Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution

Injection molding (IM) is one complex manufacturing process characterized by nonlinear behavior. Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. Artificial neural networks (ANNs) specifically have demonstrated exceptional performance in problems involving nonlinear modeling. This work will employ complete factorial design of experiments (DoE) to acquire a dataset which is both resilient and suitable for training, validation, and testing purposes. The predictive model demonstrated outstanding performance throughout the training, validation, and test sets. The aggregate R2 values for the training, validation, and tests datasets were 97.58%, 93.76%, and 91.31%, respectively, demonstrating a strong ability to accurately foresee outcomes. Differential evolution (DE) successfully achieved a 2% decrease in weight and a notable 14% decrease in energy consumption. The results indicate that combining an ANN with DE is a viable approach for enhancing injection molding parameters, especially in scenarios with multiple objectives.