An adaptive chatter detection method for micro milling based on variational mode extraction

Wei Kang Wang, Bao Guo Jia, Min Wan, Dan Yang Wen, Wei Hong Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Current chatter detection methods using variational mode decomposition (VMD) hardly select appropriate intrinsic mode functions (IMFs) for characterizing chatter in micro milling. This article introduces a novel variational mode extraction (VME) method to isolate a single IMF representing chatter. An adaptive filter reduces chatter-independent components, enabling VME to identify the desired IMF with adaptive calculations for center frequency and penalty factor. The Hilbert–Huang transform is then applied to obtain the time–frequency distribution of the obtained IMF, and various features are computed. Finally, two methods are employed: streaming feature selection considers feature interaction, and radial basis function support vector machine selects pertinent features to establish the chatter detection model. Extensive micro milling tests validate the method, achieving an average detection accuracy of 99.34%.

Original languageEnglish
Article number115377
JournalMeasurement: Journal of the International Measurement Confederation
Volume238
DOIs
StatePublished - Oct 2024

Keywords

  • Adaptive filter
  • Chatter detection
  • Hilbert–Huang transform
  • Micro milling
  • Variational mode extraction

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