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M.Sc. Philipp Gönnheimer

Research Associate
department: Machines, Equipment and Process Automation
office hours: to be agreed
room: Raum 015, Geb. 50.36
phone: +49 1523 9502578
Philipp GoennheimerQho0∂kit edu

76131 Karlsruhe
Kaiserstraße 12

M.Sc. Philipp Gönnheimer

Area of Research:

  • Digital Twins and information models for machines and production systems
  • Modular concepts for production equipment
  • Industry 4.0


General Tasks:



  • I4TP - Sino-German Industry 4.0 Factory Automation Platform
  • ROBOTOP - Modular, open and internet-based platform for robot applications in industry and service


Curriculum Vitae:

since 01/2018 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)


[ 1 ] Barton, D.; Gönnheimer, P.; Qu, C. & Fleischer, J. (2018), "Self-describing connected components for live information access within production systems". 4th International Conference on System-Integrated Intelligence: Intelligent, Flexible and Connected Systems in Products and Production, eds. Denkena, B.; Thoben, K. & Trächtler, A., pp. 250-257.
Access to data from components in production systems is potentially an enabler for various data-based approaches. This paper presents a practical approach to transform mechanical components into self-describing cyber-physical systems connected within a local network. The requirements for typical use cases are analysed and a modular cyber-physical connector is proposed. The data is collected by a central OPC UA client and fed into a web-based visualisation, so that it is easily accessible for operators, maintenance staff, and other stakeholders. The approach is illustrated for components with two different levels of complexity.

[ 2 ] Barton, D.; Gönnheimer, P.; Schade, F.; Ehrmann, C.; Becker, J. & Fleischer, J. (2019), "Modular smart controller for Industry 4.0 functions in machine tools". Procedia CIRP, eds. Butala, P.; Govekar, E. & Vrabič, R., pp. 1331-1336.
In machine tools, Industry 4.0 functions can increase availability through predictive maintenance, while other functions improve productivity and workpiece quality through process supervision and optimisation. Many of these functions rely on data communication between systems from different suppliers. Requirements regarding latency and computing vary widely depending on the application. Based on an analysis of these requirements, a smart controller for the implementation of Industry 4.0 is designed, using a hypervisor to allow for the integration of soft real-time and best-effort applications.