In the context of a rolling mill case study, this paper presents a methodical framework based on data mining for predicting the physical quality of intermediate products in interlinked manufacturing processes. In the first part, implemented data preprocessing and feature extraction components of the Inline Quality Prediction System are introduced. The second part shows how the combination of supervised and unsupervised data mining methods can be applied to identify most striking operational patterns, promising quality-related features and production parameters. The results indicate how sustainable and energy-efficient interlinked manufacturing processes can be achieved by the application of data mining. © 2013 The Authors.
Lieber, D., Stolpe, M., Konrad, B., Deuse, J., & Morik, K. (2013). Quality prediction in interlinked manufacturing processes based on supervised & unsupervised machine learning. In Procedia CIRP (Vol. 7, pp. 193–198). Elsevier B.V. https://doi.org/10.1016/j.procir.2013.05.033