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.

A neural network approach for predicting the diameters of electrospun polyvinylacetate (PVAc) nanofibers

Frontera P;MORABITO, Francesco Carlo
2017

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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12318/16606
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