Quality Prediction Based on Artificial Neural Networks in the Plastic Injection Molding Process

This study proposes a data-driven approach for predicting product quality in plastic injection molding based on actual manufacturing data. The dataset comprises 500 production observations, including 461 non-defective and 39 defective products. The data were divided into training and testing sets using an 80:20 ratio, with SMOTE applied exclusively to the training set to mitigate the substantial class imbalance. Regression analysis was subsequently conducted to identify the most relevant process parameters from five candidate variables. This analysis identified melt end point and melting time as statistically significant predictors, which were then used to develop three classification models: Backpropagation Neural Network (BPNN), Random Forest, and Support Vector Machine. Each model was evaluated over ten experimental repetitions, with Accuracy and AUC serving as the primary performance measures, while Precision, Recall, F1-score, and confusion matrices provided additional evaluation. Across the experiments, BPNN produced the strongest overall predictive performance. The main contribution of this study is the integration of statistically informed feature selection and imbalance treatment with computationally lightweight machine-learning models, demonstrating a practical approach to quality prediction when only a limited amount of real-world manufacturing data is available.