Chatter detection and suppression in machining processes: a comprehensive analysis
Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, incl...
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Gespeichert in:
| Autor*in: |
Basit, Abdul [verfasserIn] Khan, Niaz Bahadur [verfasserIn] Ali, Sadaqat [verfasserIn] Muhammad, Riaz [verfasserIn] Abduvalieva, Dilsora [verfasserIn] Khan, M. Ijaz [verfasserIn] Jameel, Mohammed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal on interactive design and manufacturing - Springer Paris, 2007, 18(2024), 6 vom: 23. Jan., Seite 3751-3771 |
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| Übergeordnetes Werk: |
volume:18 ; year:2024 ; number:6 ; day:23 ; month:01 ; pages:3751-3771 |
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| DOI / URN: |
10.1007/s12008-023-01716-8 |
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SPR057183481 |
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| 520 | |a Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. | ||
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10.1007/s12008-023-01716-8 doi (DE-627)SPR057183481 (SPR)s12008-023-01716-8-e DE-627 ger DE-627 rakwb eng 690 VZ Basit, Abdul verfasserin aut Chatter detection and suppression in machining processes: a comprehensive analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. Chatter detection (dpeaa)DE-He213 Chatter suppression (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Vibration (dpeaa)DE-He213 Khan, Niaz Bahadur verfasserin aut Ali, Sadaqat verfasserin aut Muhammad, Riaz verfasserin aut Abduvalieva, Dilsora verfasserin aut Khan, M. Ijaz verfasserin aut Jameel, Mohammed verfasserin aut Enthalten in International journal on interactive design and manufacturing Springer Paris, 2007 18(2024), 6 vom: 23. Jan., Seite 3751-3771 (DE-627)546503195 (DE-600)2390733-2 1955-2505 nnns volume:18 year:2024 number:6 day:23 month:01 pages:3751-3771 https://dx.doi.org/10.1007/s12008-023-01716-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 6 23 01 3751-3771 |
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10.1007/s12008-023-01716-8 doi (DE-627)SPR057183481 (SPR)s12008-023-01716-8-e DE-627 ger DE-627 rakwb eng 690 VZ Basit, Abdul verfasserin aut Chatter detection and suppression in machining processes: a comprehensive analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. Chatter detection (dpeaa)DE-He213 Chatter suppression (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Vibration (dpeaa)DE-He213 Khan, Niaz Bahadur verfasserin aut Ali, Sadaqat verfasserin aut Muhammad, Riaz verfasserin aut Abduvalieva, Dilsora verfasserin aut Khan, M. Ijaz verfasserin aut Jameel, Mohammed verfasserin aut Enthalten in International journal on interactive design and manufacturing Springer Paris, 2007 18(2024), 6 vom: 23. Jan., Seite 3751-3771 (DE-627)546503195 (DE-600)2390733-2 1955-2505 nnns volume:18 year:2024 number:6 day:23 month:01 pages:3751-3771 https://dx.doi.org/10.1007/s12008-023-01716-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 6 23 01 3751-3771 |
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10.1007/s12008-023-01716-8 doi (DE-627)SPR057183481 (SPR)s12008-023-01716-8-e DE-627 ger DE-627 rakwb eng 690 VZ Basit, Abdul verfasserin aut Chatter detection and suppression in machining processes: a comprehensive analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. Chatter detection (dpeaa)DE-He213 Chatter suppression (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Vibration (dpeaa)DE-He213 Khan, Niaz Bahadur verfasserin aut Ali, Sadaqat verfasserin aut Muhammad, Riaz verfasserin aut Abduvalieva, Dilsora verfasserin aut Khan, M. Ijaz verfasserin aut Jameel, Mohammed verfasserin aut Enthalten in International journal on interactive design and manufacturing Springer Paris, 2007 18(2024), 6 vom: 23. Jan., Seite 3751-3771 (DE-627)546503195 (DE-600)2390733-2 1955-2505 nnns volume:18 year:2024 number:6 day:23 month:01 pages:3751-3771 https://dx.doi.org/10.1007/s12008-023-01716-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 6 23 01 3751-3771 |
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10.1007/s12008-023-01716-8 doi (DE-627)SPR057183481 (SPR)s12008-023-01716-8-e DE-627 ger DE-627 rakwb eng 690 VZ Basit, Abdul verfasserin aut Chatter detection and suppression in machining processes: a comprehensive analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. Chatter detection (dpeaa)DE-He213 Chatter suppression (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Vibration (dpeaa)DE-He213 Khan, Niaz Bahadur verfasserin aut Ali, Sadaqat verfasserin aut Muhammad, Riaz verfasserin aut Abduvalieva, Dilsora verfasserin aut Khan, M. Ijaz verfasserin aut Jameel, Mohammed verfasserin aut Enthalten in International journal on interactive design and manufacturing Springer Paris, 2007 18(2024), 6 vom: 23. Jan., Seite 3751-3771 (DE-627)546503195 (DE-600)2390733-2 1955-2505 nnns volume:18 year:2024 number:6 day:23 month:01 pages:3751-3771 https://dx.doi.org/10.1007/s12008-023-01716-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 6 23 01 3751-3771 |
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10.1007/s12008-023-01716-8 doi (DE-627)SPR057183481 (SPR)s12008-023-01716-8-e DE-627 ger DE-627 rakwb eng 690 VZ Basit, Abdul verfasserin aut Chatter detection and suppression in machining processes: a comprehensive analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. Chatter detection (dpeaa)DE-He213 Chatter suppression (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Artificial intelligence (dpeaa)DE-He213 Vibration (dpeaa)DE-He213 Khan, Niaz Bahadur verfasserin aut Ali, Sadaqat verfasserin aut Muhammad, Riaz verfasserin aut Abduvalieva, Dilsora verfasserin aut Khan, M. Ijaz verfasserin aut Jameel, Mohammed verfasserin aut Enthalten in International journal on interactive design and manufacturing Springer Paris, 2007 18(2024), 6 vom: 23. Jan., Seite 3751-3771 (DE-627)546503195 (DE-600)2390733-2 1955-2505 nnns volume:18 year:2024 number:6 day:23 month:01 pages:3751-3771 https://dx.doi.org/10.1007/s12008-023-01716-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2024 6 23 01 3751-3771 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. 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Basit, Abdul |
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chatter detection and suppression in machining processes: a comprehensive analysis |
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Chatter detection and suppression in machining processes: a comprehensive analysis |
| abstract |
Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Chatter is a phenomenon that occurs during machining operations, causing vibrations that can negatively impact the quality of the machined surface. Detecting and avoiding chatter is crucial for efficient machining processes. Various strategies have been developed to address this issue, including offline chatter prediction, online chatter detection and suppression, and the use of artificial intelligence (AI) solutions in line with Industry 4.0 trends. However, the topic of chatter detection is partially discussed as a section in some review publications, and it does not appear as a kernel focus. With the addition of the latest development in chatter detection and suppression, conducting a rigorous review of chatter is critical. This work entails tracing analytical chatter detection techniques (stability lobe diagram, Nyquist plot, finite element analysis), experimental chatter detection techniques by using various data acquisition signals and from time–frequency signal processing methods (fast Fourier transform, discrete wavelet transform, hilbert-huang transform, short-time Fourier transform, etc.), as well as the most recent AI techniques (artificial neural network, support vector machine, hidden markov model, fuzzy logic, k-nearest neighbor, etc.). A thorough investigation was conducted to determine the limitations of these various techniques and to provide potential solutions for detecting chattering in machining processes. Moreover, The approaches for suppressing chatter (active + passive) during the machining process will also be thoroughly reviewed in this article. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Chatter detection and suppression in machining processes: a comprehensive analysis |
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https://dx.doi.org/10.1007/s12008-023-01716-8 |
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Khan, Niaz Bahadur Ali, Sadaqat Muhammad, Riaz Abduvalieva, Dilsora Khan, M. Ijaz Jameel, Mohammed |
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Khan, Niaz Bahadur Ali, Sadaqat Muhammad, Riaz Abduvalieva, Dilsora Khan, M. Ijaz Jameel, Mohammed |
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10.1007/s12008-023-01716-8 |
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2024-09-03T04:49:17.963Z |
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| score |
7.397217 |