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- W2376074359 abstract "To avoid the pre-designed teaching or training phase for the condition monitoring system at present,an intelligent monitoring system for high-speed milling process is proposed based on the analysis of the tool wear rule and different tool wear stages.Self-learning was introduced to the system to automatically identify different tool wear states and estimate the wear value,which is independent of the pre-designed teaching or training phase.Three-direction components of the cutting force signals generated in high-speed milling process were processed using discrete wavelet decomposition technology.Features in different time and frequency domains were extracted and selected through correlation analysis method.The real-time intelligent monitoring system was built on the cycle process of linear fitting and Mahalanobis distance(MD) calculation.A series of experiments on a CNC vertical milling machine tool shows that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools." @default.
- W2376074359 created "2016-06-24" @default.
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- W2376074359 date "2010-01-01" @default.
- W2376074359 modified "2023-09-22" @default.
- W2376074359 title "An intelligent monitoring system for high-speed milling process" @default.
- W2376074359 hasPublicationYear "2010" @default.
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