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TU Berlin

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Prof. Dr.-Ing. Franz Dietrich


Office: PTZ 303
Tel.: +49 (0)30/314-22014
Fax: +49 (0)30/314-22759
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Scientific career

2018 Professor and head of the department of assembly and handling technology (successor em. Prof. Günther Seliger), TU Berlin

2017 TU Berlin: Call to TU Berlin

2013 Head of the research group "Assembly and Production Automation", Institute of Machine Tools and Production Technology, TU Braunschweig, with active contribution to the research campus OpenHybrid LabFactory, Wolfsburg to the research center BatteryLabFactory Braunschweig

2013 Promotion to Dr.-Ing. with the research topic "Nonlinear Modelling of Hydraulically Actuated Production Machines Using Optimized Experiments", TU Braunschweig

2005 Diplom mechanical engineering (mechatronics & microsystems technology), Karlsruhe Institute of Technology KIT (former University Karlsruhe (TH)), with studies in England and at the University of Bremen

  • Chairman and organizer of the 7th International CIRPe Web Conference 2019
  • Research Affiliate of the International Academy of Production Engineering (CIRP)
  • Winner of the science award of the Heribert-Nasch-Foundation
  • Guest lectures at the Tongji-University, Shanghai, und Singapore Institute of Manufacturing Technology (SIMTECH), Singapur
  • Scientific advisor in the EXIST-project FormHand (now FormHand GmbH, Braunschweig)
  • Involved in over 70 scientific publications and several patents

Contact me for a full CV.

Scientific interests

  • Dynamised production with utilization of user-centred means of intervention and design thinking models
  • Handling technology, robotics, systems technology and control technology for production automation

    • Human-Robot collaboration
    • Control technology for robots, process automation and command levels
    • Modeling, control, trajectory generation
    • Robot controlled additive production
    • Machine concepts, multi-purpose gripper and end effectors
    • Micro assembly, precision assembly, high speed assembly
    • Lab automation and packaging technology (pharamaceutics and bio technology)

  • Process automation, linking and stacking technology for batteries and fuel cells
  • Automation for production process chains in lightweight construction / multi-material-components / in additive production
  • Handling technology for flexible transfer, intra-logistics and commissioning
  • Handling technology, assembly and disassembly in the context of sustainability and energy efficiency
  • New forms of engineer training, i.e. with augmented reality and maker spaces
  • Augmented reality for qualification and productivity increase in assembly and logistics
  • Automation and rationalization of non-production handling processes (i.e. flow of goods, services, construction industry)
  • Targeted use and management of heat in automated production


Citation key BobkaHeynHenningsonEtAl2018
Author Bobka, Paul and Heyn, Jakob and Henningson, Jann-Ole and Römer, Martin and Engbers, Thomas and Dietrich, Franz and Dröder, Klaus
Pages 28-41
Year 2018
ISBN 1895-7595
Journal Journal of Machine Engineering; XXIX CIRP Sponsored Conference on Supervising and Diagnostics of Machining Systems in Karpacz
Volume Vol 18, No. 3, 2018,
Abstract A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process, as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.
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Prof. Dr.-Ing. Franz Dietrich
sec. PTZ2
Pascalstr. 8-9
10587 Berlin
+49 (0)30/314-22014
+49 (0)30/314-22759