Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy
Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xuetao Wei [verfasserIn] Caixu Yue [verfasserIn] Desheng Hu [verfasserIn] Xianli Liu [verfasserIn] Yunpeng Ding [verfasserIn] Steven Y. Liang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Chinese Journal of Mechanical Engineering - SpringerOpen, 2018, 35(2022), 1, Seite 12 |
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| Übergeordnetes Werk: |
volume:35 ; year:2022 ; number:1 ; pages:12 |
| Links: |
Link aufrufen |
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| DOI / URN: |
10.1186/s10033-022-00770-9 |
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| Katalog-ID: |
DOAJ028372190 |
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| 520 | |a Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. | ||
| 650 | 4 | |a Side milling | |
| 650 | 4 | |a Axial vibration | |
| 650 | 4 | |a Ultrasonic milling | |
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| 650 | 4 | |a Linear regression | |
| 650 | 4 | |a Surface roughness | |
| 653 | 0 | |a Ocean engineering | |
| 653 | 0 | |a Mechanical engineering and machinery | |
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| 700 | 0 | |a Desheng Hu |e verfasserin |4 aut | |
| 700 | 0 | |a Xianli Liu |e verfasserin |4 aut | |
| 700 | 0 | |a Yunpeng Ding |e verfasserin |4 aut | |
| 700 | 0 | |a Steven Y. Liang |e verfasserin |4 aut | |
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10.1186/s10033-022-00770-9 doi (DE-627)DOAJ028372190 (DE-599)DOAJe69e678f18ce41f493bfff96638b1c09 DE-627 ger DE-627 rakwb eng TC1501-1800 TJ1-1570 Xuetao Wei verfasserin aut Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness Ocean engineering Mechanical engineering and machinery Caixu Yue verfasserin aut Desheng Hu verfasserin aut Xianli Liu verfasserin aut Yunpeng Ding verfasserin aut Steven Y. Liang verfasserin aut In Chinese Journal of Mechanical Engineering SpringerOpen, 2018 35(2022), 1, Seite 12 (DE-627)356885089 (DE-600)2093153-0 21928258 nnns volume:35 year:2022 number:1 pages:12 https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 kostenfrei https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/toc/1000-9345 Journal toc kostenfrei https://doaj.org/toc/2192-8258 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 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_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 35 2022 1 12 |
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10.1186/s10033-022-00770-9 doi (DE-627)DOAJ028372190 (DE-599)DOAJe69e678f18ce41f493bfff96638b1c09 DE-627 ger DE-627 rakwb eng TC1501-1800 TJ1-1570 Xuetao Wei verfasserin aut Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness Ocean engineering Mechanical engineering and machinery Caixu Yue verfasserin aut Desheng Hu verfasserin aut Xianli Liu verfasserin aut Yunpeng Ding verfasserin aut Steven Y. Liang verfasserin aut In Chinese Journal of Mechanical Engineering SpringerOpen, 2018 35(2022), 1, Seite 12 (DE-627)356885089 (DE-600)2093153-0 21928258 nnns volume:35 year:2022 number:1 pages:12 https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 kostenfrei https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/toc/1000-9345 Journal toc kostenfrei https://doaj.org/toc/2192-8258 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 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_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 35 2022 1 12 |
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10.1186/s10033-022-00770-9 doi (DE-627)DOAJ028372190 (DE-599)DOAJe69e678f18ce41f493bfff96638b1c09 DE-627 ger DE-627 rakwb eng TC1501-1800 TJ1-1570 Xuetao Wei verfasserin aut Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness Ocean engineering Mechanical engineering and machinery Caixu Yue verfasserin aut Desheng Hu verfasserin aut Xianli Liu verfasserin aut Yunpeng Ding verfasserin aut Steven Y. Liang verfasserin aut In Chinese Journal of Mechanical Engineering SpringerOpen, 2018 35(2022), 1, Seite 12 (DE-627)356885089 (DE-600)2093153-0 21928258 nnns volume:35 year:2022 number:1 pages:12 https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 kostenfrei https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/toc/1000-9345 Journal toc kostenfrei https://doaj.org/toc/2192-8258 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 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_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 35 2022 1 12 |
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10.1186/s10033-022-00770-9 doi (DE-627)DOAJ028372190 (DE-599)DOAJe69e678f18ce41f493bfff96638b1c09 DE-627 ger DE-627 rakwb eng TC1501-1800 TJ1-1570 Xuetao Wei verfasserin aut Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness Ocean engineering Mechanical engineering and machinery Caixu Yue verfasserin aut Desheng Hu verfasserin aut Xianli Liu verfasserin aut Yunpeng Ding verfasserin aut Steven Y. Liang verfasserin aut In Chinese Journal of Mechanical Engineering SpringerOpen, 2018 35(2022), 1, Seite 12 (DE-627)356885089 (DE-600)2093153-0 21928258 nnns volume:35 year:2022 number:1 pages:12 https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 kostenfrei https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/toc/1000-9345 Journal toc kostenfrei https://doaj.org/toc/2192-8258 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 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_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 35 2022 1 12 |
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10.1186/s10033-022-00770-9 doi (DE-627)DOAJ028372190 (DE-599)DOAJe69e678f18ce41f493bfff96638b1c09 DE-627 ger DE-627 rakwb eng TC1501-1800 TJ1-1570 Xuetao Wei verfasserin aut Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness Ocean engineering Mechanical engineering and machinery Caixu Yue verfasserin aut Desheng Hu verfasserin aut Xianli Liu verfasserin aut Yunpeng Ding verfasserin aut Steven Y. Liang verfasserin aut In Chinese Journal of Mechanical Engineering SpringerOpen, 2018 35(2022), 1, Seite 12 (DE-627)356885089 (DE-600)2093153-0 21928258 nnns volume:35 year:2022 number:1 pages:12 https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 kostenfrei https://doi.org/10.1186/s10033-022-00770-9 kostenfrei https://doaj.org/toc/1000-9345 Journal toc kostenfrei https://doaj.org/toc/2192-8258 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 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_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2048 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_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2129 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 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_4277 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_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 35 2022 1 12 |
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Xuetao Wei @@aut@@ Caixu Yue @@aut@@ Desheng Hu @@aut@@ Xianli Liu @@aut@@ Yunpeng Ding @@aut@@ Steven Y. Liang @@aut@@ |
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The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. 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Xuetao Wei |
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Xuetao Wei misc TC1501-1800 misc TJ1-1570 misc Side milling misc Axial vibration misc Ultrasonic milling misc Finite element simulation misc Linear regression misc Surface roughness misc Ocean engineering misc Mechanical engineering and machinery Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy |
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TC1501-1800 TJ1-1570 Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy Side milling Axial vibration Ultrasonic milling Finite element simulation Linear regression Surface roughness |
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misc TC1501-1800 misc TJ1-1570 misc Side milling misc Axial vibration misc Ultrasonic milling misc Finite element simulation misc Linear regression misc Surface roughness misc Ocean engineering misc Mechanical engineering and machinery |
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misc TC1501-1800 misc TJ1-1570 misc Side milling misc Axial vibration misc Ultrasonic milling misc Finite element simulation misc Linear regression misc Surface roughness misc Ocean engineering misc Mechanical engineering and machinery |
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Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy |
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Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy |
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Xuetao Wei |
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Chinese Journal of Mechanical Engineering |
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Xuetao Wei Caixu Yue Desheng Hu Xianli Liu Yunpeng Ding Steven Y. Liang |
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research on surface roughness of supersonic vibration auxiliary side milling for titanium alloy |
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Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy |
| abstract |
Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. |
| abstractGer |
Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. |
| abstract_unstemmed |
Abstract The processed surface contour shape is extracted with the finite element simulation software. The difference value of contour shape change is used as the parameters of balancing surface roughness to construct finite element model of supersonic vibration milling in cutting stability domain. The surface roughness trial scheme is designed in the orthogonal test design method to analyze the surface roughness test result in the response surface methodology. The surface roughness prediction model is established and optimized. Finally, the surface roughness finite element simulation prediction model is verified by experiments. The research results show that, compared with the experiment results, the error range of the finite element simulation model is 27.5%–30.9%, and the error range of the empirical model obtained by the response surface method is between 4.4% and 12.3%. So, the model in this paper is accurate and will provide the theoretical basis for the optimization study of the auxiliary milling process of supersonic vibration. |
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Research on Surface Roughness of Supersonic Vibration Auxiliary Side Milling for Titanium Alloy |
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https://doi.org/10.1186/s10033-022-00770-9 https://doaj.org/article/e69e678f18ce41f493bfff96638b1c09 https://doaj.org/toc/1000-9345 https://doaj.org/toc/2192-8258 |
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Caixu Yue Desheng Hu Xianli Liu Yunpeng Ding Steven Y. Liang |
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Caixu Yue Desheng Hu Xianli Liu Yunpeng Ding Steven Y. Liang |
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356885089 |
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TC - Hydraulic and Ocean Engineering |
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| doi_str |
10.1186/s10033-022-00770-9 |
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TC1501-1800 |
| up_date |
2024-07-03T17:13:46.741Z |
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