects for products manufactured by glass fiber-reinforced plastic (GFRP) injection molding. To minimize these two defects, an extreme learning machine optimized with a genetic algorithm (GA-ELM), multi-objective firefly algorithm (MOFA), and a multi-objective decision-making method (GRA-TOPSIS) were implemented in this study. All of the experiments, based on Latin hypercubic sampling (LHS), were conducted using Moldflow software to obtain the results for warpage and volume shrinkage. The prediction accuracy of the defect-prediction models based on the extreme learning machine (ELM) and GA-ELM algorithms were compared. The results show that the GA-ELM models can better predict the defect values. Finally, MOFA was used to find the Pareto optimal front, and the GRA-TOPSIS method was used to find the optimum solution from the Pareto optimal front. According to the results of the simulation verification, the warpage and volume shrinkage were effectively reduced by 12.25% and 6.11%, respectively, compared with before optimization, which indicates the effectiveness and reliability of the optimization method.