Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Premasudha, Mookala and Bhumi Reddy, Srinivasulu Reddy and Lee, Yeon‐Ju and Panigrahi, Bharat B. and Cho, Kwon‐Koo and Nagireddy Gari, Subba Reddy (2021) Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter. Journal of Applied Polymer Science, 138 (11). p. 50014. ISSN 0021-8995

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Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide-based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The relationship between diameter and its quality with process parameters is complex and nonlinear. The backpropagation algorithm was used to train the ANN model and achieved the classification accuracy, precision, and recall of 93.9%, 95.2%, and 95.2%, respectively. The average errors of the predicted fiber diameter for training and unseen testing data were found to be 0.05% and 2.6%, respectively. A stand-alone ANN software was designed to extract information on the electrospinning system from a small experimental database. It was successful in establishing the relationship between electrospinning process parameters and fiber quality and diameter. The yield of smaller diameter with good quality was favored by lower feed rate, lower polymer solution concentration, and higher applied voltage.

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IITH Creators:
IITH CreatorsORCiD
Panigrahi, Bharat BhooshanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: Classification accuracy; Electrospinning process; Experimental database; Extract informations; Polymer concentrations; Process parameters; Solution concentration; Spinning distance;Backpropagation; Biopolymers; Electrospinning; Nanofibers; Starch
Subjects: Others > Metallurgy Metallurgical Engineering
Materials Engineering > Materials engineering
Divisions: Department of Material Science Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 27 Jul 2021 04:35
Last Modified: 27 Jul 2021 04:35
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