Professorship for Manufacturing – Innovative Manufacturing
Organisational unit: Professoship
Main research areas
The professorship for manufacturing technology – which takes the form of a shared professorship with the Helmholtz-Zentrum hereon GmbH – covers a wide spectrum of manufacturing processes, both in regard to research and in regard to teaching. In doing so, research work focuses primarily on forming technology and the integration of additional manufacturing technologies, such as additive manufacturing. In terms of research and teaching, the key specialisations of this group are light construction and the conservation of resources, the adaptation of process- and material-induced product properties, components and processes for electric mobility, Industry 4.0, and networked and digitalised processes. These fields are researched from a manufacturing technology point of view and furthered through cooperations with other disciplines.
What’s more, one of the key activities carried out by this professorship is the transfer of this knowledge into regional, national and international industry. In addition, the professorship represents Leuphana University and the Helmholtz-Zentrum hereon GmbH national and international committees for manufacturing technology, such as the AGU (German Metal Forming Association) or CIRP (the International Academy for Production Engineering).
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Dynamic Lot Size Optimization with Reinforcement Learning
Voss, T., Bode, C. & Heger, J., 01.01.2022, Dynamics in Logistics : Proceedings of the 8th International Conference LDIC 2022, Bremen, Germany. Freitag, M., Kinra, A., Kotzab, H. & Megow, N. (eds.). Cham: Springer Science and Business Media B.V., p. 376-385 10 p. (Lecture Notes in Logistics).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Formwerkzeug und Verfahren zum Strangpressen von metallischen Werkstoffen
Ben Khalifa, N., Isakovic, J. & Bohlen, J., 17.02.2022, IPC No. B21C 25/02, Deutsches Patent- und Markenamt, Patent No. 102020128163, 27.10.2020Research output: Patents › Patent
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Dynamische Losgrößenoptimierung mit bestärkendem Lernen
Voß, T., Bode, C. & Heger, J., 2021, In: ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb. 116, 11, p. 815-819 5 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Experimental Setup of Dieless Drawing Process for Magnesium Wire
Braatz, M., Dieckmann, A., Bohlen, J. & Ben Khalifa, N., 05.09.2021, Production at the Leading Edge of Technology: Proceedings of the 11th Congress of the German Academic Association for Production Technology (WGP), Dresden 2021. Behrens, B.-A., Brosius, A., Drossel, W.-G., Hintze, W., Ihlenfeldt, S. & Nyhuis, P. (eds.). Cham: Springer Nature Switzerland AG, p. 38-46 9 p. (Lecture Notes in Production Engineering; vol. Part F1160).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Geometrical Accuracy in Two-Stage Incremental Sheet Forming with Active Medium
Thiery, S. & Ben Khalifa, N., 2022, Production at the Leading Edge of Technology: Proceedings of the 11th Congress of the German Academic Association for Production Technology (WGP), Dresden, September 2021. Behrens, B.-A., Brosius, A., Drossel, W.-G., Hintze, W., Ihlenfeldt, S. & Nyhuis, P. (eds.). Cham: Springer, p. 65-74 10 p. (Lecture Notes in Production Engineering; vol. Part F1160).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Entscheidungsbäume und bestärkendes Lernen zur dynamischen Auswahl von Reihenfolgeregeln in einem flexiblen Produktionssystem
Heger, J., Zein El Abdine, M., Sekar, S. & Voß, T., 26.08.2021, Simulation in Produktion und Logistik 2021: Erlangen, 15. - 17. September 2021. Franke, J. & Schuderer, P. (eds.). Göttingen: Cuvillier Verlag, p. 337-346 10 p. (ASIM-Mitteilung; vol. AM 177).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Flexible Manufacturing of Concave–Convex Parts by Incremental Sheet Forming with Active Medium
Thiery, S. & Ben Khalifa, N., 2021, Forming the Future: Proceedings of the 13th International Conference on the Technology of Plasticity. Daehn, G., Cao, J., Kinsey, B., Tekkaya, E., Vivek, A. & Yoshida, Y. (eds.). Cham: Springer, p. 549-561 13 p. (The Minerals, Metals and Materials Series).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Dynamically adjusting the k-values of the ATCS rule in a flexible flow shop scenario with reinforcement learning
Heger, J. & Voss, T., 2023, In: International Journal of Production Research. 61, 1, p. 147-161 15 p.Research output: Journal contributions › Journal articles › Research › peer-review
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Outperformed by a Computer? - Comparing Human Decisions to Reinforcement Learning Agents, Assigning Lot Sizes in a Learning Factory: 11th Conference on Learning Factories, CLF2021
Voß, T., Rokoss, A., Maier, J. T., Schmidt, M. & Heger, J., 06.2021, Rochester: Elsevier Inc., 6 p. (SSRN).Research output: Working paper › Working papers
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Co-extrusion of compound-cast AA7075/6060 bilayer billets at various temperatures
Chen, H., Giannopoulou, D., Greß, T., Mittler, T., Isakovic, J., Volk, W. & Ben Khalifa, N., 2021, Forming the Future - Proceedings of the 13th International Conference on the Technology of Plasticity. Daehn, G., Cao, J., Kinsey, B., Teekaya, E., Vivek, A. & Yoshida, Y. (eds.). Cham: Springer International Publishing AG, p. 993-1001 9 p. (Minerals, Metals and Materials Series).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review