Klute, M. & Heim, H.-P.Digital Twin of Injection Molding: Controlling quality properties of recycled plastics by using self re-training machine learning algorithms2023SPE ANTEC 2023, March 27-30, Denver CO, USADownload
Rehmer, A. & Klute, M. & Kroll, A. & Heim, H.-P.A Digital Twin for Part Quality Prediction and Control in Plastic Injection Molding2023Modeling, Identification, and Control for Cyber-Physical Systems Towards Industry 4.0Download
Rehmer, A. & Klute, M. & Kroll, A. & Heim, H.-P.An internal dynamics approach to predicting batch-end product quality in plastic injection molding using Recurrent Neural Networks2022IFAC-PapersOnLine 6th IEEE Conference on Control Technology and Applications (CCTA) , Vol. 53 , Elsevier , Trieste, ItalyDownload
Rehmer, A. & Kroll, A.Eine Python-Toolbox zur datengetriebenen Modellierung von Spritzgießprozessen und Lösung von Optimalsteuerungsproblemen zur Steuerung der Bauteilqualität202232. Workshop Computational Intelligence, KIT Scientific Publishing , Berlin, DeutschlandDownload
Rehmer, A. & Kroll, A.A Deep Recurrent Neural Network model for affine quasi-LPV System identification2022Proceedings of the 20th European Control Conference (ECC), pp. 566-571, London, UKDownload
Rehmer, A. & Kroll, A.The effect of the forget gate on bifurcation boundaries and dynamics in Recurrent Neural Networks and its implications for gradient-based optimization.2022International Joint Conference on Neural Networks (IJCNN), pp. 01-08, Padova, ItalyDownload
Rehmer, A. & Kroll, A.On affine quasi-LPV System Identification with unknown state-scheduling using (deep) Recurrent Neural Networks.2022IFAC-PapersOnLine Proceedings of the 26th International Conference on System Theory, Control and Computing (ICSTCC), pp. 446-451, Sinaia, RomaniaDownload