Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

With regard to the fact that currently there is no comprehensive method to predict diameter of polyurethane/solvent fiber from electrospinning, in this study, diameter prediction of polyurethane/solvent fiber was conducted using neural networks and an error of 166 nm was observed. This error shows that artificial neural networks (ANNs) can predict diameter of electrospinning polyurethane fibers well. Then, considering weak repeatability nature of electrospinning in fabricating fibers with desired diameter, least mean square is used to improve stability of neural network model that shows an error of 113 nm, which represented better results compared to common ANN. To investigate the effect of each one of parameters affecting fiber diameter, sensitivity analysis was conducted. Along with this predicting model, sensitivity analysis can be used to reduce parameters space before conducting future studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45116.