Rule-based explanations based on ensemble machine learning for detecting sink mark defects in the injection moulding process

Abstract

Manufacturing quality control (QC) in plastic injection moulding is of the upmost importance since almost one third of plastic products are manufactured via the injection moulding process. Moreover, smart manufacturing technologies are enabling the generation of huge amounts of data in production lines. This data can be used for predicting the quality of manufactured plastic products using machine learning methods, allowing companies to save costs and improve their production efficiency. However, high-performance machine learning models are usually too complicated to be understood by human intuition. Therefore, we have introduced a rule-based explanations (RBE) framework that combines several machine learning interpretation methods to help to understand the decision mechanisms of accurate and complex predictive models – specifically tree ensemble models. These generated rules can be used to visually and easily understand the main factors that affect the quality in the manufacturing process. To demonstrate the applicability of RBE, we present two experiments with real industrial data gathered from a plastic injection moulding machine in a Singapore model factory. The collected datasets contain condition data for several manufacturing processes as well as the QC results for sink mark defects in the production of small plastic products. The experiments revealed that it is possible to extract meaningful explanations in the form of simple decision rules that are enhanced with partial dependence plots and feature importance rankings for a better understanding of the underlying mechanisms and data relationships of accurate tree ensembles.