Research on the Injection Mold Design and Molding Process Parameter Optimization of a Car Door Inner Panel

Abstract

Numerical simulation of the injection molding of the inner panel of the car door and the optimization of its process parameters were completed. The inner panel and its injection mold were designed by UG software and simulated by the filling and cooling module of the Moldflow software. Different gate schemes were selected to compare quality indicators such as filling time, air pockets, and weld lines to obtain the optimal number of gates. We established the objective function model with gate position as the independent variable, and used a multi-population genetic algorithm to solve the optimal position of the function model to get the best gate position. The mold temperature, melt temperature, cooling time, holding pressure, and holding time were selected as the influencing factors, and volume shrinkage and warpage deformation were selected as the evaluation indicators to design and complete the orthogonal test. The test data were simulated by Moldflow, and the optimal combination of process parameters was determined by range and variance analysis. The BP neural network model related to the molding process parameters, volume shrinkage, and warpage deformation was built, and the trained network model was optimized with the ant colony algorithm. The optimal parameter combination was: mold temperature 76 $°C$ , melt temperature 205 $°C$ , cooling time 23.8s, holding pressure 54.7Mpa, and holding time 22.1s. The simulation results showed that volume shrinkage was 13.32% and warpage deformation was 4.315 mm. The design of an injection mold for the car door inner panel was completed and its molding process parameters were optimized.