Electrospinning predictions using artificial neural networks

Brooks, Hadley and Tucker, Nick (2015) Electrospinning predictions using artificial neural networks. Polymer, 58 . pp. 22-29. ISSN 0032-3861

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Electrospinning is a relatively simple method of producing nanofibres. Currently there is no method to predict the characteristics of electrospun fibres produced from a wide range of polymer/solvent combinations and concentrations without first measuring a number of solution properties. This paper shows how artificial neural networks can be trained to make electrospinning predictions using only commonly available prior knowledge of the polymer and solvent. Firstly, a probabilistic neural network was trained to predict the classification of three possibilities: no fibres (electrospraying); beaded fibres; and smooth fibres with >80% correct predictions. Secondly, a generalised neural network was trained to predict fibre diameter with an average absolute percentage error of 22.3% for the validation data. These predictive tools can be used to reduce the parameter space before scoping exercises.

Keywords:Electrospinning, Neural networks, NotOAChecked
Subjects:H Engineering > H660 Control Systems
Divisions:College of Science > School of Engineering
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ID Code:19644
Deposited On:22 Nov 2015 20:18

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