Output Only Functional Series Time Dependent AutoRegressive Moving Average (FS-TARMA) Modelling of Tool Acceleration Signals for Wear Estimation

Aghdam B. H., CİĞEROĞLU E., Sadeghi M. H.

33rd IMAC Conference and Exposition on Structural Dynamics, Florida, Amerika Birleşik Devletleri, 2 - 05 Şubat 2015, ss.111-122 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1007/978-3-319-15230-1_11
  • Basıldığı Şehir: Florida
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.111-122
  • Anahtar Kelimeler: Tool wear, Turning, FS-TARMA, Time series, ARTIFICIAL NEURAL-NETWORK, SENSOR FUSION, ACOUSTIC-EMISSION, ONLINE, MACHINE
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper, tool vibration signals obtained from a turning process are used for tool wear estimation purposes. During the cutting process, tool acceleration signals are recorded for different levels of wear. Due to non-stationarity of tool/holder system's response, Time dependent time series model of Functional Series Time dependent AutoRegressive Moving Average (FS-TARMA) type is used for modelling the signals and extraction of wear sensitive features that will be exploited in a wear estimation algorithm. Results of the analysis through FS-TARMA, reveals its higher accuracy with respect to stationary type models, since it captures time dependent properties as well, which can be used in an online tool wear estimation algorithm.