A neural network model for the numerical prediction of the diameter of electro-spun polyethylene oxide nanofibers

Electrospinning predictions using artificial neural networks

“…In literature this is often reported as nominal electric field strength in kV/cm, the distance referring to the gap between the nozzle tip and collector [39,40,46].…”

“…Some report the flow rate is a parameter that can be used to control the final fibre diameter whilst others report it has no statistical significance [30,39,46,49,62]. In the case of stable electrospinning where the meniscus at the spinning tip maintains constant shape, the flow rate into the Taylor cone must balance the rate of drawing from the jet.…”

“…Sarkar et al and Klanlou et al trained artificial neural networks (ANNs) to predict fibre diameters for PEO and poly(methyl methacrylate) (PMMA) respectively [46,47]. Sarkar was able to predict fibre diameters over several datasets taken from literature; however this data contained a priori solution measurements.…”

Artificial Neural Networks: Applications in Nanotechnology

“…Performing the sensitivity analysis on the model, it was indicated that the current density was the most important factor, while the temperature had the lowest impact on the grain size. In 2009, Sarkar et al (Sarkar et al, 2009) used ANNs as tools to study the diameter of electrospun nanofibers. The input variables in this study were concentration of the solution, electrical conductivity, flow rate, and strength of the electric field.…”

Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA

“…The NNs have been shown to be capable of building a class of flexible models which can be used for a variety of different applications, such as nonlinear regression and discrimination analysis (Li, Wu, & Zhang, 2008;Sarkar, Ben Ghlia, Wu, & Bose, 2009). With its ability to learn from the sample training set in a supervised or unsupervised manner, the NN might provide a promising alternative for the conventional regression equation approach on estimating SIFs.…”