Jan-Philipp Kaiser, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Jan-Philipp Kaiser, M.Sc.

Forschungs- und Arbeitsgebiete:

  • Qualitätssicherung im Remanufacturing mittels optischer Messtechnik


Allgemeine Aufgaben:

  • Praktikum Produktionsintegrierte Messtechnik
    • Robotergestützte Messtechnik
    • Koordinatenmesstechnik
  • Lernfabrik Globale Produktion Modul 3 – Qualitätsmanagement (Six-Sigma)



  • AgiProbot
  • DemoBat



seit 11/2019

Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT) 


Studium des Maschinenbaus am Karlsruher Institut für Technologie (KIT)


[ 1 ] Wurster, M.; Exner, Y.; Kaiser, J.; Stricker, N. & Lanza, G. (2021), „Towards planning and control in cognitive factories - A generic model including learning effects and knowledge transfer across system entities“. Procedia CIRP, Elsevier, Amsterdam, S. 158-163. 10.1016/j.procir.2021.10.025
Cognitive abilities allow robots to learn and reason from their environment. The gained knowledge can then be incorporated into the robot’s actions which in turn affect the environment. Therefore, a cognitive robot is no longer a static system that performs actions based on a pre-defined set of rules but a complex entity that dynamically adjusts over time. With this, challenges arise for production systems that need to observe and ideally anticipate the cognitive robot’s behavior. Often, digital twins are employed to test and optimize production control systems. This paper presents a generic approach to characterize, model and simulate learning processes and formalized knowledge in hybrid production systems assuming different station types with learning effects. Thereby, quantitative and qualitative learning processes are mapped including knowledge sharing and transfer across entities. A modular and parameterizable design enables the adjustment to different use cases. Eventually, the model is instantiated as a digital twin of a real production system for product disassembly employing cognitive-autonomous robots among human operators and rigidly automated machines. The model shows great potential to be integrated into test beds for planning and control systems of cognitive factories.

[ 2 ] Kaiser, J.; Mitschke, N.; Stricker, N.; Heizmann, M. & Lanza, G. (2021), „Konzept einer automatisierten und modularen Befundungsstation in der wandlungsfähigen Produktion“, Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), Band 116, Nr. 5, S. 313-317.
Inspektionsprozesse werden im Remanufacturing auch heute noch vorwiegend manuell durchgeführt, da die Einschätzung des Qualitätszustands von rückläufigen Gebrauchtprodukten komplex und damit schwer zu automatisieren ist. Dies ist darauf zurückzuführen, dass Abnutzungsgrade, Deformationen und Schädigungen eine individuelle Bewertung des Gebrauchtprodukts nach sich ziehen und somit schwer standardisierbar sind. In diesem Beitrag werden die Anforderungen an ein System für die Bewältigung der Herausforderung der automatisierten Inspektion im Remanufacturing abgeleitet. Darauf aufbauend wird das Konzept einer Befundungsstation, welches diese Anforderungen erfüllt, präsentiert und Anwendungsfälle im Rahmen des von der CarlZeiss-Stiftung geförderten Forschungsprojekts „AgiProbot - Agiles Produktionssystem mittels mobiler, lernender Roboter mit Multisensorik bei ungewissen Produktspezifikationen“ vorgestellt.

[ 3 ] Kuhnle, A.; Kaiser, J.; Theiß, F.; Stricker, N. & Lanza, G. (2021), „Designing an adaptive production control system using reinforcement learning“, Journal of Intelligent Manufacturing, Band 32, S. 855?876. 10.1007/s10845-020-01612-y
Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are "black box" approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited.

[ 4 ] Kaiser, J.; Becker, S. N.; Wurster, M.; Stricker, N. & Lanza, G. (2021), „Framework for simulation-based Trajectory Planning and Execution of Robots equipped with a Laser Scanner for Measurement and Inspection“. 9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19, S. 292-297. DOI: 10.1016/j.procir.2021.10.047

[ 5 ] Kaiser, J.; Bolender, M.; Eschner, N. & Lanza, G. (2021), „View Planning im Remanufacturing“, wt Werkstattstechnik online, Band 111, Nr. 11, S. 781-785. DOI 10.37544/1436–4980–2021–11–12–11
Eine Automatisierung der Inspektion im Remanufacturing bietet die Möglichkeit, Kostenpotenziale zu erschließen. Dies lässt sich mittels robotergeführter optischer Messsysteme erreichen. In diesem Beitrag werden bestehende Ansätze vorgestellt und Herausforderungen für View-Planning-Ansätze diskutiert, welche sich aus den Besonderheiten des Remanufacturing heraus ergeben. Gleichzeitig werden Lösungsansätze für diese Problemstellungen an einem beispielhaften Anwendungsfall aufgezeigt.

[ 6 ] Lanza, G.; Asfour, T.; Beyerer, J.; Deml, B.; Fleischer, J.; Heizmann, M.; Furmans, K.; Hofmann, C.; Cebulla, A.; Dreher, C.; Kaiser, J.; Klein, J.; Leven, F.; Mangold, S.; Mitschke, N.; Stricker, N.; Pfrommer, J.; Wu, C.; Wurster, M. & Zaremski, M. (2022), „Agiles Produktionssystem mittels lernender Roboter bei ungewissen Produktzuständen am Beispiel der Anlasser-Demontage“, at - Automatisierungstechnik, Band 70, S. 504-516. 10.1515/auto-2021-0158
Agile production systems combine a high degree of flexibility and adaptability. These qualities are particularly crucial in an environment with high uncertainty, for example in the context of remanufacturing. Remanufacturing describes the industrial process of reconditioning used parts so that they regain comparable technical properties as new parts. Due to the scarcity of resources and regulatory requirements, the importance of remanufacturing is increasing. Due to the unpredictable component properties, automation plays a subordinate role in remanufacturing. The authors present a concept how automated disassembly can be achieved even for components of uncertain specifications by using artificial intelligence. For the autonomous development of disassembly capabilities, digital twins are used as learning environments. On the other hand, skills and problem-solving strategies are identified and abstracted from human observation. To achieve an efficient disassembly system, a modular station concept is applied, both on the technical and on the information technology level.

[ 7 ] Wurster, M.; Klein, J.; Kaiser, J.; Mangold, S.; Furmans, K.; Heizmann, M.; Fleischer, J. & Lanza, G. (2022), „Integrierte Steuerungsarchitektur für ein agiles Demontagesystem mit autonomer Produktbefundung“, at - Automatisierungstechnik, Band 70, S. 542-556. 10.1515/auto-2021-0157
Competitive remanufacturing of used products with uncertain conditions requires a high degree of flexibility and responsiveness. This article describes an integrated control architecture for a modular, agile disassembly system with autonomous product inspection and learning production resources. The approach includes a material flow control and vertically-integrated sub-architectures to control the station and intralogistics operations.