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A Neural Network Approach for Predicting the Diameters of Electrospun Polyvinylacetate (PVAc) Nanofibers

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Engineering Applications of Neural Networks (EANN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 744))

Abstract

This study focuses on the design of a Neural Network (NN) model for the prediction of interpolated values of polyvinylacetate (PVAc) nanofiber diameters produced by the electrospinning process and it supposes to be a preliminary work for future and industrial applications. The experimental data gathered from the literature form the basis for generating a more consistent sample through standard interpolation. The inputs of the NN are the polymer concentration, the applied voltage, the nozzle-collector distance and the flow rate parameters of the process, whereas the average diameter acts as the unique output of the network. The generated model is able to approximate the mapping between process parameters and fiber morphology, which is of practical importance to help prepare homogeneous nano-fibers. The reliability of the model was tested by 7-fold cross validation as well as leave-one-out method, showing good performance in terms of both average RMSE (0.109, corresponding to 138.51 nm) and correlation coefficient (0.905) between the desired and the predicted diameters when a White Gaussian Noise with 2% power (WGN2%) is applied to the interpolations.

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Authors and Affiliations

  1. DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Cosimo Ieracitano, Patrizia Frontera & Francesco Carlo Morabito

  2. DIIES, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Fabiola Pantò

Authors
  1. Cosimo Ieracitano
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  2. Fabiola Pantò
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  3. Patrizia Frontera
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  4. Francesco Carlo Morabito
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Corresponding author

Correspondence to Cosimo Ieracitano .

Editor information

Editors and Affiliations

  1. Politecnico di Milano, Milan, Italy

    Giacomo Boracchi

  2. Democritus University of Thrace, University Campus, Xanthi, Greece

    Lazaros Iliadis

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Univesity of Ioannina, Ioannina, Greece

    Aristidis Likas

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Ieracitano, C., Pantò, F., Frontera, P., Morabito, F.C. (2017). A Neural Network Approach for Predicting the Diameters of Electrospun Polyvinylacetate (PVAc) Nanofibers. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-65172-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65171-2

  • Online ISBN: 978-3-319-65172-9

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