Process parameters optimization of injection molding of beacon light cover based on orthogonal test and BPNN-GA

Abstract

The molding quality of injection parts was improved by optimizing the process parameters. The beacon light cover was taken as an example, a CAD model was established and the finite element analysis software Moldflow was used for the numerical simulation of injection molding. Combining the orthogonal test design method with numerical simulation of injection molding process, the effect rules of melt temperature, mold temperature, packing pressure and packing time on warpage, which is the key quality characteristics of injection molding, were evaluated comprehensively by the analysis for orthogonal test simulation results. The BP neural network(BPNN) model was trained with simulation data to characterize the relationship between process parameters and warpage, and the genetic algorithm (GA) was used to optimize the injection molding process parameters under the constraint of minimum warpage. The results show that the influence order of each factors on warpage of beacon light cover is melt temperature>packing time>packing pressure>mold temperature, and the melt temperature and packing time are the significant influence factors. The process conditions optimized by the proposed BPNN-GA method were obtained: the melt temperature of 215.01℃, the packing time of 5.31 s, the holding pressure of 50.37 MPa and the mold temperature of 78.76℃. Under this process condition, the maximum warpage of beacon light cover is 1.736 mm by Moldflow simulation and 1.748 mm by BPNN-GA prediction. The two results are in good agreement, indicating that the established neural network model can represent the relation between process parameters and warpage well.