Melt blowing is an industrial approach for producing microfibrous nonwoven materials utilizing high-speed air to attenuate polymer melt. The melt-blowing air flow field which is widely believed to be turbulence determines the process of fiber formation. In this study, the turbulent air flow field in slot-die melt blowing was experimental measured by hot-wire anemometer. The fluctuations of air velocity and temperature, the mean velocity and mean temperature were measured and analyzed; moreover, the relationship between turbulent air flow field and fiber formation in melt blowing was discussed and predicted. In the last part of this paper, the coupling effect of air temperature and velocity was studied tentatively, results showed that air temperature not only had an enhanced effect on velocity, but contributed to the fluctuation of velocity. This work shows that the fluctuating characteristics of air velocity and temperature have dominant effect on fiber motion and the evenness of fiber diameter.

This study describes the application of a hybrid neuro-fuzzy inference system to control electrospinning process and how to use this approach for developing an electrospun fiber quality prediction system. An adaptive neuro-fuzzy inference system model has been applied to the use of electrospinning process parameters to study the relationship between electrospinning processing parameters and electrospun fiber morphology. Fiber morphology has been predicted and the impact of each processing parameter has been investigated. It was found that four electrospinning process parameters including: polymer solution concentration, spinning distance, applied voltage, and volume flow rate are the most influential factors to the electrospun fiber morphology. It was observed that the relationship existing between electrospinning processing parameters and nanofiber morphology is nonlinear.