Abstract
Melt-blown nonwovens have fine fiber diameter, excellent filtration performance, small pores, and large specific surface area. However, the fluffiness of the material is not good enough. Adding conventional fibers into the melt-blown material can increase the thickness and fluffiness of the material, and improve the sound-absorbing performance and heat preservation performance of the material. To study how much fiber can be added to make melt-blown nonwovens better, grey correlation analysis is used to explore the influence of intercalation rate on each parameter. BP neural network is used for training, with process parameters as the input index and structural variables as the output index, and the results are obtained. According to the principle of statistics, the data of the results are analyzed and the process parameters of the highest filtration are obtained. Finally, the random forest regression model is used to establish the objective programming function to predict the added constraints.
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