Abstract
There are many factors affecting the preparation of intercalation melt-blown nonwovens, and scientists are committed to studying the conditions required for optimal product performance. Therefore, in order to study the optimal conditions, this paper first uses multiple linear regression to explore the correlation between intercalation rate and the change rule. It is found that intercalation rate is related to the change rule of thickness and filtration resistance, but has nothing to do with other indicators. In order to explore the relationship between process parameters and structure variables, a multi-layer perceptron (BP neural network) model with process parameters as input and structure variables as output is established. The relationship between input and output is obtained by using the trained network and the functional relationship is calculated. By analyzing the function, it is found that the correlation between the receiving distance and thickness and porosity is stronger than the positive correlation between the hot air speed and the compression resilience is stronger than the negative correlation between the hot air speed. The correlation between hot air velocity and thickness and porosity is weaker than that of receiving distance, and the correlation between hot air velocity and compressive resilience is weaker than that of receiving distance.
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