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

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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
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