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.
Highlights
Nickel-Titanium (Ni-Ti) alloys are recognized for their excellent electrical, mechanical and damping properties, including superelasticity and shape-memory
The neural networks (NNs) performance expressed as success rate (SR) (%) are shown in Tables 3–5 for each vibration acceleration component ax, ay, and az, with reference to the diverse NN configurations (5-5-1, 5-10-1, 5-15-1)
The best NN SR value (88.0%) for Overall machinability classification is obtained for the 5-5-1 NN configuration and the DAD wavelet packet transform (WPT) packet showing a good balance between the identification of Acceptable (SR = 91.1%) and Poor (SR = 83.3%)
Summary
Nickel-Titanium (Ni-Ti) alloys are recognized for their excellent electrical, mechanical and damping properties, including superelasticity and shape-memory. Such attributes make these alloys a promising material for a number of applications in different fields like automotive, aerospace and robotics [1,2,3]. Due to the high temperatures and stresses generated during machining of Ni-Ti alloys, the latter are classified as difficult-to-machine materials. A rapid tool failure and poor surface quality of the workpiece are generated during Ni-Ti alloys machining due to excessive burr formation, adhesions on the machined surface and microstructure alterations of the workpiece material [7]. The Sensors 2017, 17, 2885; doi:10.3390/s17122885 www.mdpi.com/journal/sensors
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