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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.

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In this study, the diameter of polyvinyl butyral nanofibers was modeled using response surface method based on three variables, at three levels of central composite design and particle swarm optimization-least squares support vector machine. Under optimal conditions, the measured mean diameter of the nanofibers was 175 nm. Sensitivity analysis in both models showed that polyvinyl butyral concentration in the solution was found to be the most effective parameter on the nanofiber diameter. The voltage is placed in the next. Fracture toughness under Mode I condition shows that the use of electrospun nanowebs as an interlayer in the structure of multi-layers composite has a positive effect on the GIc which values for the oriented and random nanofibers modified samples increased by 60% and 55%, respectively. According to SEM images, the main mechanism of fracture toughness in these samples was crack deflection and nanofibers crack bridging.