Vibration sensor monitoring of nickel-titanium alloy turning for machinability evaluation

Tiziana Segreto, Alessandra Caggiano, Sara Karam, Roberto Teti

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Original languageEnglish
Article number2885
JournalSensors (Switzerland)
Volume17
Issue number12
DOIs
Publication statusPublished - 12 Dec 2017

Keywords

  • Cognitive pattern recognition
  • Machinability
  • Nickel-titanium alloy
  • Sensor monitoring
  • Turning
  • Vibration

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