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Tool Monitoring – A Scalable Learning Approach to Estimate Cutting Tool Conditions with Machine-Internal Data in Job Shop Production of a Milling Process

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Advances in Production Research (WGP 2018)

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Abstract

Process-parallel prediction of cutting tool conditions during a milling process serves as important additional information for optimized order planning, production monitoring and quality assurance. This information is used both in mass production and in job shop production.

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Author information

Authors and Affiliations

  1. Laboratory of Machine Tools and Production Engineering WZL, RWTH Aachen University, Steinbachstrasse 19, 52074, Aachen, Germany

    Marian Wiesch, Alexander Epple & Christian Brecher

Authors
  1. Marian Wiesch
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  2. Alexander Epple
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  3. Christian Brecher
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Corresponding author

Correspondence to Marian Wiesch .

Editor information

Editors and Affiliations

  1. Werkzeugmaschinenlabor WZL, RWTH Aachen University, Aachen, Germany

    Robert Schmitt

  2. Werkzeugmaschinenlabor WZL, RWTH Aachen University, Aachen, Germany

    Günther Schuh

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Wiesch, M., Epple, A., Brecher, C. (2019). Tool Monitoring – A Scalable Learning Approach to Estimate Cutting Tool Conditions with Machine-Internal Data in Job Shop Production of a Milling Process. In: Schmitt, R., Schuh, G. (eds) Advances in Production Research. WGP 2018. Springer, Cham. https://doi.org/10.1007/978-3-030-03451-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-03451-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03450-4

  • Online ISBN: 978-3-030-03451-1

  • eBook Packages: EngineeringEngineering (R0)

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