Double Ensemble Technique for Improving the Weight Defect Prediction of Injection Molding in Smart Factories

Abstract

The growing move toward smart factories can leverage industrial big data to enhance productivity. In particular, research is being conducted on injection molding and utilizing machine learning techniques to analyze molding process data, discover optimal molding conditions, and predict and improve product quality. This study aims to identify the key factors influencing the weight defects of injection-molded products and demonstrate the potential use of the double ensemble technique for better prediction accuracy of weight defects. We obtain the key factors influencing weight defects prediction, barrel H2 temp real, metering time, and fill time using gain ratio analysis. Subsequently, we develop single models using machine learning algorithms, including decision tree, random forest, logistic regression, the Bayesian network, and the artificial neural network. Ensemble models, including bagging and boosting and double ensemble models are developed to compare their performance with that of single models. The findings indicate that ensemble models outperform the prediction accuracy of the single models. The double ensemble technique demonstrates the greatest improvements in prediction accuracy over the single models. These results showcase the potential of applying the double ensemble technique to other injection molding areas and suggest that adopting this technique will contribute to establishing other smart factories that will enhance both productivity and cost competitiveness.