Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (04): 153-159.doi: 10.13475/j.fzxb.20210305407

• Machinery & Accessories • Previous Articles     Next Articles

Research on on-line detection system of broken filaments in industrial polyester filament

ZHANG Ronggen1, FENG Pei1,2(), LIU Dashuang1, ZHANG Junping1, YANG Chongchang1,2   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Advanced Textile Machinery, Donghua University, Shanghai 201620, China
  • Received:2021-03-15 Revised:2021-10-25 Online:2022-04-15 Published:2022-04-20
  • Contact: FENG Pei E-mail:pfeng@dhu.edu.cn

RichHTML

14

PDF (PC)

97

Abstract:

The on-line detection process of polyester filaments hinders intelligent manufacturing in the whole production process. In order to solve the problem in inspecting polyester broken filament yarns and to improve the on-line quality detection system, a method of identifying broken filament yarns and counting the number by means of machine vision intelligent detection technology is proposed. Based on LabVIEW image processing technology,the yarn length is taken as the judgment basis. Image enhancement,binarization,digital morphology and other methods are used to obtain the filament image and to extract the length information. Through experiments, the detection threshold of filament length is obtained. When the filament length in the image exceeds the detection threshold,the existence of the filament will be recognized,and the number of such filaments can be accumulated. The experimental results show that the accuracy of the detection scheme is over 90%,the design is reasonable,the cost is low,and it has great practical value for improving polyester filament quality and reducing enterprise cost.

Key words: industrial polyester filament, filament detection, image processing, LabVIEW image processing technology, on-line quality detection

CLC Number: 

  • TP391.41

Fig.1

Filament photographs taken at different light intensities (a) and speeds (b)"

Fig.2

Filament image processed by different gray scale transformation functions. (a)Original image;(b)Logarithmic transformation;(c)Gamma correction;(d)Square root transformation;(e)Power 1/x transformation;(f)Power transformation"

Fig.3

Filament image processed by histogram processing function. (a)Original image;(b)Histogram function processing effect"

Fig.4

Filament image processed by spatial filter function. (a)Original image;(b)Spatial filtering 1;(c)Spatial filtering 2; (d)Spatial filtering 3;(e)Spatial filtering 4;(f)Spatial filtering 5;(g)Spatial filtering 6;(h)Spatial filtering 7; (i)Spatial filtering 8;(j)Spatial filtering 9;(k)Spatial filtering 10"

Fig.5

Filament image processed by frequency domain enhancement function. (a)Original image;(b)Low pass cutoff;(c)Low pass attenuation"

Fig.6

Filament image processed by binary function of different threshold. (a)Original image;(b)Image enhancement processing; (c)Binarization 150;(d)Binarization 175;(e)Binarization 200;(f)Binarization 225;(g)Binarization 250"

Fig.7

Filament image processed by mathematical morphology function. (a)Original image;(b)Image enhancement processing;(c)Binarization;(d)Dilate erode;(e)Corrode;(f)Open;(g)Close;(h)Proper open; (i)Proper Close;(j)Gradient in;(k)Gradient out;(l)Auto median"

Fig.8

Comparison chart of processing filament images with different templates of open(a) and proper open(b) functions"

Fig.9

Process of extracting hair defects. (a)Original image;(b)Image enhancement processing;(c)Binarization;(d)Morphological processing;(e)Subtraction operation;(f)Filter processing"

Tab.1

1 mm filament width pixels"

长丝样本编号 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
像素点数 27 28 35 30 37 24 29 28 29 34

Tab.2

Experimental verification data"

长丝毛丝样
本编号		 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10
毛丝像素点数 195 189 211 128 213 191 199 184 193 210
[1] 李晓东. 浅析涤纶工业长丝生产过程中毛丝形成的原因及解决措施[J]. 黑龙江纺织, 2007(2):12-15.
LI Xiaodong. Reasons for fuzz formation during superfine PET industry yarn production and the solution mea-sures[J]. Heilongjiang Textile, 2007(2): 12-15.
[2] 史新涛. 应用于PA66中细旦丝生产的Fraytec Fv毛丝检测系统[J]. 河南科技, 2013(8):43-68.
SHI Xintao. Fraytec Fv lousiness inspection system used in the production of PA66 medium and fine denier[J]. Henan Science & Technology, 2013(8): 43-68.
[3] 穆征, 张冶. CCD摄像传感器在纺织上的应用[J]. 上海纺织科技, 2005, 33(8):8-11.
MU Zheng, ZHANG Zhi. The application of CCD photography sensor in textile industry[J]. Shanghai Textile Science & Technology, 2005, 33(8): 8-11.
[4] 张凤生, 刘冲. 高精度激光衍射测量系统[J]. 仪器仪表学报, 2001, 22(3): 149-151.
ZHANG Fengsheng, LIU Chong. Super precision laser diffraction measuring system for fine diameter[J]. Chinese Journal of Scientific Instrument, 2001, 22(3): 149-151.
[5] CHEN Z K, WITTER G. Electrical contacts for automotive applications: a review[J]. IEICE Transactions on Electronics, 2004, 87 (8):1248-1254.
[6] 李若仲, 齐跃虎, 李兆展. 光电检测系统中弱信号的检测[J]. 空军工程大学学报(自然科学版), 2002(4): 33-35.
LI Ruozhong, QI Yuehu, LI Zhaozhan. Measure and test of weak signal in the photoelectric detecting system[J]. Journal of Air Force Engineering University(Natural Science Edition), 2002(4): 33-35.
[7] LIN Jengjong, LIN Chunghua, TSAI I Shou. Applying expert system and fuzzy logic to an intellect diagnosis system for fabric inspection[J]. Textile Research Journal, 1995(12): 697-709.
[8] TSAI I S. Automatic inspection of fabric defects using and artificial neural network technique[J]. Textile Research Journal, 1996(7): 474-482.
[9] TSAI I S. Applying an artifical neural network to pattern recognition in fabric defects[J]. Textile Research Journal, 1995(3): 123-130.
[10] 余恒炜, 谭雅岚, 侯群, 等. 图像处理技术在粘胶长丝毛丝自动检测系统中的应用[J]. 自动化技术与应用, 2017, 36(9): 90-92.
YU Hengwei, TAN Yalan, HOU Qun, et al. Application of image processing technology in automatic detection of broken filament of viscose filament[J]. Techniques of Automation & Applications, 2017, 36(9): 90-92.
[11] HUANG Shihchia, CHENG Fanchieh, CHIU Yisheng. Efficient contrast enhancement using adaptive gamma correction with weighting distribution[J]. IEEE Transactions on Image Processing, 2013, 22(3): 1032-1041.
doi: 10.1109/TIP.2012.2226047 pmid: 23144035
[12] 毛磊, 连文浩, 范振钦, 等. 数字图像处理算法概述[J]. 科技与创新, 2020(19): 29-30.
MAO Lei, LIAN Wenhao, FAN Zhenqin, et al. Overview of digital image processing[J]. Algorithms Science and Technology & Innovation, 2020(19): 29-30.
[13] GE Yunwang. Summarization of intelligentized and networked induction hardening control system[J]. Heat Treatment of Metals, 2007, 32(9): 96-100.
[14] HU Guanghua, WANG Qinghui. Defect detection via un-decimated wavelet decomposition and gumbel distribution model[J]. Journal of Engineered Fibers and Fabrics, 2018, 13(1): 15-32.
[15] STERNBERG S R. Grayscale morphology[J]. Computer Vision,Graphics,and Image Processing, 1986, 35(1):333-335.
doi: 10.1016/0734-189X(86)90004-6
[1] XIONG Jingjing, YANG Xue, SU Jing, WANG Hongbo. Testing method for fabric moisture conductivity based on image technology [J]. Journal of Textile Research, 2021, 42(12): 70-75.
[2] LÜ Wentao, LIN Qiqi, ZHONG Jiaying, WANG Chengqun, XU Weiqiang. Research progress of image processing technology for fabric defect detection [J]. Journal of Textile Research, 2021, 42(11): 197-206.
[3] XIA Xuwen, MENG Shuo, PAN Ruru, GAO Weidong. On-line detection of warp collision and reed embedding based on improved inter-frame difference method [J]. Journal of Textile Research, 2021, 42(06): 91-96.
[4] JIANG Yanting, YAN Qingshuai, XIN Binjie, GAO Cong, SHI Meiwu. Comparative study on testing methods for unidirectional water transport in fabrics [J]. Journal of Textile Research, 2021, 42(05): 51-58.
[5] LI Dongjie, GUO Shuai, YANG Liu. Yarn defect detection based on improved image threshold segmentation algorithm [J]. Journal of Textile Research, 2021, 42(03): 82-88.
[6] TANG Qianhui, WANG Lei, GAO Weidong. Detection of fabric shape retention based on image processing [J]. Journal of Textile Research, 2021, 42(03): 89-94.
[7] MENG Shuo, XIA Xuwen, PAN Ruru, ZHOU Jian, WANG Lei, GAO Weidong. Detection of fabric density uniformity based on convolutional neural network [J]. Journal of Textile Research, 2021, 42(02): 101-106.
[8] ZHANG Zhengye, XIN Binjie, DENG Na, CHEN Yang, XING Wenyu. Research and application of algorithm for measuring hemp fiber cross-sectional parameters based on boundary tracking [J]. Journal of Textile Research, 2020, 41(02): 39-43.
[9] WU Yilun, LI Zhongjian, PAN Ruru, GAO Weidong, ZHANG Ning. Weft knitted fabric appearance simulation using colored spun yarn image [J]. Journal of Textile Research, 2019, 40(06): 111-116.
[10] HUANG Jiajun, KE Wei, WANG Jing, DENG Zhongmin. Color shading detection and rating system for denim based on computer vision [J]. Journal of Textile Research, 2019, 40(05): 163-169.
[11] WANG Wendi, XIN Binjie, DENG Na, LI Jiaping, LIU Ningjuan. Identification and application of yarn hairiness using adaptive threshold method under single vision [J]. Journal of Textile Research, 2019, 40(05): 150-156.
[12] CAI Yichao, ZHOU Xiao, SONG Mingfeng, MOU Xin'gang. Defect detection of cheese yarn based on multi-scale multi-direction template convolution [J]. Journal of Textile Research, 2019, 40(04): 152-157.
[13] . Detection and evaluation on yarn hairiness of blackboard with image processing [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(08): 144-149.
[14] . Position recognition of spinning yarn breakage based on convolution neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 136-141.
[15] . Detecting method of foreign fibers in seed cotton based on deep-learning [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 131-135.
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