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Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter
Author(s) -
Premasudha Mookala,
Bhumi Reddy Srinivasulu Reddy,
Lee YeonJu,
Panigrahi Bharat B.,
Cho KwonKoo,
Nagireddy Gari Subba Reddy
Publication year - 2021
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.50014
Subject(s) - electrospinning , nanofiber , materials science , starch , artificial neural network , backpropagation , polymer , fiber , composite material , artificial intelligence , biological system , chemical engineering , computer science , chemistry , engineering , biochemistry , biology
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.