Author: jobregon

Project Synopsis
Title:	Rule-based explanations based on ensemble machine learning for detecting sink mark defects in the injection moulding process
Highlights:	Ensemble learning models can predict the quality in the injection molding process. Rule-based explanations are developed for interpreting the ensemble models. The method generates decision rules and visualizes them with PDP and ICE plots.
Period:	August 2020- July 2021
Outputs:	Article published in the international Journal of Manufacturing Systems
Skills & Technologies:	Ensemble learning Explainable Machine Learning Injection Molding Process Academic writing

Project details

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.