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Sensors 2017, 17(12), 2885; https://doi.org/10.3390/s17122885

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

1
Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh-J_LEAPT Naples), P.le Tecchio 80, 80125 Naples, Italy
2
Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
3
Department of Industrial Engineering, University of Naples Federico II, P.le Tecchio 80, 80125 Naples, Italy
4
South Eastern Applied Material Research Centre, WIT, Applied Technology Building, Paddy Browns Road, X91 TX03 Waterford, Ireland
*
Author to whom correspondence should be addressed.
Received: 20 October 2017 / Revised: 4 December 2017 / Accepted: 11 December 2017 / Published: 12 December 2017
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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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. View Full-Text
Keywords: Nickel-Titanium alloy; turning; sensor monitoring; vibration; machinability; cognitive pattern recognition Nickel-Titanium alloy; turning; sensor monitoring; vibration; machinability; cognitive pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Segreto, T.; Caggiano, A.; Karam, S.; Teti, R. Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation. Sensors 2017, 17, 2885.

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