Online machining chatter forecast based on improved local mean decomposition
Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online mach...
Ausführliche Beschreibung
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| Autor*in: |
Sun, Huibin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
Online machining chatter forecast |
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| Anmerkung: |
© Springer-Verlag London 2015 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 84(2015), 5-8 vom: 09. Sept., Seite 1045-1056 |
|---|---|
| Übergeordnetes Werk: |
volume:84 ; year:2015 ; number:5-8 ; day:09 ; month:09 ; pages:1045-1056 |
| Links: |
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| DOI / URN: |
10.1007/s00170-015-7785-8 |
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OLC2026081816 |
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| 520 | |a Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. | ||
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| 700 | 1 | |a Wang, Junyang |4 aut | |
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10.1007/s00170-015-7785-8 doi (DE-627)OLC2026081816 (DE-He213)s00170-015-7785-8-p DE-627 ger DE-627 rakwb eng 670 VZ Sun, Huibin verfasserin aut Online machining chatter forecast based on improved local mean decomposition 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2015 Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. Online machining chatter forecast Improved local mean decomposition Sensitive feature extraction Zhang, Xianzhi aut Wang, Junyang aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 84(2015), 5-8 vom: 09. Sept., Seite 1045-1056 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:84 year:2015 number:5-8 day:09 month:09 pages:1045-1056 https://doi.org/10.1007/s00170-015-7785-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 84 2015 5-8 09 09 1045-1056 |
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10.1007/s00170-015-7785-8 doi (DE-627)OLC2026081816 (DE-He213)s00170-015-7785-8-p DE-627 ger DE-627 rakwb eng 670 VZ Sun, Huibin verfasserin aut Online machining chatter forecast based on improved local mean decomposition 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2015 Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. Online machining chatter forecast Improved local mean decomposition Sensitive feature extraction Zhang, Xianzhi aut Wang, Junyang aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 84(2015), 5-8 vom: 09. Sept., Seite 1045-1056 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:84 year:2015 number:5-8 day:09 month:09 pages:1045-1056 https://doi.org/10.1007/s00170-015-7785-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 84 2015 5-8 09 09 1045-1056 |
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10.1007/s00170-015-7785-8 doi (DE-627)OLC2026081816 (DE-He213)s00170-015-7785-8-p DE-627 ger DE-627 rakwb eng 670 VZ Sun, Huibin verfasserin aut Online machining chatter forecast based on improved local mean decomposition 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2015 Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. Online machining chatter forecast Improved local mean decomposition Sensitive feature extraction Zhang, Xianzhi aut Wang, Junyang aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 84(2015), 5-8 vom: 09. Sept., Seite 1045-1056 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:84 year:2015 number:5-8 day:09 month:09 pages:1045-1056 https://doi.org/10.1007/s00170-015-7785-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 84 2015 5-8 09 09 1045-1056 |
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Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. © Springer-Verlag London 2015 |
| abstractGer |
Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. © Springer-Verlag London 2015 |
| abstract_unstemmed |
Abstract Machining chatter is the self-excited vibration between the cutting tool and work piece that can deteriorate surface quality and increase cost. A reliable machining chatter forecast method is needed to recognize chatter symptom, while the chatter is developing. In this paper, an online machining chatter forecast framework is proposed. The improved moving average algorithm is designed to improve the performance of the local mean decomposition (LMD) method in dealing with non-stationary force and vibration signals. According to the result of signal character analysis, some sensitive features are extracted to build characteristic vectors. A machining chatter forecast model is put forward based on the hidden Markov model (HMM). The Viterbi algorithm is used to resolve the optimum cutting transient sequence. The mapping relationship between vector sequences in the input space and images in the pattern space has been developed based on the posterior transient transform probability. An experimental platform has been implemented to illustrate and validate the proposed method. Case studies showed that machining chatter could be forecasted effectively in advance during its developing stage. Quantitative analysis and comparison verified that both the precision and timeliness could be improved. © Springer-Verlag London 2015 |
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Online machining chatter forecast based on improved local mean decomposition |
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Zhang, Xianzhi Wang, Junyang |
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