Welcome to Francis Academic Press

Academic Journal of Materials & Chemistry, 2023, 4(5); doi: 10.25236/AJMC.2023.040503.

Performance Study of Intercalated Meltblown Nonwoven Materials Based on Gaussian Process Regression


Chen Huang1, Bingzhou Bao2

Corresponding Author:
Chen Huang

1School of Material and Chemical Engineering, Anhui Jianzhu University, Hefei, 230601, China

2School of Computer and Information Technology, Anhui Normal University, Wuhu, 241002, China


The demand for face masks is on the rise due to the COVID-19 pandemic. Melt-blown nonwoven material is an important raw material for mask production, and its structural variables determine the performance of the final product. In this paper, the prediction model between structural variables and product performance is established, and the relationship between structural variables and product performance is obtained by using Gaussian process regression method, and the prediction model is tested to further verify the reliability of the model. The experimental results show that. The prediction accuracy of the model based on Gaussian process regression is high, and the average absolute error is less than 0.2, so the prediction method is effective. The final experimental results have a certain degree of reference significance for the study of the influence of the structure variables of intercalated melt-blown nonwovens on the product properties, which is helpful to provide a certain theoretical basis for the establishment of the control mechanism of product properties.


Structure variables; Properties of products; Gaussian process regression; Intercalated melt-blown nonwoven material

Cite This Paper

Chen Huang, Bingzhou Bao. Performance Study of Intercalated Meltblown Nonwoven Materials Based on Gaussian Process Regression. Academic Journal of Materials & Chemistry (2023) Vol. 4, Issue 5: 18-24. https://doi.org/10.25236/AJMC.2023.040503.


[1] Bement T W, Mitros A, Lau R, et al. Filtration and breathability of nonwoven fabrics used in washable masks[J]. 2022.

[2] Zhang Houbing, Tu Hongyu, Chen Lihua, Wang Junsu, Liu Hao, Li Xianliang, Wang Guoliang. Development and application of a fully automatic tester for compression resilience and fluffiness of textiles and fibre products[J]. Chongqing Entry-Exit Inspection and Quarantine Bureau Inspection and Quarantine Technology Centre, 2017, 25(01): 60-64. 

[3] Zhang Mingjun, Liu Peisheng, Song Shuai. Influence of preparation process parameters on the structure and relative density of oversized graphite/Cu matrix composites[J]. Key Laboratory of the Ministry of Education for Radiation Beam Technology, School of Nuclear Science and Technology, Beijing Normal University, 2022, 32(02):406-415. 

[4] Zhang Guidong, Sheng Yuhong. Maximum likelihood estimation of variable parameters in uncertain differential equations [J]. Journal of Xinjiang University, 2022, 39(04):421-431. 

[5] Liu Dan. A study on the application of electron beam radiation therapy based on Monte Carlo method [D]. Tsinghua University, 2013, 37(04):14-18. 

[6] Ma Guangfu, Liu Yuhan, Lv Yueyong, Guo Yanning. Gaussian process regression-based attitude takeover learning control for combinatorial spacecraft[J]. Aerospace Shanghai, 2022, 39(04), 42-51. 

[7] Brown R. L., Durbin J. and Evans J. M. Techniques for Testing the Constancy of Regression Relationships over Time [J]. Journal of the Royal Statistical Society, 1975, 37(02):149-192. 

[8] Rongjun Qin. Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery [J]. ISPRS Journal of Photogrammetry and Remote Sensi, 2014(96):179-192. 

[9] Tong Lijing, Yang Fan. Application of probabilistic models to image classification [J]. School of Informatics, North China University of Technology, 2022, 40(08):1-3. 

[10] He Yingchao, Guo Jixin, Liu Han. Application of time series models for monitoring settlement deformation in metro tunnels[J]. China Hebei Construction&Geotechnical Investigation Group Limited, 2022, 45(09):238-240. 

[11] Yan Wei. Application of computer digital processing techniques in image processing [J]. Changsha University, 2022, 40(09):99-101. 

[12] Ye Lijun. Artificial intelligence-based automatic control systems for communications [J]. 2022, 41 (09): 36-39. 

[13] Gan Lu, Chen Hui. Statistical model modification and probabilistic structural damage identification based on Bayesian methods [J]. School of Electrical and Mechanical Engineering, School of Post and Information Engineering, Wuhan Institute of Technology, 2022, 44(01):133-139. 

[14] Yao Zhenhan. Boundary element method [M]. Higher Education Press, 2010. 

[15] Belen Martin-Barragan, Rosa Lillo, Juan Romo. Interpretable support vector machines for functional data [J]. European Journal of Operational Research, 2014(1). 

[16] Inagaki Takahiro, Inaba Kensuke, Leleu Timothée, et al. Collective and synchronous dynamics of photonic spiking neurons [J]. Nature Communications, 2021, 12(1). 

[17] Rouhou Ahmed Cheikh, Dhiaf Marwa, Kessentini Yousri, et al. Transformer-based approach for joint handwriting and named entity recognition in historical document[J]. Pattern Recognition Letters, 2021(prepublish)

[18] Zhai Hengdong, Li Limin, Yang Jun, Huo Yufeng, Deng Sihong, Jiang Qilin. Identification of steel slag temperature distribution based on conjugate gradient method [J]. MCC Baosteel Technology Services Co. Limited, 2022, (02), 1-8. 

[19] Maysoun Ibrahim, Ali El-Zaart, Carl Adams. Smart sustainable cities roadmap: Readiness for transformation towards urban sustainability[J]. Sustainable Cities and Society, 2018. 

[20] Zhou Bin, Luo Yaguang, Huang Lihan, et al. Determining effects of temperature abuse timing on shelf life of RTE baby spinach through microbial growth models and its association with sensory quality[J]. Food Control, 2022, 133(PB).