Multiobjective Optimization Method for Polymer Injection Molding Based on a Genetic Algorithm

Abstract

To solve the quality problem of polymer injection parts, a quality prediction and multiobjective optimization method is established. In this method, the parameters that have an important effect on the part quality are selected using an orthogonal testing method, and then a central composite design experiment is performed using these parameters. A mathematical model considering an objective and impact factors is developed using the response surface method. The optimal combination of the impact parameters is determined using a multiobjective genetic algorithm. The injection molding of a typical interior trim part of a car, i.e., the seat belt cover plate, is used as an example to demonstrate the method. The two most troublesome problems in this process—the sink marks and warpage—are multiobjectively analyzed using the established method, and the optimal combination of impact parameters that minimized the defects is determined. The errors of the sink marks and warpage between the experimental and theoretical values were 7.95% and 0.2%, respectively. The optimized parameters were tested in actual injection molding. The results show that the shrinkage and warpage of the parts are obviously improved by optimization using the proposed method, allowing the parts to satisfy the requirements of assembly and appearance.