TY - CHAP
T1 - Machine learning for automated quality control in injection moulding manufacturing
AU - Michiels, S.
AU - Schryver, C. De
AU - Houthuys, L.
AU - Vogeler, F.
AU - Desplentere, F.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
U2 - 10.48550/arXiv.2206.15285
DO - 10.48550/arXiv.2206.15285
M3 - Chapter
SN - 9782875870841
T3 - Proc. of the European Symposium on Artificial Neural Networks (ESANN)
SP - 127
EP - 132
BT - Proc. of the European Symposium on Artificial Neural Networks (ESANN)
PB - ArXiv
ER -