Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (03): 78-82.doi: 10.13475/j.fzxb.20210701205

• Textile Engineering • Previous Articles     Next Articles

Evaluation of anti-pilling performance of sized yarns based on hairiness image detection

GUO Min, WANG Jing'an, GUO Mingrui, GAO Weidong()   

  1. Key Laboratory of Eco-Textiles(Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2021-07-01 Revised:2021-09-29 Online:2022-03-15 Published:2022-03-29
  • Contact: GAO Weidong E-mail:gaowd3@163.com

RichHTML

20

PDF (PC)

100

Abstract:

In order to evaluate accurately the hairiness of sized yarns before and after weaving and characterize effectively the anti-pilling performance of sizing, an evaluation method of anti-pilling performance of sized yarns based on image detection of hairiness was proposed. Cotton raw yarns of 14.5 tex and sized yarn with three types of sizing add-ons were studied to simulate weaving load on JN-01 sizing abrasion resistance tester, and abrasion resistance experiments were carried out. Based on image acquisition, yarn evenness segmentation, hairiness segmentation and main abrasion zone positioning, the hairiness index was extracted. The logarithmic function model of sizing hairiness and weaving load was established, and the comprehensive yarn pilling index is constructed based on this model. The results show that the proposed image method can accurately identify the yarn hairiness, and the yarn pilling index can effectively characterize the anti-pilling performance of the sized yarns under various weaving loads.

Key words: sized yarn, weaving load, regenerated hairiness, image detection, hairiness detection, pilling index, anti-pilling performance

CLC Number: 

  • TS103.12

Tab.1

Sizing process parameters of samples"

试样编号 浆液质量分数/% 压浆力/kN 上浆率/%
1# — — 0.0
2# 9 12 5.8
3# 10 11 8.7
4# 11 10 10.5

Fig.1

Schematic diagram of yarn image acquisition device"

Fig.2

Segmentation of yarn evenness and yarn hairiness. (a) Original image of yarn; (b) Segmentation image of yarn evenness; (c) Enhancement image of yarn; (d) Segmentation image of yarn hairiness"

Fig.3

Test results of loading times-hairiness"

Tab.2

Model fitting results of four yarn samples"

试样编号 a b R2 c
1# 5.480 7.565 0.975 41.456
2# 2.013 3.175 0.949 6.392
3# 1.402 2.905 0.920 4.072
4# 0.765 1.868 0.902 1.429
[1] DJORDJEVIC S, KOVACEVIC S, DJORDJEVIC D, et al. Sizing process of cotton yarn by size from a copolymer of methacrylic acid and hydrolyzed potato starch[J]. Textile Research Journal, 2019,89(17):3457-3465.
doi: 10.1177/0040517518813628
[2] GUDLIN S I, KOVACEVIC S, DIMITROVSKI K. Analysis of changes in mechanical and deformation properties of yarn by sizing[J]. Textile Research Journal, 2011,81(5):545-555.
doi: 10.1177/0040517510383612
[3] ZHU B, LI Z J, CAO X W, et al. Dynamic measurement of foam-sized yarn properties from yarn sequence images[J]. Autex Research Journal, 2018,18(3):314-321.
doi: 10.1515/aut-2017-0030
[4] RAFIKOV A S, KHAKIMOVA M S, FAYZULLAYEVA D A, et al. Microstructure, morphology and strength of cotton yarns sized by collagen solution[J]. Cellulose, 2020,27(17):10369-10384.
doi: 10.1007/s10570-020-03450-w
[5] JIANG Z, YU C W, YANG J P, et al. Estimation of yarn strength based on critical slipping length and fiber length distribution[J]. Textile Research Journal, 2019,89(2):182-194.
doi: 10.1177/0040517517741160
[6] CEVEN E K. Evaluation of chenille yarn abrasion behavior with abrasion tests and image analysis[J]. Textile Research Journal, 2006,76(4):315-321.
doi: 10.1177/0040517506061961
[7] SUN Y Y, LI Z J, PAN R R, et al. Measurement of long yarn hair based on hairiness segmentation and hairiness tracking[J]. Journal of The Textile Institute, 2017,108(7):1271-1279.
[8] GUO M, LIU J L, ZHU B, et al. Evaluation of physical and mechanical properties of cotton warps under a cyclic load of stretch-abrasion[J]. Autex Research Journal, 2020.DOI: 10.2478/aut-2020-0051.
doi: 10.2478/aut-2020-0051
[9] 郭敏, 高卫东, 朱博, 等. 模拟织造状态下的浆纱耐磨测试方法[J]. 纺织学报, 2021,42(11):46-50.
GUO Min, GAO Weidong, ZHU Bo, et al. Test method for abrasion resistance of sizing yarn in the simulated weaving condition[J]. Journal of Textile Research, 2021,42(11):46-50.
[10] 孙银银, 潘如如, 高卫东. 基于数字图像处理的纱线毛羽检测[J]. 纺织学报, 2013,34(6):102-106.
SUN Yinyin, PAN Ruru, GAO Weidong. Detection of yarn hairiness based on digital image processing[J]. Journal of Textile Research, 2013,34(6):102-106.
[11] WANG L, XU B G, GAO W D. Multi-perspective measurement of yarn hairiness using mirrored images[J]. Textile Research Journal, 2016,88(6):621-629.
doi: 10.1177/0040517516685281
[12] 陆奕辰, 王蕾, 唐千惠, 等. 应用图像处理的纱线黑板毛羽量检测与评价[J]. 纺织学报, 2018,39(8):144-149.
LU Yichen, WANG Lei, TANG Qianhui, et al. Detection and evaluation on yarn hairiness of blackboard with image processing[J]. Journal of Textile Research, 2018,39(8):144-149.
[13] 李忠健, 董龙, 倪海云, 等. 基于散焦信息的纱线毛羽三维测量与验证[J]. 丝绸, 2021,58(6):41-47.
LI Zhongjian, DONG Long, NI Haiyun, et al. Three dimensional measurement and verification of yarn hairiness based on defocus information[J]. Journal of Silk, 2021,58(6):41-47.
[1] GUO Min, GAO Weidong, ZHU Bo, LIU Jianli, GUO Mingrui. Test method for abrasion resistance of sized yarn under simulated weaving conditions [J]. Journal of Textile Research, 2021, 42(11): 46-50.
[2] LI Pengfei, YAN Kai, ZHANG Huanhuan, JING Junfeng. Hairiness detection based on maximum entropy and density clustering [J]. Journal of Textile Research, 2019, 40(07): 158-162.
[3] JING Junfeng, ZHANG Xingxing. Fiber glass bobbin yarn hairiness detection based on machine vision [J]. Journal of Textile Research, 2019, 40(05): 157-162.
[4] . Influence of humidity and temperature on properties of sized polyester/cotton 65/35 yarn [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 70-74.
[5] ZHAO Qiming;LU Huacheng. Size pick-up prediction for the pre-wet sizing process [J]. JOURNAL OF TEXTILE RESEARCH, 2008, 29(10): 39-42.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd