Brooks, Hadley Laurence and Tucker, Nick (2015) Electrospinning predictions using artificial neural networks. Polymer, 58 . pp. 22-29. ISSN 0032-3861
- Draft Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Official URL: http://dx.doi.org/10.1016/j.polymer.2014.12.046
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.
|Uncontrolled Keywords (separate with ;):||Artificial neural network; Electrospinning; Prediction|
|Schools:||Faculty of Science and Technology > School of Engineering|
|Deposited By:||Hadley Laurence Brooks|
|Deposited On:||18 Aug 2015 10:36|
|Last Modified:||24 Oct 2016 03:11|
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