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Continuous improvement of HSM process by data mining

  • Victor Godreau
  • Mathieu Ritou
  • Etienne Chové
  • Benoit Furet
  • Didier Dumur
Article
  • 136 Downloads

Abstract

The efficient use of digital manufacturing data is a key leverage point of the factories of the future. Automatic analysis tools are required to provide smart and comprehensible information from large process databases collected on shopfloor machines-tools. In this paper, an original and dedicated approach is proposed for the data mining of HSM (High Speed Machining) flexible productions. It relies on an unsupervised learning (by statistical modelling of machining vibrations) for the classification of machining critical events and their aggregation. Moreover, a contextual clustering is suggested for a better data selection, and a visualization of machining KPI for decision aiding. It results in new leverages for decision making and process improvement; through automatic detection of the main faulty programs, tools or machine conditions. This analysis has been performed over two spindle lifespans (18 months) of industrial HSM production in aeronautics and results are presented, which assess the proposed approach.

Keywords

Monitoring Machining Data mining 

Notes

Acknowledgements

The financial support of the French government on FUI QuaUsi and ANR SmartEmma (ANR-16-CE10-0005) is acknowledged. The authors also thank the contributions of the industrial partners.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Victor Godreau
    • 1
    • 2
  • Mathieu Ritou
    • 1
  • Etienne Chové
    • 2
  • Benoit Furet
    • 1
  • Didier Dumur
    • 3
  1. 1.LS2N (Laboratory of Digital Sciences of Nantes, UMR CNRS 6004)University of NantesNantesFrance
  2. 2.Europe TechnologiesCarquefouFrance
  3. 3.L2S (Laboratory of Signals and Systems, UMR CNRS 8506)Centrale SupélecGif sur YvetteFrance

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