A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO
As a kind of self-excited vibrations, chatter vibration is extremely common in end milling, especially in high-speed cutting processes. It affects the machining accuracy of products and decreases the processing efficiency of machine tools. Thus it is very crucial to develop an effective condition mo...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2020/7943807 |
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| _version_ | 1832566339142680576 |
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| author | Erhua Wang Peng Yan Jie Liu |
| author_facet | Erhua Wang Peng Yan Jie Liu |
| author_sort | Erhua Wang |
| collection | DOAJ |
| description | As a kind of self-excited vibrations, chatter vibration is extremely common in end milling, especially in high-speed cutting processes. It affects the machining accuracy of products and decreases the processing efficiency of machine tools. Thus it is very crucial to develop an effective condition monitoring system to extract the chatter feature before chatter vibration grows. In this paper, a hybrid chatter detection method (HCDM) is proposed for chatter feature extraction and classification in end milling. Firstly, wavelet packet decomposition is employed to decompose cutting vibration signals into a series of wavelet coefficients, and the signals of each frequency band are reconstructed. Secondly, fast Fourier transform and singular spectrum analysis are chosen to obtain the chatter features. Furthermore, the support vector machine model is optimized by particle swarm optimization to recognize the cutting states in end milling. At last, cutting experiments of 300 M steel under different machining conditions are conducted, and the results indicate that the proposed HCDM can distinguish the stable, transition, and chatter states accurately and rapidly in end milling. |
| format | Article |
| id | doaj-art-63048ec880274e16a36d54c0478d8e9a |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-63048ec880274e16a36d54c0478d8e9a2025-02-03T01:04:22ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/79438077943807A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSOErhua Wang0Peng Yan1Jie Liu2Changzhou Key Laboratory of Advanced Technology, Changzhou College of Information Technology, Changzhou 213164, ChinaChangzhou Key Laboratory of Advanced Technology, Changzhou College of Information Technology, Changzhou 213164, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaAs a kind of self-excited vibrations, chatter vibration is extremely common in end milling, especially in high-speed cutting processes. It affects the machining accuracy of products and decreases the processing efficiency of machine tools. Thus it is very crucial to develop an effective condition monitoring system to extract the chatter feature before chatter vibration grows. In this paper, a hybrid chatter detection method (HCDM) is proposed for chatter feature extraction and classification in end milling. Firstly, wavelet packet decomposition is employed to decompose cutting vibration signals into a series of wavelet coefficients, and the signals of each frequency band are reconstructed. Secondly, fast Fourier transform and singular spectrum analysis are chosen to obtain the chatter features. Furthermore, the support vector machine model is optimized by particle swarm optimization to recognize the cutting states in end milling. At last, cutting experiments of 300 M steel under different machining conditions are conducted, and the results indicate that the proposed HCDM can distinguish the stable, transition, and chatter states accurately and rapidly in end milling.http://dx.doi.org/10.1155/2020/7943807 |
| spellingShingle | Erhua Wang Peng Yan Jie Liu A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO Shock and Vibration |
| title | A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO |
| title_full | A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO |
| title_fullStr | A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO |
| title_full_unstemmed | A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO |
| title_short | A Hybrid Chatter Detection Method Based on WPD, SSA, and SVM-PSO |
| title_sort | hybrid chatter detection method based on wpd ssa and svm pso |
| url | http://dx.doi.org/10.1155/2020/7943807 |
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