An accuracy design approach for a multi-axis NC machine tool based on reliability theory
Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Ziling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
Thermal and cutting force induced error model |
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| Anmerkung: |
© Springer-Verlag London 2016 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 |
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| Übergeordnetes Werk: |
volume:91 ; year:2016 ; number:5-8 ; day:13 ; month:12 ; pages:1547-1566 |
| Links: |
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| DOI / URN: |
10.1007/s00170-016-9824-5 |
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| Katalog-ID: |
SPR001450603 |
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| 245 | 1 | 3 | |a An accuracy design approach for a multi-axis NC machine tool based on reliability theory |
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| 520 | |a Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. | ||
| 650 | 4 | |a Comprehensive error model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Thermal and cutting force induced error model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Reliability and sensitivity analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Accuracy design |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine tool |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Zhifeng |4 aut | |
| 700 | 1 | |a Cai, Ligang |4 aut | |
| 700 | 1 | |a Cheng, Qiang |4 aut | |
| 700 | 1 | |a Qi, Yin |4 aut | |
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10.1007/s00170-016-9824-5 doi (DE-627)SPR001450603 (SPR)s00170-016-9824-5-e DE-627 ger DE-627 rakwb eng Zhang, Ziling verfasserin aut An accuracy design approach for a multi-axis NC machine tool based on reliability theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Liu, Zhifeng aut Cai, Ligang aut Cheng, Qiang aut Qi, Yin aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:91 year:2016 number:5-8 day:13 month:12 pages:1547-1566 https://dx.doi.org/10.1007/s00170-016-9824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_206 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_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 91 2016 5-8 13 12 1547-1566 |
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10.1007/s00170-016-9824-5 doi (DE-627)SPR001450603 (SPR)s00170-016-9824-5-e DE-627 ger DE-627 rakwb eng Zhang, Ziling verfasserin aut An accuracy design approach for a multi-axis NC machine tool based on reliability theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Liu, Zhifeng aut Cai, Ligang aut Cheng, Qiang aut Qi, Yin aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:91 year:2016 number:5-8 day:13 month:12 pages:1547-1566 https://dx.doi.org/10.1007/s00170-016-9824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_206 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_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 91 2016 5-8 13 12 1547-1566 |
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10.1007/s00170-016-9824-5 doi (DE-627)SPR001450603 (SPR)s00170-016-9824-5-e DE-627 ger DE-627 rakwb eng Zhang, Ziling verfasserin aut An accuracy design approach for a multi-axis NC machine tool based on reliability theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Liu, Zhifeng aut Cai, Ligang aut Cheng, Qiang aut Qi, Yin aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:91 year:2016 number:5-8 day:13 month:12 pages:1547-1566 https://dx.doi.org/10.1007/s00170-016-9824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_206 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_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 91 2016 5-8 13 12 1547-1566 |
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10.1007/s00170-016-9824-5 doi (DE-627)SPR001450603 (SPR)s00170-016-9824-5-e DE-627 ger DE-627 rakwb eng Zhang, Ziling verfasserin aut An accuracy design approach for a multi-axis NC machine tool based on reliability theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Liu, Zhifeng aut Cai, Ligang aut Cheng, Qiang aut Qi, Yin aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:91 year:2016 number:5-8 day:13 month:12 pages:1547-1566 https://dx.doi.org/10.1007/s00170-016-9824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_206 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_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 91 2016 5-8 13 12 1547-1566 |
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10.1007/s00170-016-9824-5 doi (DE-627)SPR001450603 (SPR)s00170-016-9824-5-e DE-627 ger DE-627 rakwb eng Zhang, Ziling verfasserin aut An accuracy design approach for a multi-axis NC machine tool based on reliability theory 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London 2016 Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 Liu, Zhifeng aut Cai, Ligang aut Cheng, Qiang aut Qi, Yin aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 91(2016), 5-8 vom: 13. Dez., Seite 1547-1566 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:91 year:2016 number:5-8 day:13 month:12 pages:1547-1566 https://dx.doi.org/10.1007/s00170-016-9824-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_206 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_2008 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_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 91 2016 5-8 13 12 1547-1566 |
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Zhang, Ziling @@aut@@ Liu, Zhifeng @@aut@@ Cai, Ligang @@aut@@ Cheng, Qiang @@aut@@ Qi, Yin @@aut@@ |
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It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. 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| author |
Zhang, Ziling |
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Zhang, Ziling misc Comprehensive error model misc Thermal and cutting force induced error model misc Reliability and sensitivity analysis misc Accuracy design misc Machine tool An accuracy design approach for a multi-axis NC machine tool based on reliability theory |
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An accuracy design approach for a multi-axis NC machine tool based on reliability theory Comprehensive error model (dpeaa)DE-He213 Thermal and cutting force induced error model (dpeaa)DE-He213 Reliability and sensitivity analysis (dpeaa)DE-He213 Accuracy design (dpeaa)DE-He213 Machine tool (dpeaa)DE-He213 |
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misc Comprehensive error model misc Thermal and cutting force induced error model misc Reliability and sensitivity analysis misc Accuracy design misc Machine tool |
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misc Comprehensive error model misc Thermal and cutting force induced error model misc Reliability and sensitivity analysis misc Accuracy design misc Machine tool |
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An accuracy design approach for a multi-axis NC machine tool based on reliability theory |
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Zhang, Ziling Liu, Zhifeng Cai, Ligang Cheng, Qiang Qi, Yin |
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Zhang, Ziling |
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10.1007/s00170-016-9824-5 |
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accuracy design approach for a multi-axis nc machine tool based on reliability theory |
| title_auth |
An accuracy design approach for a multi-axis NC machine tool based on reliability theory |
| abstract |
Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. © Springer-Verlag London 2016 |
| abstractGer |
Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. © Springer-Verlag London 2016 |
| abstract_unstemmed |
Abstract Accuracy design constitutes an important role in machine tool designing. It is used to determine the permissible level of each error parameter of a machine tool, so that any criterion can be optimized. Geometric, thermal-induced, and cutting force-induced errors are responsible for a large number of comprehensive errors of a machine tool. These errors not only influence the machining accuracy but are also of great importance for accuracy design to be performed. The aim of this paper is the proposal of a general approach that simultaneously considered geometric, thermal-induced, and cutting force-induced errors, in order for machine tool errors to be allocated. By homogeneous transformation matrix (HTM) application, a comprehensive error model was developed for the machining accuracy of a machine tool to be acquired. In addition, a generalized radial basis function (RBF) neural network modeling method was used in order for a thermal and cutting force-induced error model to be established. Based on the comprehensive error model, the importance sampling method was applied for the reliability and sensitivity analysis of the machine tool to be conducted, and two mathematical models were presented. The first model predicted the reliability of the machine tool, whereas the second was used to identify and optimize the error parameters with larger effect on the reliability. The permissible level of each geometric error parameter can therefore be determined, whereas the reliability met the design requirement and the cost of this machining was optimized. An experiment was conducted on a five-axis machine tool, and the results confirmed the proposed approach being able to display the accuracy design of the machine tool. © Springer-Verlag London 2016 |
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An accuracy design approach for a multi-axis NC machine tool based on reliability theory |
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https://dx.doi.org/10.1007/s00170-016-9824-5 |
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Liu, Zhifeng Cai, Ligang Cheng, Qiang Qi, Yin |
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10.1007/s00170-016-9824-5 |
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| score |
7.4015865 |