Carmen Krahe, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Carmen Krahe, M.Sc.

Forschungs- und Aufgabengebiete:

  • Produkt-Produktions-Codesign
  • Maschinelles Lernen auf 3D-Modellen
  • Industrie 4.0
  • Produktionsplanung und -steuerung
  • Steuerung flexibler Produktionssysteme

Allgemeine Aufgaben:

  • Vorlesungsbetreuer der Vorlesung Integrierte Produktionsplanung im Zeitalter von I4.0 (IPP)

    • Detailplanung: Produktionsplanung und -steuerun

  • Übungskoordination IPP


  • AIAx – Artificial Intelligence goes CAx
  • MoSyS - Menschorientierte Gestaltung komplexer System of Systems


seit 11/2018 Wissenschaftliche Mitarbeiterin am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT)
10/2011 - 10/2018 Studium des Wirtschaftsingenieurwesens am Karlsruher Institut für Technologie (KIT)
05/04/1993 Geboren in Karlsruhe



[ 1 ] Hofmann, C.; Brakemeier, N.; Krahe, C.; Stricker, N. & Lanza, G. (2018), „The Impact of Routing and Operation Flexibility on the Performance of Matrix Production Compared to a Production line“. Advances in Production Research, Hrsg. Schmitt, R. & Schuh, G., S. 155-165.
An increasing number of product variants and a decrease in demand certainty challenge manufacturing companies. Lean, flow-oriented production lines are best-practice to assure efficient production in a predictable environment. However, with the increase in complexity and uncertainty, more flexible production systems such as matrix production currently receive much attention. Having neither a common takt time nor a rigid linkage, they offer new degrees of freedom regarding process order and machine choice. This paper contributes to answering the question under which conditions a matrix production is favourable compared to a production line. To answer this question, the effects of MTTF and MTTR as driving factor to choose a matrix production over a production line are analysed. Regarding the material flow in the matrix, the benefits of routing flexibility and operation flexibility concerning throughput time, tardiness and output of the matrix production are evaluated. The results show that a rule based approach has its limits especially regarding the exploitation of operation flexibility. For low levels of routing flexibility, the rule based approach tends to generate sup-optimal solutions due to a lack of coordination between the agents.

[ 2 ] Lanza, G.; Klenk, F. & Krahe, C. (2019), „Track & Trace als Basis für die Kreislaufwirtschaft in der Automobilindustrie“, Werkstoffe in der Fertigung, S. 22-23.
Die Kreislaufwirtschaft zielt darauf ab, den aktuellen „take, make & dispose“-Ansatz der linearen Wirtschaft, der Auslöser massiver Ressourcenverschwendung ist, durch die mehrfache, wertschöpfende Zufuhr von Produkten in den Produktionsprozess zu verändern. Hierdurch kann der Ressourcen- und Energieverbrauch reduziert sowie die Wirtschaftlichkeit der Unternehmen erhöht werden. Eine Voraussetzung für die Implementierung der Kreislaufwirtschaft ist die lückenlose Nachverfolgbarkeit von Produkten, Komponenten und Materialien. So wird ermöglicht, dass Produkte, die am Ende ihres Lebenszyklus wieder in den Besitz eines Unternehmens gelangen, leichter identifiziert, aufgearbeitet und weiterverwendet werden können. Der vorliegende Artikel zielt darauf ab, eine Übersicht aktueller Track & Trace-Technologien zu geben und die Möglichkeiten aufzuzeigen, wie mittels einer zentralen Austauschplattform ein Mehrwert aus Daten generiert werden kann.

[ 3 ] Greinacher, S.; Overbeck, L.; Kuhnle, A.; Krahe, C. & Lanza, G. (2020), „Multi-objective optimization of lean and resource efficient manufacturing systems“, Production Engineering, Band 14, S. 165-176. 10.1007/s11740-019-00945-9
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.

[ 4 ] Kandler, M.; Lanza, G. & Krahe, C. (2020), „Development of a Human-centered Industry 4.0 Philosophy“. New Developments in Sheet Metal Forming, S. 123-135.
Industry 4.0 is increasingly finding its way into production. New technologies and instruments are increasingly changing production processes and, in particular, production work. New instruments to support employees make it possible to increase labour productivity, which is a key factor contributing to economic growth. While at the beginning the benefits were unclear and the investment strategy was missing, today the lack of acceptance and unsuitable organizational structures in the companies are the biggest obstacles. This is reminiscent of the first failure of Lean Management. Only after several years with the anchoring of Lean principles as a philosophy and a sustainable improvement culture, Lean Management was successful. What is needed is an approach that involves employees in incremental process improvements using Industry 4.0 in order to support the acceptance. To develop this approach, the respective success factors of Lean Management and Industry 4.0 are first studied and then combined to form an integrated process optimization approach. The result is a human-centred optimization approach that promotes an increased acceptance of Industry 4.0 and supports employees in coping with future flexible work content.

[ 5 ] Krahe, C.; Kalaidov, M.; Doellken, M.; Gwosch, T.; Kuhnle, A.; Lanza, G. & Matthiesen, S. (2021), „AI-Based Knowledge Extraction for Automatic Design Proposals Using Design-Related Patterns“. Procedia CIRP, S. 397-402. 10.1016/j.procir.2021.05.093
Engineering competence and the digitization of all processes along the product development process are highly decisive for today's success of industrial companies. The design process is very individual and strongly based on design engineers' experience. Part of this knowledge and the result of the design approach are fixated in the existing variations of the product generations, but are difficult to extract and to formalize. Conclusions about design-related patterns between products of different generations or variants can be drawn from the model tree representing the design engineer?s thinking process for each individual CAD model. However, the model tree has hardly been used so far. The aim of this paper is to examine whether there exist any common design patterns between CAD models of certain component classes by the exemplary use case in the area of mechanical engineering. To identify patterns and to extract knowledge out of complex data sets, Machine Learning (ML), especially Deep Learning, has proven an immense capability. Finally, based on the learned patterns, meaningful next design steps are to be proposed in the form of an assistance system. The results show that there exist common design patterns for various classes of components. It is illustrated on an exemplary component class that those patterns can be used to train an assistance system based on Recurrent Neural Networks (RNNs). The corresponding design patterns were extracted from data of an industrial application partner. By transferring these design patterns to the development of new product generations or variants, on the one hand the design process itself and thus the time to market can be shortened. On the other hand, the knowledge from previous product generations contained in those patterns can be preserved. For further research the design patterns of CAD models extracted by ML algorithms is a contribution to faster knowledge extrapolation.