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Modal identification of a machine tool structure during machining operations

  • Asia Maamar
  • Thien-Phu Le
  • Vincent GagnolEmail author
  • Laurent Sabourin
ORIGINAL ARTICLE
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Abstract

The identification of modal parameters of a machine tool structure, in service, is important to ensure stability and productivity during machining operations. The characterization can be carried out through an Operational Modal Analysis (OMA). However, in the presence of strong harmonic excitation, the application of OMA is not straightforward. To overcome this difficulty, the Transmissibility Function-Based (TFB) method is proposed. The major advantage of this approach is its independence from the excitation nature and its ability to separate structural poles from spurious ones. The main novelty of this paper lies in the investigation of the TFB approach to identify the modal properties of a machine tool, during machining operations. Identified modal model through an Experimental Modal Analysis (EMA) of the considered machine tool, at rest, presents our reference modal base to validate results obtained through the TFB approach. For a comparison purpose, the modified Enhanced Frequency Domain Decomposition (EFDD) method is also investigated. Both methods enable the identification of the modal properties under operational conditions, with a clear advantage to the TFB approach due to its ability to eliminate all of the preponderant harmonic components from the measured data without the need of any additional selection criteria. The TFB method is thus a reliable technique for the identification of modal parameters of a machine tool in operational conditions.

Keywords

Operational modal analysis Machine tool Identification procedure Transmissibility 

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Notes

Funding information

This work was sponsored by the French government research program through the IMobS3 Excellence Laboratory, by the European Union through the Regional competitiveness program and by the Auvergne region.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Asia Maamar
    • 1
  • Thien-Phu Le
    • 2
  • Vincent Gagnol
    • 1
    Email author
  • Laurent Sabourin
    • 1
  1. 1.Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut PascalClermont-FerrandFrance
  2. 2.LMEE, Université d’Évry Val-d’EssonneEvry cedexFrance

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