Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD
M. Hanitz, M. Schöne, T. Voigt, M. Kohlhase, in: P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition, MLDM 2022, ibai-publishing, Leipzig, 2022, pp. 121–135.

Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, M. Kuhn, in: H. Yin, D. Camacho, P. Tino (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings, Springer International Publishing, Cham, 2022, pp. 379–390.

Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge
T. Voigt, M. Kohlhase, O. Nelles, Mathematics 9 (2021).

Space-Filling Designs for Experiments with Assembled Products
T. Voigt, M. Schöne, M. Kohlhase, O. Nelles, M. Kuhn, in: 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, New York, NY, USA, 2021, pp. 192–199.

Advanced Data Analytics Platform for Manufacturing Companies
T. Voigt, N. Migenda, M. Schöne, D. Pelkmann, M. Fricke, W. Schenck, M. Kohlhase, in: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, pp. 01–08.

Variational Autoencoder based Novelty Detection for Real-World Time Series
L. Steinmann, N. Migenda, T. Voigt, M. Kohlhase, W. Schenck, in: 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, New York, NY, USA, 2021, pp. 1–7.

Incremental Latin Hypercube Additive Design for LOLIMOT
T. Voigt, M. Kohlhase, O. Nelles, in: Institute of Electrical and Electronics Engineers (IEEE) (Ed.), 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2020, pp. 1602–1609.

Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels
T. Voigt, M. Kohlhase, A. Peter, atp magazin 62 (2020) 62–69.

Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs
T. Voigt, M. Kohlhase, O. Nelles, in: Proceedings - 29. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, 2019, pp. 267–288.

Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern
T. Voigt, M. Kohlhase, in: Proceedings - 28. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, 2018, pp. 93–111.