Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter |
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| Authors: | Mookala Premasudha Srinivasulu Reddy Bhumi Reddy Yeon-Ju Lee Bharat B Panigrahi Kwon-Koo Cho Subba Reddy Nagireddy Gari |
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| Affiliation: | 1. Department of Materials Engineering and Convergence Technology and RIGET, Gyeongsang National University, Jinju, South Korea;2. Department of Materials Science and Metallurgical Engineering, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India;3. Virtual Materials Lab, School of Materials Science and Engineering, Gyeongsang National University, Jinju, South Korea |
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| Abstract: | 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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| Keywords: | applications biopolymers and renewable polymers mechanical preperties |
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