Leonard Overbeck, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Leonard Overbeck, M.Sc.

Area of Research: 

  • Digital Twins of production systems and learning simulation models
  • Planning and control of agile production systems

 

General Tasks:

  • Lecture integrated production planning in the age of I4.0 (IPP) chapters:
    • Data collection and analysis
    • Concept planning
    • IT systems for the factory of the future

 

Projects:

 

Curriculum Vitae:

since 05/2019 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)
10/2011-04/2018 Study of Industrial Engineering at Karlsruhe Institute of Technology (KIT)

 

Publications

[ 1 ] Greinacher, S.; Overbeck, L.; Kuhnle, A.; Krahe, C. & Lanza, G. (2020), "Multi-objective optimization of lean and resource efficient manufacturing systems", Production Engineering, vol. 14, pp. 165-176. 10.1007/s11740-019-00945-9
Abstract
In the manufacturing industry, target-oriented and efficient use of resources is gaining importance, alongside economic optimization. The economic and organizational optimization of manufacturing systems according to the lean principles is only partly compatible with the goals of resource-efficient manufacturing. Therefore, an approach is sought to improve individual analyses of manufacturing systems. This paper proposes an approach for the multi-objective optimization of lean and resource-efficient manufacturing systems. To predict the dynamic effects of several configurations of manufacturing systems, material, energy, and information flows of a discrete event simulation are coupled with an assessment model, based on objectives of lean and resource-efficient manufacturing. Using design of experiments, Gaussian process meta-models are computed for the behavior of the simulation model. These meta-models allow the approximation of the system behavior to be computed in a short period of time and enable extensive multi-objective optimization and more adequate decision-making support systems. The proposed approach is tested in the metalworking industry.

[ 2 ] Overbeck, L.; Brützel, O.; Stricker, N. & Lanza, G. (2020), "Digitaler Zwilling des Produktionssystems", ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, pp. 62-65. 10.3139/104.112326
Abstract
Ein Digitaler Zwilling eines Produktionssystems kann vielfältig zur Planung, Steuerung und Optimierung genutzt werden. Bislang sind seine Erstellung und Pflege jedoch noch sehr aufwändig, weshalb häufig nicht der reale Zustand der Produktion abgebildet ist bzw. schnell veraltet und der Digitale Zwilling somit nicht mehr effektiv genutzt werden kann. Dieser Beitrag präsentiert ein Konzept für Digitale Zwillinge von Produktionssystemen, die sich selbstständig an die reale Produktion anpassen. Der Digitale Zwilling basiert auf einer Materialflusssimulation, die direkt an die Produktionsdatenbank angeschlossen ist und durch lernende Algorithmen adaptiert wird.

[ 3 ] May, M. C.; Overbeck, L.; Wurster, M.; Kuhnle, A. & Lanza, G. (2020), "Foresighted Digital Twin for situational Agent Selection in Production Control". Elsevier, pp. 27-32.
Abstract
As intelligent Data Acquisition and Analysis in Manufacturing nears its apex, a new era of Digital Twins is dawning. Foresighted Digital Twins enable short- to medium-term system behavior predictions to infer optimal production operation strategies. Creating up-to-the-minute Digital Twins requires both the availability of real-time data and its incorporation and serve as a stepping-stone into developing unprecedented forms of production control. Consequently, we regard a new concept of Digital Twins that includes foresight, thereby enabling situational selection of production control agents. One critical element for adequate system predictions is human behavior as it is neither rule-based nor deterministic, which we therefore model applying Reinforcement Learning. Owing to these ever-changing circumstances, rigid operation strategies crucially restrain reactions, as opposed to circumstantial control strategies that hence can outperform traditional approaches. Building on enhanced foresights we show the superiority of this approach and present strategies for improved situational agent selection.

[ 4 ] Brützel, O.; Overbeck, L.; Nagel, M.; Stricker, N. & Lanza, G. (2020), "Generische Modellierung von halbautomatisierten Produktionssystemen für Ablaufsimulationen", ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, pp. 792-796. 10.3139/104.112450
Abstract
Beschleunigte Produktentwicklungs- und Anpassungszyklen sowie zunehmende Variantenvielfalt veranlassen Unternehmen flexiblere, mitarbeitergebundene Produktionssysteme einzuführen. Die Analyse der komplexen Wechselwirkungen zwischen den Aktionen mehrerer Mitarbeiter innerhalb solcher Systeme kann mit einer Ablaufsimulation erfolgen. Um die Erstellung solcher Simulationsmodelle zu vereinfachen, wurde ein generisches Modellierungskonzept entwickelt, das es erlaubt, durch ereignisdiskrete Materialflusselemente mit agentenbasierten Mitarbeitern solche komplexen Strukturen abzubilden. Der vorliegende Beitrag zeigt die M?glichkeiten für einen beschleunigten Aufbau eines Simulationsmodells zur Bewertung von Verbesserungsmaßnahmen in variablen halbautomatisierten Produktionssystemen.

[ 5 ] Bruetzel, O.; Kueppers, F.; Overbeck, L.; Stricker, N.; Verhaelen, B. & Lanza, G. (2021), "Eine automatisierungsgerechte robuste Produktionsplanung", ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, no. 2, 10.1515/zwf-2021-0009
Abstract
Bedingt durch volatile und neue Rahmenbedingen wird es für Unternehmen wichtiger, ihre Wettbewerbsfähigkeit durch den effizienten Einsatz ihrer Ressourcen im globalen Produktionsnetzwerk abzusichern und verschiedene denkbare Entwicklungen zu berücksichtigen. Hierzu wird ein Verfahren entwickelt, das dies für ein Problem der integrierten Auftragsallokation und Netzwerkkonfiguration ermöglicht. Die Berücksichtigung von Auftragsunsicherheit im entwickelten Verfahren ist heuristisch und basiert auf einer Prognose zukünftig denkbarer Szenarien. Aufbauend auf den szenariospezifischen Lösungen eines linearen Optimierungssystems werden Entscheidungen identifiziert, die die Robustheit der Planung steigern. Diese Entscheidungen werden fixiert und in einem Anwendungsfall bezüglich der ursprünglichen, nicht robusten Planung in verschiedenen Szenarien bewertet.

[ 6 ] Overbeck, L.; Brützel, O.; Teufel, M.; Stricker, N.; Kuhnle, A. & Lanza, G. (2021), "Continuous adaption through real data analysis turn simulation models into digital twins". Procedia CIRP, Elsevier.
Abstract
Digital twins of production systems enable new forms of production control, flexibility and continuous improvement. While off-the-shelf software for discrete-event simulation permits the fast implementation of rough simulation models with sufficient accuracy for project-based analysis, they lack the precision and generality of a digital twin. This paper presents an approach to close the gap between model and reality by continuous and iterative updates enabled by connecting the simulation model to IT systems and smart data analysis. However, handling different databases requires a generative and flexible modelling approach as well as suitable algorithms for probability distribution estimation and control logic identification. The presented approach was validated at a real world example from the automotive industry where an average deviation of output to reality per week of 0.1% was achieved, proving the effectiveness of the approach.

[ 7 ] Overbeck, L.; Hugues, A.; May, M. C.; Kuhnle, A. & Lanza, G. (2021), "Reinforcement Learning Based Production Control of Semi-automated Manufacturing Systems". Procedia CIRP, Elsevier, pp. 170-175.
Abstract
In an environment which is marked by an increasing speed of changes, industrial companies have to be able to quickly adapt to new market demands and innovative technologies. This leads to a need for continuous adaption of existing production systems and the optimization of their production control. To tackle this problem digitalization of production systems has become essential for new and existing systems. Digital twins based on simulations of real production systems allow the simplification of analysis processes and, thus, a better understanding of the systems, which leads to broad optimization possibilities. In parallel, machine learning methods can be integrated to process the numerical data and discover new production control strategies. In this work, these two methods are combined to derive a production control logic in a semi-automated production system based on the chaku-chaku principle. A reinforcement learning method is integrated into the digital twin to autonomously learn a superior production control logic for the distribution of tasks between the different workers on a production line. By analyzing the influence of different reward shaping and hyper-parameter optimization on the quality and stability of the results obtained, the use of a well-configured policy-based algorithm enables an efficient management of the workers and the deduction of an optimal production control logic for the production system. The algorithm manages to define a control logic that leads to an increase in productivity while having a stable task assignment so that a transfer to daily business is possible. The approach is validated in the digital twin of a real assembly line of an automotive supplier. The results obtained suggest a new approach to optimizing production control in production lines. Production control shall be centered directly on the workers' routines and controlled by artificial intelligence infused with a global overview of the entire production system.

[ 8 ] Overbeck, L.; Le Louarn, A.; Brützel, O.; Stricker, N. & Lanza, G. (2021), "Continuous Validation and Updating for High Accuracy of Digital Twins of Production Systems ". Simulation in Produktion und Logistik 2021 , pp. 609-617.
Abstract
Despite continuous improvements in modelling, software tools and data availability, simulation projects of production systems still require a lot of manual effort, expertise in various disciplines and time. In many projects the high initial invest for building the simulation model is followed by a rather short period of experimentation and analysis. As production systems have to be adapted at an increasing pace to respond to rapidly changing markets and business environments, simulation models of these systems become outdated earlier, reducing their useful time window. One way to extend this time window would be the implementation of a method of automated comparison with the current production systems and subsequent self-adaption of the model to reality to maintain and even improve its accuracy over time. This approach will be presented and validated at a real world use case. Such an enhanced simulation model can be called a digital twin of the production system.

[ 9 ] Ruhland, J.; Storz, T.; Kößler, F.; Ebel, A.; Sawodny, J.; Hillenbrand, J.; Gönnheimer, P.; Overbeck, L.; Lanza, G.; Hagen, M.; Tübke, J.; Gandert, J.; Paarmann, S.; Wetzel, T.; Mohacsi, J.; Altvater, A.; Spiegel, S.; Klemens, J.; Scharfer, P.; Schabel, W.; Nowoseltschenko, K.; Müller-Welt, M.; Philip, P.; Bause, K.; Albers, A.; Schall, D.; Grün, T.; Hiller, M.; de Biasi, L.; Ehrenberger, H. & Fleischer, J. (2021), "Development of a Parallel Product-Production Co-design for an Agile Battery Cell Production System". Springer, pp. 96-104.
Abstract
Since current battery cell production lines are not flexible regarding format and material, it is necessary to develop new production systems. It is also required to develop this production line as an agile system in order to be able to flexibly counteract unit-specific capacity fluctuations. In addition, only low scrap rates are allowed when integrating new material systems which requires a holistic in-process or in-line control and the associated quality assurance. Agile produc-tion systems open up new possibilities for developing the battery cell product. Therefore, this article will present a novel product-production co-design that can be specifically adapted to customer requirements.