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Automated Identification of Parameters in Control Systems of Machine Tools

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Production at the leading edge of technology (WGP 2020)

Part of the book series: Lecture Notes in Production Engineering ((LNPE))

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Abstract

Especially in the context of Artificial Intelligence (AI) applications and increasing Overall Equipment Effectiveness (OEE) requirements, the use of data in production is gaining in importance. Applications in the field of process or condition monitoring use, for example, machine component parameters such as motor currents, travel speeds and position information. However, as the data is usually only accessible in the machine control systems in non-standard structures and semantics, while having a large number of potential variables, the identification and use of these parameters and data sources represents a significant challenge. This paper therefore presents an approach to automatically identify and assign machine parameters on the basis of time series data. For the identification, feature- and deep learning-based classification approaches are used and compared. Classification results show a general usability of the approaches for the identification of machine parameters.

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Authors and Affiliations

  1. Wbk Institute of Production Science, Kaiserstrasse 12, 76131, Karlsruhe, Germany

    P. Gönnheimer, A. Puchta & J. Fleischer

Authors
  1. P. Gönnheimer
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  2. A. Puchta
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  3. J. Fleischer
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Corresponding author

Correspondence to P. Gönnheimer .

Editor information

Editors and Affiliations

  1. Institute of Forming Technology and Machines, Garbsen, Germany

    Bernd-Arno Behrens

  2. Institute of Manufacturing Science and Engineering, Dresden, Germany

    Alexander Brosius

  3. Institute of Production Management and Technology, Hamburg, Germany

    Wolfgang Hintze

  4. Institute of Mechatronic Engineering, Dresden, Germany

    Steffen Ihlenfeldt

  5. Laboratorium for Manufacturing Technology, Hamburg, Germany

    Jens Peter Wulfsberg

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© 2021 The Author(s), under exclusive license to Springer-Verlag GmbH, DE , part of Springer Nature

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Gönnheimer, P., Puchta, A., Fleischer, J. (2021). Automated Identification of Parameters in Control Systems of Machine Tools. In: Behrens, BA., Brosius, A., Hintze, W., Ihlenfeldt, S., Wulfsberg, J.P. (eds) Production at the leading edge of technology. WGP 2020. Lecture Notes in Production Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62138-7_57

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  • DOI: https://doi.org/10.1007/978-3-662-62138-7_57

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-62137-0

  • Online ISBN: 978-3-662-62138-7

  • eBook Packages: EngineeringEngineering (R0)

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