Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

被引:0
作者
Tiziana Segreto
Doriana D’Addona
Roberto Teti
机构
[1] University of Naples Federico II,Department of Chemical, Materials and Industrial Production Engineering
[2] Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology (Fh J_LEAPT UniNaples),undefined
来源
Production Engineering | 2020年 / 14卷
关键词
Inconel 718; Tool wear; Multiple sensor monitoring; Wavelet packet transform; Machine learning; Artificial neural networks;
D O I
暂无
中图分类号
学科分类号
摘要
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.
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页码:693 / 705
页数:12
相关论文
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