Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone
Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, p...
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| Autor*in: |
Khatti, Tahere [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© The Natural Computing Applications Forum 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 31(2017), 1 vom: 28. Apr., Seite 239-248 |
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| Übergeordnetes Werk: |
volume:31 ; year:2017 ; number:1 ; day:28 ; month:04 ; pages:239-248 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-017-2996-6 |
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OLC2025610270 |
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| 520 | |a Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. | ||
| 650 | 4 | |a Electrospining | |
| 650 | 4 | |a Nanofiber | |
| 650 | 4 | |a Artificial neural networks | |
| 650 | 4 | |a Response surface methodology | |
| 650 | 4 | |a Polycaprolactone | |
| 700 | 1 | |a Naderi-Manesh, Hossein |4 aut | |
| 700 | 1 | |a Kalantar, Seyed Mehdi |4 aut | |
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10.1007/s00521-017-2996-6 doi (DE-627)OLC2025610270 (DE-He213)s00521-017-2996-6-p DE-627 ger DE-627 rakwb eng 004 VZ Khatti, Tahere verfasserin aut Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. Electrospining Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Neural computing & applications Springer London, 1993 31(2017), 1 vom: 28. Apr., Seite 239-248 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:31 year:2017 number:1 day:28 month:04 pages:239-248 https://doi.org/10.1007/s00521-017-2996-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 31 2017 1 28 04 239-248 |
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10.1007/s00521-017-2996-6 doi (DE-627)OLC2025610270 (DE-He213)s00521-017-2996-6-p DE-627 ger DE-627 rakwb eng 004 VZ Khatti, Tahere verfasserin aut Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. Electrospining Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Neural computing & applications Springer London, 1993 31(2017), 1 vom: 28. Apr., Seite 239-248 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:31 year:2017 number:1 day:28 month:04 pages:239-248 https://doi.org/10.1007/s00521-017-2996-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 31 2017 1 28 04 239-248 |
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10.1007/s00521-017-2996-6 doi (DE-627)OLC2025610270 (DE-He213)s00521-017-2996-6-p DE-627 ger DE-627 rakwb eng 004 VZ Khatti, Tahere verfasserin aut Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. Electrospining Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Neural computing & applications Springer London, 1993 31(2017), 1 vom: 28. Apr., Seite 239-248 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:31 year:2017 number:1 day:28 month:04 pages:239-248 https://doi.org/10.1007/s00521-017-2996-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 31 2017 1 28 04 239-248 |
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10.1007/s00521-017-2996-6 doi (DE-627)OLC2025610270 (DE-He213)s00521-017-2996-6-p DE-627 ger DE-627 rakwb eng 004 VZ Khatti, Tahere verfasserin aut Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. Electrospining Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Neural computing & applications Springer London, 1993 31(2017), 1 vom: 28. Apr., Seite 239-248 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:31 year:2017 number:1 day:28 month:04 pages:239-248 https://doi.org/10.1007/s00521-017-2996-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 31 2017 1 28 04 239-248 |
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10.1007/s00521-017-2996-6 doi (DE-627)OLC2025610270 (DE-He213)s00521-017-2996-6-p DE-627 ger DE-627 rakwb eng 004 VZ Khatti, Tahere verfasserin aut Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2017 Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. Electrospining Nanofiber Artificial neural networks Response surface methodology Polycaprolactone Naderi-Manesh, Hossein aut Kalantar, Seyed Mehdi aut Enthalten in Neural computing & applications Springer London, 1993 31(2017), 1 vom: 28. Apr., Seite 239-248 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:31 year:2017 number:1 day:28 month:04 pages:239-248 https://doi.org/10.1007/s00521-017-2996-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 31 2017 1 28 04 239-248 |
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Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone |
| abstract |
Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. © The Natural Computing Applications Forum 2017 |
| abstractGer |
Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. © The Natural Computing Applications Forum 2017 |
| abstract_unstemmed |
Abstract Due to increasing application of nanofibers in many research fields, comprehensive knowledge of the electrospinning process as the most popular method of fiber production is essential. Modeling techniques are valuable tools for managing contributing factors in the electrospinning process, prior to the more expensive experimental techniques. In the present research, effective parameters on the diameter of electrospun polycaprolactone (PCL) nanofibers are analyzed using artificial neural networks (ANN) and response surface methodology (RSM). The assessed parameters include polymer concentration, voltage, and nozzle-to-collector distance. Response surface methodology based on the Box-Behnken design is utilized to develop a mathematical model as well as to determine the optimum condition for production of nanofiber with minimum diameter. In addition, multilayer perceptron neural networks are designed and trained by the sets of input-output patterns using the Levenberg-Marquardt backpropagation algorithm. The high regression coefficient value (R2 ≥ 0.97) and low root-mean-square error (RMSE ≤3.81) of the two models indicate that both models performed well in predicting PCL fiber diameter, although the RSM model slightly outperformed the ANN model in accuracy. The represented models could assist researchers in fabricating electrospun scaffolds with a defined fiber diameter, thus specializing such scaffolds in particular applications. © The Natural Computing Applications Forum 2017 |
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| title_short |
Application of ANN and RSM techniques for modeling electrospinning process of polycaprolactone |
| url |
https://doi.org/10.1007/s00521-017-2996-6 |
| remote_bool |
false |
| author2 |
Naderi-Manesh, Hossein Kalantar, Seyed Mehdi |
| author2Str |
Naderi-Manesh, Hossein Kalantar, Seyed Mehdi |
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| doi_str |
10.1007/s00521-017-2996-6 |
| up_date |
2024-07-04T01:41:54.732Z |
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