Optimizing process parameters for the production of intercalated melt-blown nonwoven materials for face masks based on machine learning algorithms

Abstract

Currently a new type of coronavirus is raging around the world, and many countries have relaxed the control of the epidemic. Wearing a mask has become the best self-protection measure for people to travel. Intercalated melt-blown nonwoven materials are in short supply as filter layers for daily-worn masks. This paper studies the relationship between the process parameters and structural variables of intercalated melt-blown nonwoven materials, and creatively uses machine learning-related algorithms to solve its nonlinear relationship. The optimized back propagation neural network model is the most suitable in this field, and the goodness of fit can reach more than 99.99%. Based on various limitations of actual industrial production, this model is used to traverse the process parameters, and the intercalated melt-blown nonwoven material is obtained. The best process parameters, in which the receiving distance is 27 cm, and the hot air velocity is 890 r/min, in this case, the thickness and porosity of the material produced are very low, while the compression resilience is very high, considering the filtration efficiency of the mask and comfort.