Wavelet transform feature extraction for chip form recognition during carbon steel turning

S. Karam, R. Teti

Research output: Contribution to journalConference articlepeer-review

31 Citations (Scopus)


Cutting force sensor monitoring and wavelet decomposition signal processing were implemented for feature extraction and pattern recognition of chip form typology during turning of 1045 carbon steel. The wavelet packet transform was applied for the analysis of the detected cutting force signals by representing them in a time-frequency domain and providing for the extraction of wavelet packet statistical features. The latter were used to construct wavelet packet feature vectors, ranked according to the number of overlapping elements related to favourable or unfavourable chip forms that cause noise in the pattern recognition procedure (lower number, lower noise, higher rank). The eight highest ranked wavelet packet feature vectors were selected as inputs to a neural network decision-making system on chip form acceptability. Subsequently, a data refinement procedure was employed to improve the neural network performance in the chip form identification process.

Original languageEnglish
Pages (from-to)97-102
Number of pages6
JournalProcedia CIRP
Publication statusPublished - 2013
Externally publishedYes
Event8th CIRP International Conference on Intelligent Computation in Manufacturing Engineering, ICME 2012 - Ischia, Italy
Duration: 18 Jul 201220 Jul 2012


  • Chip form
  • Feature extraction
  • Neural networks
  • Pattern recognition
  • Sensor monitoring
  • Wavelet packet trasnform


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