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