Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact

Injection molding is the most widely used process in manufacturing polymer products. The warpage induced during injection molding process has an important influence on the mechanical performance of injection molded products. Therefore, how to optimize process parameters becomes the key issue in improving the mechanical performance of the product towards the expected service conditions. In this paper, a combined artificial neural network and particle swarm optimization (PSO) algorithm method is proposed to optimize the injection molding process. An integrated finite element analysis of the injection molding process, the warpage-induced residual stresses during assembly, and mechanical performance of serviced product is firstly proposed. A back propagation neural network model is then developed to map the complex nonlinear relationship between process parameters and mechanical performance of the product. The PSO algorithm is interfaced with this predictive model to optimize process parameters and thereby significantly improve the mechanical performance. A case study of vehicle window made of polycarbonate (PC) is presented. Optimum values of process parameters are determined to minimize the maximum von Mises stress within the PC vehicle window under impact loading.