Application of Machine Learning for Prediction and Process Optimization-Case Study of Blush Defect in Plastic Injection Molding

Injection molding is one of the most important processes for the mass production of plastic parts. In recent years, many researchers have focused on predicting the occurrence and intensity of defects in injected molded parts, as well as the optimization of process parameters to avoid such defects. One of the most frequent defects of manufactured parts is blush, which usually occurs around the gate location. In this study, to identify the effective parameters on blush formation, eight design parameters with effect probability on the influence of this defect have been investigated. Using a combination of design of experiments (DOE), finite element analysis (FEA), and ANOVA, the most significant parameters have been identified (runner diameter, holding pressure, flow rate, and melt temperature). Furthermore, to provide an efficient predictive model, machine learning methods such as basic artificial neural networks, their combination with genetic algorithms, and particle swarm optimization have been applied and their performance analyzed. It was found that the basic artificial neural network (ANN), with an average accuracy error of 1.3%, provides the closest predictions to the FEA results. Additionally, the process parameters were optimized using ANOVA and a genetic algorithm, which resulted in a significant reduction in the blush defect area.

Automated summary

Injection molding is a crucial process for mass production of plastic parts. Researchers have been focusing on predicting defects and optimizing process parameters to avoid them. Blush, a common defect near the gate, was studied in this research. Design of experiments, finite element analysis, and ANOVA were used to investigate eight design parameters with impact on blush formation. Machine learning methods including artificial neural networks, their combination with genetic algorithms, and particle swarm optimization were applied for efficient predictive modeling. Among them, basic artificial neural network achieved the closest predictions with an average accuracy error of 1.3%. ANOVA and genetic algorithm were utilized for process parameter optimization, resulting in significant reduction of blush defect area.