Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (12): 75-81.doi: 10.13475/j.fzxb.20210607107

• Textile Engineering • Previous Articles     Next Articles

Prediction of pore dimension in composite nonwovens based on image simulation and support vector machine

JIN Guanxiu1(), ZHU Chengyan2   

  1. 1. Zhejiang Industry Polytechnic College, Shaoxing, Zhejiang 312000, China
    2. College of Textile Science and Engineering (International Silk Institute), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2021-06-24 Revised:2022-09-04 Online:2022-12-15 Published:2023-01-06

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Abstract:

In order to study the influence of nonwoven compounding on pore dimension in composite nonwovens between before and after compounded, the nonwoven webs was modeled based on the use of digital images taking into considerations of fiber number, the average fiber diameter and the variation coefficient of fiber diameter served as the model variables. Thus the image model of composite nonwoven was then obtained by superimposing two compound elements. The pore size percentage difference (Er) and the average pore size variation coefficient (Mv) of two compound elements were used to characterize the pore dimension of nonwoven before compounded. Furthermore, the pore size composite index (IP) and pore size variation coefficient composite index (IV) were used to characterize the pore dimension after compounding. The test results show that there is a complex non-linear relationship on pore dimension between before and after compounding. Er and Mv were used as the inputs of support vector machine model to predict IP and IV, respectively. The results indicate that the mean absolute percentage errors of the above two predictions are only 1.84% and 1.92%, respectively. The verification experiment result further confirms the high prediction accuracy of the support vector machine model.

Key words: composite nonwoven, pore size, pore size variation coefficient, digital image simulation, support vector machine

CLC Number: 

  • TS171

Tab.1

Modeling Scheme for compound element"

复合单元编号 n d/μm Vd/%
A 24 10 10
B 24 10 30
C 24 20 10
D 24 20 30
E 36 10 10
F 36 10 30
G 36 20 10
H 36 20 30

Fig.1

Simulating result of compound element using digital image processing technology"

Tab.2

Measuring results of pore size and its variation coefficient for all compound elements"

复合单元编号 孔径/μm 孔径变异系数/%
A 32.9 85.39
B 28.2 45.31
C 34.9 96.53
D 31.7 52.30
E 22.0 74.56
F 24.5 39.75
G 21.6 78.66
H 21.3 49.04

Tab.3

Composite scheme of superimposing two compound elements"

复合图
像编号		 复合
单元1		 复合
单元2		 复合图
像编号		 复合
单元1		 复合
单元2
1# A B 15# C E
2# A C 16# C F
3# A D 17# C G
4# A E 18# C H
5# A F 19# D E
6# A G 20# D F
7# A H 21# D G
8# B C 22# D H
9# B D 23# E F
10# B E 24# E G
11# B F 25# E H
12# B G 26# F G
13# B H 27# F H
14# C D 28# G H

Fig.2

Simulation on superimposing two compound elements"

Tab.4

Measuring results of pore size and its variation coefficient for all composite images"

复合图
像编号		 孔径/
μm		 孔径变异
系数/%		 复合图
像编号		 孔径/
μm		 孔径变异
系数/%
1# 12.9 140.65 15# 11.1 144.10
2# 12.7 196.72 16# 12.9 135.21
3# 13.1 163.23 17# 11.2 143.58
4# 10.2 132.76 18# 13.5 112.80
5# 14.2 116.82 19# 13.2 91.02
6# 10.1 131.49 20# 15.8 46.09
7# 13.0 97.57 21# 12.7 100.23
8# 12.5 166.76 22# 14.4 41.56
9# 15.3 67.15 23# 10.9 109.64
10# 12.2 94.80 24# 8.6 158.53
11# 15.5 50.27 25# 9.6 133.69
12# 12.1 101.79 26# 10.4 113.51
13# 13.8 42.75 27# 12.8 55.69
14# 12.8 187.31 28# 9.3 147.38

Tab.5

Pore dimension of images before and after compounded"

复合图像编号 Er/% MV/% IP IV 复合图像编号 Er/% MV/% IP IV
1# 16.67 65.35 0.457 4 1.647 1 15# 58.64 85.55 0.504 5 1.492 8
2# 6.08 90.96 0.386 0 2.037 9 16# 42.45 68.14 0.526 5 1.400 7
3# 3.79 68.85 0.413 2 1.911 6 17# 61.57 87.60 0.518 5 1.487 4
4# 49.55 79.98 0.463 6 1.554 7 18# 63.85 72.79 0.633 8 1.168 5
5# 34.29 62.57 0.579 6 1.368 1 19# 44.09 63.43 0.600 0 1.220 8
6# 52.31 82.03 0.467 6 1.539 9 20# 29.39 46.03 0.644 9 0.881 3
7# 54.46 67.22 0.610 3 1.142 6 21# 46.76 65.48 0.588 0 1.274 2
8# 23.76 70.92 0.443 3 1.727 5 22# 48.83 50.67 0.676 1 0.884 6
9# 12.41 48.81 0.542 6 1.283 9 23# 11.36 57.16 0.495 5 1.470 5
10# 28.18 59.94 0.554 5 1.271 5 24# 1.85 76.61 0.398 1 2.015 4
11# 15.10 42.53 0.632 7 1.109 5 25# 3.29 61.80 0.450 7 1.793 1
12# 30.56 61.99 0.560 2 1.294 1 26# 13.43 59.21 0.481 5 1.443 0
13# 32.39 47.18 0.647 9 0.871 7 27# 15.02 44.40 0.600 9 1.135 6
14# 10.09 74.42 0.403 8 1.940 4 28# 1.41 63.85 0.436 6 1.873 6

Tab.6

Optimized parameters and prediction accuracy of SVM model"

预测参数 优化后的结构参数 U/%
W FP ε
IP 67 2 600 0.001 1.84
IV 69 2 800 0.002 1.92

Tab.7

Composite scheme of nonwoven samples"

试样编号 复(叠)合单元
T1 S1+M1
T2 S1+M2
T3 S2+M1
T4 S2+M2

Tab.8

Test results of pore size and its variation coefficient for composite nonwovens before and after compound (superimposed)"

试样编号 孔径/
μm		 孔径变异系数/
%
复(叠)合前 S1 26.5 105.83
S2 30.9 76.40
M1 19.8 16.32
M2 14.1 25.81
复(叠)合后 T1 10.3 173.84
T2 9.7 160.12
T3 14.2 69.17
T4 12.3 66.10

Tab.9

Pore dimension of nonwovens before and after compounded (superimposed)"

试样编号 ErT/% MVT/% IPT IVT
T1 12.39 63.60 0.472 5 1.642 6
T2 33.88 67.82 0.530 1 1.513 0
T3 33.49 48.89 0.651 4 0.905 4
T4 59.02 53.11 0.672 1 0.865 2

Tab.10

Results of verification experiment%"

试样编号 孔径复合指数 孔径变异系数复合指数
T1 3.72 0.39
T2 3.73 1.51
T3 0.85 0.49
T4 1.83 4.67
[1] IRZMANSKA E, JURCZYK K M, BIL M, et al. Experimental investigation of the properties of laminated nonwovens used for packaging of powders in mineral warmers[J]. Autex Research Journal, 2021, 21(2): 163-171.
doi: 10.2478/aut-2019-0078
[2] 虞胜椿, 肖春晓, 徐鹤年, 等. 亲水性纺粘-熔喷复合非织造材料的制备及性能[J]. 现代丝绸科学与技术, 2020, 35(6): 17-19.
YU Shengchun, XIAO Chunxiao, XU Henian, et al. Preparation and properties of hydrophilic spun-bonded/melt blown composite nonwovens[J]. Modern Silk Science & Technology, 2020, 35(6): 17-19.
[3] WANG H, ZHANG Y, GAO H P. Composite melt-blown nonwoven fabrics with large pore size as Li-ion battery separator[J]. International Journal of Hydrogen Energy, 2016, 41(1): 324-330.
doi: 10.1016/j.ijhydene.2015.09.130
[4] 吴芳, 王士勇, 都丽红, 等. 非织造过滤介质孔径及截留性能的研究[J]. 无机盐工业, 2020, 52(2): 38-42.
WU Fang, WANG Shiyong, DU Lihong, et al. Study on aperture performance and rejection performance of nonwoven filter media[J]. Inorganic Chemicals Industry, 2020, 52(2): 38-42.
[5] AYDILEK A H, ASCE A M, OGUZ S H. Digital image analysis to determine pore opening size distribution of nonwoven geotextiles[J]. Journal of Computing in Civil Engineering, 2002(16): 280-290.
[6] AYDILEK A H, OGUZ S H, EDIL T B. Constriction size of geotextile filters[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2005(131): 28-37.
[7] SIMMONDS G E, BOMBERGER J D, BRYNER M A. Designing nonwovens to meet pore size specifica-tions[J]. Journal of Engineering Fibers and Fabrics, 2007(2): 1-15.
[8] 潘莺, 王善元. 模拟熔喷纤网的孔径分布及其对过滤性能的影响[J]. 中国纺织大学学报, 2000, 26(5): 73-77.
PAN Ying, WANG Shanyuan. Simulation of melt blown web and the effect of its pore size distribution on filtration performance[J]. Journal of China Textile University, 2000, 26(5): 73-77.
[9] 杨旭红. 非织造材料(纤维网)形态结构的表征与分形模拟[D]. 苏州: 苏州大学, 2003: 76-87.
YANG Xuhong. Expression and fractal simulation of morphologic structures of nonwovens[D]. Suzhou: Soochow University, 2003: 76-87.
[10] DIMASSI M, KOEHL L, ZENG X, et al. Pore network modelling using image processing techniques[J]. International Journal of Clothing Science and Technology, 2008, 20 (3): 137-149.
doi: 10.1108/09556220810865193
[11] SUN L, LI Y, DU H, et al. Fault diagnosis method of low noise amplifier based on support vector machine and hidden Markov model[J]. Journal of Electronic Testing, 2021(6): 1-9.
[12] 安琪, 付译鋆, 张瑜. 医用防护服用非织造材料的研究进展[J]. 纺织学报, 2020, 41(8): 188-196.
AN Qi, FU Yijun, ZHANG Yu. Research progress of nonwovens for medical protective garment[J]. Journal of Textile Research, 2020, 41(8): 188-196.
[13] MORTEZA V, ASGHAR A Y A. Modeling the spunbonded nonwoven fabric bursting strength using finite element method[J]. Journal of The Textile Institute, 2021, 112(5): 837-845.
doi: 10.1080/00405000.2020.1783061
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