Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms
- PDF / 1,484,865 Bytes
- 13 Pages / 595.276 x 790.866 pts Page_size
- 28 Downloads / 148 Views
PRODUCTION PROCESS
Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms Tiziana Segreto1,2 · Doriana D’Addona1,2 · Roberto Teti1,2 Received: 27 July 2020 / Accepted: 5 October 2020 © The Author(s) 2020
Abstract In the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation. Keywords Inconel 718 · Tool wear · Multiple sensor monitoring · Wavelet packet transform · Machine learning · Artificial neural networks
1 Introduction In modern machining processes, tool wear estimation is a crucial requirement to prevent machine tool failure and to produce parts with the required high quality. During machining operation, the employment of worn cutting tools can cause poor surface finish and insufficient dimensional accuracy of the product as well as unexpected catastrophic tool failure events. Moreover, cutting tool health tends to deteriorate more rapidly when the workpiece hardness is higher [1]. In the last years, the notable increase in the demand for materials with high strength and temperature resistivity in aerospace and gas turbine industries has led to an extensive use of nickel-based alloys. These alloys provide properties * Tiziana Segreto [email protected] 1
Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh J_LEAPT UniNaples), Piazzale Tecchio 80, 80125 Naples, Italy
2
of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures but are classified as a hard-to-machine materials [2–4]. Inconel 718 is a nickel-based alloy largely utilized to manufacture parts of nuclear reactors, gas turbines, rocket motors, spacecraft, pumps, tooling systems, etc. It combines corrosion resistance and high strength with o
Data Loading...
