Research on property control of intercalated melt-blown nonwoven materials based on correlation analysis and neural network model

Huixin Li; Yuqi Zhou; Xin Kang

doi:10.54097/hset.v55i.9955

Authors

Huixin Li
Yuqi Zhou
Xin Kang

DOI:

https://doi.org/10.54097/hset.v55i.9955

Keywords:

intercalation melt-blowout method, multiple linear regression, Spearman correlation coefficient, canonical correlation analysis, neural network.

Abstract

Intercalated melt-blown nonwovens can solve the poor compression resilience caused by melt-blown nonwovens fibers, which is helpful to improve the performance of products, so it is of great significance to study the performance control of intercalated melt-blown nonwovens. In this paper, correlation analysis, BP neural network model and canonical correlation analysis model were constructed to analyze the changes of product performance before and after intercution. Finally, the optimal production process parameters were determined as the acceptance distance of 22.98 cm and the hot air speed of 1790 r/min through the construction of the model, which provided a certain theoretical basis for the product performance control mechanism.

Downloads

Download data is not yet available.

References

Peng M, Jia H, Jiang L, et al. Study on structure and property of PP/TPU melt-blown nonwovens[J]. The Journal of The Textile Institute, 2019, 110(3): 468-475.

Dorigato A, Brugnara M, Giacomelli G, et al. Thermal and mechanical behavior of innovative melt-blown fabrics based on polyamide nanocomposites[J]. Journal of Industrial Textiles, 2016, 45(6): 1504-1515.

Kim C, Yang K S, Ko J M, et al. Effect of Stabilization Conditions on the Microstructure and Electrochemical Properties of Melt-blown Graphite Fibers Prepared from NMP[J]. Journal of the Korean Electrochemical Society, 2001, 4(3): 104-108.

Zhu F, Su J, Zhao Y, et al. Influence of halloysite nanotubes on poly (lactic acid) melt-blown nonwovens compatibilized by dual-monomer melt-grafted poly (lactic acid) [J]. Textile Research Journal, 2019, 89(19-20): 4173-4185.

Hong S I, Rhim J W. Preparation and properties of melt-intercalated linear low density polyethylene/clay nanocomposite films prepared by blow extrusion [J]. Lwt-food science and technology, 2012, 48(1): 43-51.

Ding S, Su C, Yu J. An optimizing BP neural network algorithm based on genetic algorithm[J]. Artificial intelligence review, 2011, 36(2): 153-162.

Li J, Cheng J, Shi J, et al. Brief introduction of back propagation (BP) neural network algorithm and its improvement[M]//Advances in computer science and information engineering. Springer, Berlin, Heidelberg, 2012: 553-558.

Ray S S, Okamoto K, Okamoto M. Structure and properties of nanocomposites based on poly (butylene succinate) and organically modified montmorillonite[J]. Journal of applied polymer science, 2006, 102(1): 777-785.

Zhang S, Liu H, Yu J, et al. Microwave structured polyamide-6 nanofiber/net membrane with embedded poly (m-phenylene isophthalamide) staple fibers for effective ultrafine particle filtration[J]. Journal of Materials Chemistry A, 2016, 4(16): 6149-6157.

Zhang H, Zhen Q, Liu Y, et al. One-step melt blowing process for PP/PEG micro-nanofiber filters with branch networks [J]. Results in Physics, 2019, 12: 1421-1428.