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A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers

Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and e...

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Autores principales: Solis-Rios, Daniel, Villarreal-Gómez, Luis Jesús, Goyes, Clara Eugenia, Fonthal Rico, Faruk, Cornejo-Bravo, José Manuel, Fong-Mata, María Berenice, Calderón Arenas, Jorge Mario, Martínez Rincón, Harold Alberto, Mejía-Medina, David Abdel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386166/
https://www.ncbi.nlm.nih.gov/pubmed/37512721
http://dx.doi.org/10.3390/mi14071410
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author Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Fonthal Rico, Faruk
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
author_facet Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Fonthal Rico, Faruk
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
author_sort Solis-Rios, Daniel
collection PubMed
description Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted.
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spelling pubmed-103861662023-07-30 A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers Solis-Rios, Daniel Villarreal-Gómez, Luis Jesús Goyes, Clara Eugenia Fonthal Rico, Faruk Cornejo-Bravo, José Manuel Fong-Mata, María Berenice Calderón Arenas, Jorge Mario Martínez Rincón, Harold Alberto Mejía-Medina, David Abdel Micromachines (Basel) Article Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted. MDPI 2023-07-12 /pmc/articles/PMC10386166/ /pubmed/37512721 http://dx.doi.org/10.3390/mi14071410 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Solis-Rios, Daniel
Villarreal-Gómez, Luis Jesús
Goyes, Clara Eugenia
Fonthal Rico, Faruk
Cornejo-Bravo, José Manuel
Fong-Mata, María Berenice
Calderón Arenas, Jorge Mario
Martínez Rincón, Harold Alberto
Mejía-Medina, David Abdel
A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title_full A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title_fullStr A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title_full_unstemmed A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title_short A Neural Network Approach to Reducing the Costs of Parameter-Setting in the Production of Polyethylene Oxide Nanofibers
title_sort neural network approach to reducing the costs of parameter-setting in the production of polyethylene oxide nanofibers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10386166/
https://www.ncbi.nlm.nih.gov/pubmed/37512721
http://dx.doi.org/10.3390/mi14071410
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