The combination of physical machines and a suitable simulation environment enables the demonstration and evaluation of various applications and new technologies. This includes in particular the evaluation and demonstration of distributed production control systems and optimizations, which are researched in the group.
The goals of the group are the development of decentralized MES (Manufacturing Execution Systems) and the maintenance of the TIPI Lab to test them in realistic scenarios. A close cooperation with the group DCC enables the use of new highly efficient and high-performance embedded systems, which also enable sophisticated algorithms.
The so-called testbed-as-a-service approach of the TIPI Lab promotes cooperation with partners in research projects as well as in academic and vocational training.
The scientific questions of the MOM group are manifold and include, for example, the evaluation of robustness and flexibility of production processes, the efficiency of distributed control systems and the optimization of production through the use of intelligent self-organizing machines. A particular focus is on questions of information retrieval from manufacturing and suitable development processes for technical MOM platforms. Ultimately, this results in comprehensive IT support for new service-based ecosystems (platform economy, servitization) in manufacturing.
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Publications
2021
Comparison of Production Dynamics Prediction Methods to Increase Context Awareness for Industrial Transport Systems
Philipp Borchers, Dennis Lisiecki, Patrick Eschemann, Linda Feeken, Mehrnoush Hajnorouzi, Ingo Stierand; Modelling and Simulation 2021; 10 / 2021
Einsatz von Künstlicher Intelligenz in der Digitalisierung von Abfallverbrennungskraftwerken
Alexandra Pehlken, Patrick Eschemann, Henriette Garmatter, Fabian Cyris, Astrid Nieße; INFORMATIK 2021 - Computer Science & Sustainability; 09 / 2021
Influence of Material Selection and Product Design on Automotive Vehicle Recyclability
Xiaohui He, Dongmei Su, Wenchao Cai, Alexandra Pehlken, Guofang Zhang, Aimin Wang, Jinsheng Xiao ; Sustainability; 03 / 2021
Optimizing Factory Layouts With Supervised Genetic Algorithm
Patrick Eschemann, Jan Elmar Krauskopf, Dr. Jürgen Sauer, Jan Stefan Zernickel; Modelling and Simulation’2021; 10 / 2021
2020
Impacts of life cycle inventory databases on life cycle assessments: A review by means of a drivetrain case study
Matthias Kalverkamp, Eckard Helmers, Alexandra Pehlken; Journal of Cleaner Production; 10 / 2020
More Sustainable Bioenergy by Making Use of Regional Alternative Biomass?
Alexandra Pehlken, Kalle Wulf, Kevin Greksch, Thomas Klenke, Nina Tsydenova; Sustainability; 09 / 2020
Towards Digital Twins for Optimizing the Factory of the Future
Patrick Eschemann, Philipp Borchers, Linda Feeken, Ingo Stierand, Jan Stefan Zernickel, Martin Neumann; Modelling and Simulation 2020; 10 / 2020
Urban Mining: Applying Digital Twins for Sustainable Product Cascade Use
Prof. Dr. Sabine Baumann, Dr. Alexandra Pehlken; 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC); 09 / 2020
2019
Adaptive manufacturing execution systems (AMES): Best practices for implementations in small and medium-sized businesses
Jonas Kallisch, Frank Oppenheimer; Prodeedings of the 10th Annual European Decision Sciences Conference (EDSI), Nottingham, UK; 06 / 2019
