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Online adaption of milling parameters for a stable and productive process

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Benjamin Bergmann
  • Svenja Reimer

Details

Original languageEnglish
Pages (from-to)341-344
Number of pages4
JournalCIRP annals
Volume70
Issue number1
Early online date19 May 2021
Publication statusPublished - 2021

Abstract

On the way to fully autonomous machine tools it is essential to independently select suitable process parameters and adapt them on-the-fly to the appropriate process conditions in a self-controlled manner. Such systems require complex physical process models and are usually limited to feed and spindle speed adaption during the milling process. This paper introduces a new approach enabling machines during the milling process to learn which parameters lead to a stable process with maximum productivity and to adjust them autonomously. It is shown that this approach enables the machine tool to independently find stable process parameters with maximum productivity.

Keywords

    Chatter, Machine Learning, Machine tool

ASJC Scopus subject areas

Cite this

Online adaption of milling parameters for a stable and productive process. / Bergmann, Benjamin; Reimer, Svenja.
In: CIRP annals, Vol. 70, No. 1, 2021, p. 341-344.

Research output: Contribution to journalArticleResearchpeer review

Bergmann B, Reimer S. Online adaption of milling parameters for a stable and productive process. CIRP annals. 2021;70(1):341-344. Epub 2021 May 19. doi: 10.1016/j.cirp.2021.04.086
Bergmann, Benjamin ; Reimer, Svenja. / Online adaption of milling parameters for a stable and productive process. In: CIRP annals. 2021 ; Vol. 70, No. 1. pp. 341-344.
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