Multi-objective Optimization and Quality Monitoring of Two-piece Injection Molding Products

Halogen detector is an important halogen gas leakage detection instrument. In order to ensure that the upper and lower shells have the same quality, it is necessary to use one mold and two pieces in production. Compared with the conventional one-mold two-cavity process, it is easier to produce warpage and volume shrinkage. To solve this problem, a multi-objective injection molding process optimization method based on deep neural network (DNN) model based on stochastic weight average (SWA) method and multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed. Melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time are the six parameters and important structure parameters (gate diameter) as design variables, warpage, and volume shrinkage rate as the optimization goal. The neural network model between variable and goal was established, and the MOEA/D algorithm was used for global optimization. The multi-objective decision method based on TOPSIS was used to evaluate the Pareto solution set. Finally, the optimized warpage was 0.3031 mm and the volume shrinkage was 5.527%. Compared with the scheme before optimization, the warpage and volume shrinkage are reduced by 0.8259 mm and 4.713%, which shows the effectiveness of the multi-objective optimization method.