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- W2023559716 abstract "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." @default.
- W2023559716 created "2016-06-24" @default.
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- W2023559716 date "2015-02-01" @default.
- W2023559716 modified "2023-10-09" @default.
- W2023559716 title "Electrospinning predictions using artificial neural networks" @default.
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- W2023559716 doi "https://doi.org/10.1016/j.polymer.2014.12.046" @default.
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