Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets
Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling l...
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Gespeichert in:
| Autor*in: |
Li, Weining [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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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - London : Springer, 1985, 123(2022), 3-4 vom: 05. Okt., Seite 821-838 |
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| Übergeordnetes Werk: |
volume:123 ; year:2022 ; number:3-4 ; day:05 ; month:10 ; pages:821-838 |
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| DOI / URN: |
10.1007/s00170-022-10213-3 |
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| 245 | 1 | 0 | |a Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
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| 520 | |a Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. | ||
| 650 | 4 | |a High-temperature incremental sheet forming of Ti-6Al-4V |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Machine learning network |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a Geometric accuracy |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Surface roughness |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Microstructural analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Shu, Chang |4 aut | |
| 700 | 1 | |a Hassan, Ali |4 aut | |
| 700 | 1 | |a Attallah, Moataz M. |0 (orcid)0000-0002-7074-9522 |4 aut | |
| 700 | 1 | |a Essa, Khamis |0 (orcid)0000-0001-6090-0869 |4 aut | |
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10.1007/s00170-022-10213-3 doi (DE-627)SPR04841705X (SPR)s00170-022-10213-3-e DE-627 ger DE-627 rakwb eng Li, Weining verfasserin (orcid)0000-0002-8844-564X aut Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 Shu, Chang aut Hassan, Ali aut Attallah, Moataz M. (orcid)0000-0002-7074-9522 aut Essa, Khamis (orcid)0000-0001-6090-0869 aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 123(2022), 3-4 vom: 05. Okt., Seite 821-838 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 https://dx.doi.org/10.1007/s00170-022-10213-3 kostenfrei 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_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_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_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 123 2022 3-4 05 10 821-838 |
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10.1007/s00170-022-10213-3 doi (DE-627)SPR04841705X (SPR)s00170-022-10213-3-e DE-627 ger DE-627 rakwb eng Li, Weining verfasserin (orcid)0000-0002-8844-564X aut Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 Shu, Chang aut Hassan, Ali aut Attallah, Moataz M. (orcid)0000-0002-7074-9522 aut Essa, Khamis (orcid)0000-0001-6090-0869 aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 123(2022), 3-4 vom: 05. Okt., Seite 821-838 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 https://dx.doi.org/10.1007/s00170-022-10213-3 kostenfrei 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_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_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_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 123 2022 3-4 05 10 821-838 |
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10.1007/s00170-022-10213-3 doi (DE-627)SPR04841705X (SPR)s00170-022-10213-3-e DE-627 ger DE-627 rakwb eng Li, Weining verfasserin (orcid)0000-0002-8844-564X aut Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 Shu, Chang aut Hassan, Ali aut Attallah, Moataz M. (orcid)0000-0002-7074-9522 aut Essa, Khamis (orcid)0000-0001-6090-0869 aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 123(2022), 3-4 vom: 05. Okt., Seite 821-838 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 https://dx.doi.org/10.1007/s00170-022-10213-3 kostenfrei 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_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_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_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 123 2022 3-4 05 10 821-838 |
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10.1007/s00170-022-10213-3 doi (DE-627)SPR04841705X (SPR)s00170-022-10213-3-e DE-627 ger DE-627 rakwb eng Li, Weining verfasserin (orcid)0000-0002-8844-564X aut Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 Shu, Chang aut Hassan, Ali aut Attallah, Moataz M. (orcid)0000-0002-7074-9522 aut Essa, Khamis (orcid)0000-0001-6090-0869 aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 123(2022), 3-4 vom: 05. Okt., Seite 821-838 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 https://dx.doi.org/10.1007/s00170-022-10213-3 kostenfrei 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_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_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_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 123 2022 3-4 05 10 821-838 |
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10.1007/s00170-022-10213-3 doi (DE-627)SPR04841705X (SPR)s00170-022-10213-3-e DE-627 ger DE-627 rakwb eng Li, Weining verfasserin (orcid)0000-0002-8844-564X aut Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 Shu, Chang aut Hassan, Ali aut Attallah, Moataz M. (orcid)0000-0002-7074-9522 aut Essa, Khamis (orcid)0000-0001-6090-0869 aut Enthalten in The international journal of advanced manufacturing technology London : Springer, 1985 123(2022), 3-4 vom: 05. Okt., Seite 821-838 (DE-627)270127712 (DE-600)1476510-X 1433-3015 nnns volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 https://dx.doi.org/10.1007/s00170-022-10213-3 kostenfrei 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_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_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_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 123 2022 3-4 05 10 821-838 |
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Enthalten in The international journal of advanced manufacturing technology 123(2022), 3-4 vom: 05. Okt., Seite 821-838 volume:123 year:2022 number:3-4 day:05 month:10 pages:821-838 |
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Li, Weining @@aut@@ Shu, Chang @@aut@@ Hassan, Ali @@aut@@ Attallah, Moataz M. @@aut@@ Essa, Khamis @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR04841705X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509114328.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221026s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00170-022-10213-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR04841705X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00170-022-10213-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Weining</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-8844-564X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">High-temperature incremental sheet forming of Ti-6Al-4V</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning network</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tool design</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Geometric accuracy</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Surface roughness</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microstructural analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shu, Chang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hassan, Ali</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Attallah, Moataz M.</subfield><subfield code="0">(orcid)0000-0002-7074-9522</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Essa, Khamis</subfield><subfield code="0">(orcid)0000-0001-6090-0869</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The international journal of advanced manufacturing technology</subfield><subfield code="d">London : Springer, 1985</subfield><subfield code="g">123(2022), 3-4 vom: 05. 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Li, Weining |
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Li, Weining misc High-temperature incremental sheet forming of Ti-6Al-4V misc Machine learning network misc Tool design misc Geometric accuracy misc Surface roughness misc Microstructural analysis Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
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Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets High-temperature incremental sheet forming of Ti-6Al-4V (dpeaa)DE-He213 Machine learning network (dpeaa)DE-He213 Tool design (dpeaa)DE-He213 Geometric accuracy (dpeaa)DE-He213 Surface roughness (dpeaa)DE-He213 Microstructural analysis (dpeaa)DE-He213 |
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misc High-temperature incremental sheet forming of Ti-6Al-4V misc Machine learning network misc Tool design misc Geometric accuracy misc Surface roughness misc Microstructural analysis |
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misc High-temperature incremental sheet forming of Ti-6Al-4V misc Machine learning network misc Tool design misc Geometric accuracy misc Surface roughness misc Microstructural analysis |
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Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
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Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
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Li, Weining |
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The international journal of advanced manufacturing technology |
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Li, Weining Shu, Chang Hassan, Ali Attallah, Moataz M. Essa, Khamis |
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Elektronische Aufsätze |
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Li, Weining |
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10.1007/s00170-022-10213-3 |
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application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of ti-6al-4v sheets |
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Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
| abstract |
Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. © The Author(s) 2022 |
| abstractGer |
Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. © The Author(s) 2022 |
| abstract_unstemmed |
Abstract Induction heating-assisted single point incremental sheet forming was established for Ti-6Al-4V thin sheets at closed and above beta-transus temperature (980 °C). In order to eliminate geometric inaccuracy and adherence of lubricant on the surface caused by elevated temperature, a cooling lubricant system was designed for the forming tool to decrease the thermal expansion and friction. A radial basis function (RBF)-based tool path optimisation was developed to study the measured geometric accuracy, temperature, and forming force. By adjusting cooling lubricant control and integrating the RBF framework, the first optimised tool path was used to collect the results and to validate with the finite element (FE) model and theoretical geometric profiles. The output data were further studied by RBF and generate a second optimised tool path. The measured geometric coordinates revealed that the error percentage has been reduced to less than 5%. Further, the microstructure evolution analysed by scanning electron microscopy (SEM) indicated noticeable oxidation and alpha-layer for temperature around 1040 °C and the phenomenon was removed at temperature closed to 950 °C. The surface roughness and energy-dispersive X-ray analysis (EDX) revealed the optimised tool path distributed significant improvement in surface quality. The cooling lubricant system indicated optimal performance with RBF optimised tool path to support constant temperature and reduce friction and lubricant adherence on the surface. © The Author(s) 2022 |
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3-4 |
| title_short |
Application of machine learning on tool path optimisation and cooling lubricant in induction heating-assisted single point incremental sheet forming of Ti-6Al-4V sheets |
| url |
https://dx.doi.org/10.1007/s00170-022-10213-3 |
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| author2 |
Shu, Chang Hassan, Ali Attallah, Moataz M. Essa, Khamis |
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Shu, Chang Hassan, Ali Attallah, Moataz M. Essa, Khamis |
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| doi_str |
10.1007/s00170-022-10213-3 |
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2024-07-03T19:04:52.113Z |
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| score |
7.4013557 |