Machine learning for automated quality control in injection moulding manufacturing

TL;DR

This paper demonstrates that machine learning models trained on simulated data can accurately predict product quality in injection moulding, highlighting the potential for automated quality control in manufacturing.

Contribution

It introduces a predictive model trained on simulated data for injection moulding quality control, showing high accuracy and encouraging real-world data application.

Findings

Achieved 99.4% accuracy on test data

High specificity and sensitivity in predictions

Potential for ML-based automated QC in manufacturing

Abstract

Machine learning (ML) may improve and automate quality control (QC) in injection moulding manufacturing. As the labelling of extensive, real-world process data is costly, however, the use of simulated process data may offer a first step towards a successful implementation. In this study, simulated data was used to develop a predictive model for the product quality of an injection moulded sorting container. The achieved accuracy, specificity and sensitivity on the test set was $99.4\%$, $99.7\%$ and $94.7\%$, respectively. This study thus shows the potential of ML towards automated QC in injection moulding and encourages the extension to ML models trained on real-world data.