Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals
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 mechanica...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/5/708 |
| _version_ | 1811293683585122304 |
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| author | Yongjiao Chi Wei Dai Zhiyuan Lu Meiqing Wang Yu Zhao |
| author_facet | Yongjiao Chi Wei Dai Zhiyuan Lu Meiqing Wang Yu Zhao |
| author_sort | Yongjiao Chi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:06:04Z |
| format | Article |
| id | doaj.art-0fac63db98634d3ab9e66a54b89d1b8f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T05:06:04Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0fac63db98634d3ab9e66a54b89d1b8f2022-12-22T03:01:11ZengMDPI AGApplied Sciences2076-34172018-05-018570810.3390/app8050708app8050708Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored SignalsYongjiao Chi0Wei Dai1Zhiyuan Lu2Meiqing Wang3Yu Zhao4School of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Mechanical Engineering and Automation, Beihang University, Beijing 100191, ChinaSchool of Reliability and Systems Engineering, Beihang University, Beijing 100191, ChinaThere 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.http://www.mdpi.com/2076-3417/8/5/708tool wear estimationmodal analysistool condition monitoring |
| spellingShingle | Yongjiao Chi Wei Dai Zhiyuan Lu Meiqing Wang Yu Zhao Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals Applied Sciences tool wear estimation modal analysis tool condition monitoring |
| title | Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals |
| title_full | Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals |
| title_fullStr | Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals |
| title_full_unstemmed | Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals |
| title_short | Real-Time Estimation for Cutting Tool Wear Based on Modal Analysis of Monitored Signals |
| title_sort | real time estimation for cutting tool wear based on modal analysis of monitored signals |
| topic | tool wear estimation modal analysis tool condition monitoring |
| url | http://www.mdpi.com/2076-3417/8/5/708 |
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