This study proposes an artificial intelligence-integrated Six Sigma framework for reducing multiple critical defects in plastic injection molding using real industrial production data from a washing-machine control-panel manufacturing line. Predictive models were developed under severe class imbalance conditions and combined with SHAP-based interpretability to identify the most influential process parameters. A multi-objective NSGA-II optimization strategy was then employed to simultaneously minimize major defect types, including gas-trapped burn (GTB), short shot (SS), sink mark (SK), and flash (FL). The proposed framework was validated through on-site continuous trial production of 300 parts after process stabilization, demonstrating substantial and consistent defect reduction. The results indicate that the integration of data-driven modeling, explainable artificial intelligence, and evolutionary multi-objective optimization provides a practical and scalable approach for quality improvement in industrial injection molding processes.
Köken et al. (Tue,) studied this question.