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Time Reduction in Online Programming – An Approach to Hand Guided Teaching for Small Batch Robot Machining

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Advances in System-Integrated Intelligence (SYSINT 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 546))

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Abstract

In contrast to the shortage of skilled workers and the resulting need to use their capabilities as effectively as possible, repetitive tasks are still performed by hand when it comes to small batches up to one-off, especially in smaller or medium sized enterprises. Reasons, for example, are the lack of programming and automation solutions with which simple tasks can be easily and in no time teached onto a robotic application. One of the fastest possibilities of online-teaching robots without programming is hand-guiding. Provided mainly for human robot collaboration most of the suitable cobots have a restricted payload. This leads to a very limited field of application regarding machining or the handling of heavy weight tools. In contrast high-load industrial robots are very common for large-series machining, but the necessary knowledge and programming effort currently prevent the use for quickly changing tasks.

This paper presents an approach, which reduces the time for online teaching of industrial robot machining. An equipped hand guidance and additional force sensors enable the user to perform the machining operation by handling the workpiece or tool as usual and records the resulting process paths and forces for teaching. Combined with a simple graphical interface for the necessary user-input the solution is empowered to repeat the recorded machining operation according to the current setup autonomously. With a target-actual comparison of the resulting forces the automatic process can be monitored. With this holistic approach the solution is suitable either for handling tasks, common machining and part-to-tool machining on external tools.

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Authors and Affiliations

  1. Fraunhofer Institute for Large Structures in Production Engineering IGP, Albert-Einstein-Str. 30, 18059, Rostock, Germany

    Marten Stepputat, Florian Beuss & Jan Sender

  2. Chair of Manufacturing Engineering, University of Rostock, Albert-Einstein-Str. 30, 18059, Rostock, Germany

    Wilko Fluegge

Authors
  1. Marten Stepputat
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  2. Florian Beuss
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  3. Jan Sender
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  4. Wilko Fluegge
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Corresponding author

Correspondence to Marten Stepputat .

Editor information

Editors and Affiliations

  1. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Maurizio Valle

  2. Fraunhofer Institute for Manufacturing Technology and Advanced Materials, Bremen, Germany

    Dirk Lehmhus

  3. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Christian Gianoglio

  4. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Edoardo Ragusa

  5. Department of Electrical, Electronic and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, Genoa, Italy

    Lucia Seminara

  6. Computer Science and Mathematics, University of Bremen, Bremen, Germany

    Stefan Bosse

  7. Department of Electrical and Electronics Engineering (EEE), Lebanese International University (LIU), Beirut, Lebanon

    Ali Ibrahim

  8. BIBA, University of Bremen, Bremen, Germany

    Klaus-Dieter Thoben

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Stepputat, M., Beuss, F., Sender, J., Fluegge, W. (2023). Time Reduction in Online Programming – An Approach to Hand Guided Teaching for Small Batch Robot Machining. In: Valle, M., et al. Advances in System-Integrated Intelligence. SYSINT 2022. Lecture Notes in Networks and Systems, vol 546. Springer, Cham. https://doi.org/10.1007/978-3-031-16281-7_38

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  • DOI: https://doi.org/10.1007/978-3-031-16281-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16280-0

  • Online ISBN: 978-3-031-16281-7

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