Study on the Optimization of Double Parameters of the Air Flow Resistance and the Permeability of Electrospun Nanofiber Nonwovens

In this paper, neural network is used as the tool to study the factors affecting the air flow resistance and the permeability of electrospun nanofiber nonwovens and analyze the major factors affecting the air flow resistance and the permeability such as concentration, distance, voltage and solution filling speed. First, design a five-level orthogonal table for all factors in accordance with the orthogonal experiment theory, select the corresponding parameter values, use polyvinyl alcohol (PVA) to prepare 50 samples on DXES-01 automatic electrostatic spinning machine, train them with neural network model and obtain the precise fitting function. The optimization function is constructed by the idea of two- objective optimization, and its three relative optimal values are calculated, 8.135611, 8.134624, 8.115814. Compared with the experimental results, the average relative error is 12.89 and 8.34. The experimental results show that the error is also ideal.