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Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(5), 708; https://doi.org/10.3390/app8050708
Received: 23 March 2018 / Revised: 27 April 2018 / Accepted: 27 April 2018 / Published: 3 May 2018
(This article belongs to the Section Mechanical Engineering)
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Abstract

There is a growing body of literature that recognizes the importance of product safety and the quality problems during processing. The working status of cutting tools may lead to project delay and cost overrun if broken down accidentally, and tool wear is crucial to processing precision in mechanical manufacturing, therefore, this study contributes to this growing area of research by monitoring condition and estimating wear. In this research, an effective method for tool wear estimation was constructed, in which, the signal features of machining process were extracted by ensemble empirical mode decomposition (EEMD) and were used to estimate the tool wear. Based on signal analysis, vibration signals that had better linear relationship with tool wearing process were decomposed, then the intrinsic mode functions (IMFs), frequency spectrums of IMFs and the features relating to amplitude changes of frequency spectrum were obtained. The trend that tool wear changes with the features was fitted by Gaussian fitting function to estimate the tool wear. Experimental investigation was used to verify the effectiveness of this method and the results illustrated the correlation between tool wear and the modal features of monitored signals. View Full-Text
Keywords: tool wear estimation; modal analysis; tool condition monitoring tool wear estimation; modal analysis; tool condition monitoring
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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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MDPI and ACS Style

Chi, Y.; Dai, W.; Lu, Z.; Wang, M.; Zhao, Y. Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals. Appl. Sci. 2018, 8, 708.

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