Machine learning for automated quality control in injection moulding manufacturing
This addresses quality control automation for injection molding manufacturers, but it is incremental as it relies on simulated rather than real-world data.
The study tackled the problem of costly labeling for quality control in injection molding by using simulated data to develop a predictive model for product quality, achieving test set accuracy of 99.4%, specificity of 99.7%, and sensitivity of 94.7%.
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.