Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning
Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/21/6113 |
| _version_ | 1797549686489350144 |
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| author | Jun Yuan Libing Liu Zeqing Yang Yanrui Zhang |
| author_facet | Jun Yuan Libing Liu Zeqing Yang Yanrui Zhang |
| author_sort | Jun Yuan |
| collection | DOAJ |
| description | Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability. |
| first_indexed | 2024-03-10T15:17:58Z |
| format | Article |
| id | doaj.art-8f7f3b941eb24308b8f12afe62c69aff |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:17:58Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8f7f3b941eb24308b8f12afe62c69aff2023-11-20T18:44:05ZengMDPI AGSensors1424-82202020-10-012021611310.3390/s20216113Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble LearningJun Yuan0Libing Liu1Zeqing Yang2Yanrui Zhang3School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaExperimental Training, Hebei University of Technology, Tianjin 300401, ChinaMost online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.https://www.mdpi.com/1424-8220/20/21/6113tool wear condition monitoringspindle motor currenttime–domain analysisfrequency–domain analysisvariational mode decomposition (VMD)ensemble learning (EL) |
| spellingShingle | Jun Yuan Libing Liu Zeqing Yang Yanrui Zhang Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning Sensors tool wear condition monitoring spindle motor current time–domain analysis frequency–domain analysis variational mode decomposition (VMD) ensemble learning (EL) |
| title | Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning |
| title_full | Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning |
| title_fullStr | Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning |
| title_full_unstemmed | Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning |
| title_short | Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning |
| title_sort | tool wear condition monitoring by combining variational mode decomposition and ensemble learning |
| topic | tool wear condition monitoring spindle motor current time–domain analysis frequency–domain analysis variational mode decomposition (VMD) ensemble learning (EL) |
| url | https://www.mdpi.com/1424-8220/20/21/6113 |
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