  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.
