Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, eXtreme Gradient Boosting (XGBoost), and genetic algorithms. Computer-aided engineering (CAE) simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and the strengthen elitist genetic algorithms (SEGA) was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold's digital data.