Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling
Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Choi, Minki [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© The Korean Fiber Society 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Fibers and polymers - Seoul : The Korean Fiber Society, 2000, 20(2019), 9 vom: Sept., Seite 1883-1892 |
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| Übergeordnetes Werk: |
volume:20 ; year:2019 ; number:9 ; month:09 ; pages:1883-1892 |
| Links: |
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| DOI / URN: |
10.1007/s12221-019-1094-z |
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SPR025446959 |
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| 520 | |a Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. | ||
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| 700 | 1 | |a Kim, Jooyong |4 aut | |
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10.1007/s12221-019-1094-z doi (DE-627)SPR025446959 (SPR)s12221-019-1094-z-e DE-627 ger DE-627 rakwb eng Choi, Minki verfasserin aut Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society 2019 Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 Kim, Jooyong aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 20(2019), 9 vom: Sept., Seite 1883-1892 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:20 year:2019 number:9 month:09 pages:1883-1892 https://dx.doi.org/10.1007/s12221-019-1094-z 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_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 20 2019 9 09 1883-1892 |
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10.1007/s12221-019-1094-z doi (DE-627)SPR025446959 (SPR)s12221-019-1094-z-e DE-627 ger DE-627 rakwb eng Choi, Minki verfasserin aut Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society 2019 Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 Kim, Jooyong aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 20(2019), 9 vom: Sept., Seite 1883-1892 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:20 year:2019 number:9 month:09 pages:1883-1892 https://dx.doi.org/10.1007/s12221-019-1094-z 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_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 20 2019 9 09 1883-1892 |
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10.1007/s12221-019-1094-z doi (DE-627)SPR025446959 (SPR)s12221-019-1094-z-e DE-627 ger DE-627 rakwb eng Choi, Minki verfasserin aut Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society 2019 Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 Kim, Jooyong aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 20(2019), 9 vom: Sept., Seite 1883-1892 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:20 year:2019 number:9 month:09 pages:1883-1892 https://dx.doi.org/10.1007/s12221-019-1094-z 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_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 20 2019 9 09 1883-1892 |
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10.1007/s12221-019-1094-z doi (DE-627)SPR025446959 (SPR)s12221-019-1094-z-e DE-627 ger DE-627 rakwb eng Choi, Minki verfasserin aut Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society 2019 Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 Kim, Jooyong aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 20(2019), 9 vom: Sept., Seite 1883-1892 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:20 year:2019 number:9 month:09 pages:1883-1892 https://dx.doi.org/10.1007/s12221-019-1094-z 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_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 20 2019 9 09 1883-1892 |
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10.1007/s12221-019-1094-z doi (DE-627)SPR025446959 (SPR)s12221-019-1094-z-e DE-627 ger DE-627 rakwb eng Choi, Minki verfasserin aut Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Fiber Society 2019 Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 Kim, Jooyong aut Enthalten in Fibers and polymers Seoul : The Korean Fiber Society, 2000 20(2019), 9 vom: Sept., Seite 1883-1892 (DE-627)565516485 (DE-600)2424081-3 1875-0052 nnns volume:20 year:2019 number:9 month:09 pages:1883-1892 https://dx.doi.org/10.1007/s12221-019-1094-z 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_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_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 20 2019 9 09 1883-1892 |
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Enthalten in Fibers and polymers 20(2019), 9 vom: Sept., Seite 1883-1892 volume:20 year:2019 number:9 month:09 pages:1883-1892 |
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Enthalten in Fibers and polymers 20(2019), 9 vom: Sept., Seite 1883-1892 volume:20 year:2019 number:9 month:09 pages:1883-1892 |
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Choi, Minki @@aut@@ Kim, Jooyong @@aut@@ |
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| author |
Choi, Minki |
| spellingShingle |
Choi, Minki misc Nanofiber misc Coaxial air blown misc Electrospinning misc Neuro-fuzzy logic Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling |
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Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling Nanofiber (dpeaa)DE-He213 Coaxial air blown (dpeaa)DE-He213 Electrospinning (dpeaa)DE-He213 Neuro-fuzzy logic (dpeaa)DE-He213 |
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Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling |
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Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling |
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Choi, Minki |
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Choi, Minki |
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10.1007/s12221-019-1094-z |
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development of coaxial air-blown electrospinning process for manufacturing non-woven nanofiber. ii. intelligent modeling |
| title_auth |
Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling |
| abstract |
Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. © The Korean Fiber Society 2019 |
| abstractGer |
Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. © The Korean Fiber Society 2019 |
| abstract_unstemmed |
Abstract This study describes the development of an electro-coaxial air-blown spinning system, using a sheath-core type nozzle and an intelligent model for predicting the morphological changes induced by varying four process parameters. We prepared 65 samples with different parametric conditions and analyzed their characteristics. We could confirm that the mean fiber diameter decreased with increase in the air flow rate and voltage, and increased with an increase in the solution concentration. A too short tip-to-collector distance (TCD) resulted in flat and thick fiber deposition, and no changes in fiber diameter were observed beyond a specific TCD value. Based on experimentally measured morphological data, we could establish an intelligent prediction model with four inputs (air flow rate, solution concentration, applied voltage, and TCD) and one output (mean fiber diameter). The simple linear regression analysis model and multiple linear regression analysis model were found to yield mean squared error (MSE) values too high to be used as predictive models. The neural network training model and fuzzy logic model were confirmed to have low MSE values. Among the three prediction models, the neural network training model was confirmed to have the smallest error between the experimental and predicted values. The results of the analysis of the BET specific surface area confirmed the same tendencies as those shown by the image analysis results, which allowed us to conclude that electrospinning using a coaxial air-blown nozzle can enhance the performance of nonwoven nanofibers. © The Korean Fiber Society 2019 |
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Development of Coaxial Air-blown Electrospinning Process for Manufacturing Non-woven Nanofiber. II. Intelligent Modeling |
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https://dx.doi.org/10.1007/s12221-019-1094-z |
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Kim, Jooyong |
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