diameter spectrom of yarn

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J E

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C E International Journal of Electrical, Electronics ISSN No. (Online) : 2277-2626

and Computer Engineering 2(2): 72-76(2013)

Special Edition for Best Papers of Michael Faraday IET India Summit-2013, MFIIS-13

Determination of the Diameter Spectrogram and Neps for YarnParameterization using Image Processing

Subhasish Roy*, Anindita Sengupta* and Surajit Sengupta***Department of Electrical Engineering Bengal Engineering and Science University, Shibpur, Howrah, (WB)

**Mechanical Processing Division, National Institute of Research on Jute and Allied Fiber Technology, Kolkata, (WB)

(Received 15 October, 2013 Accepted 01 December, 2013)

ABSTRACT: This paper illustrates the development of a system to measure the variation in yarn parameters using image processing

with the help of a low cost USB web camera along with a yarn moving arrangement. The complete system comprises of yarn guides and

tension control device, motor with gear box, monitor for displaying yarn parameters etc. The system enables to develop yarn diameter

spectrogram and neps count present in the yarn in a single unit. The performance of the developed system is free from any fluctuation

of ambient temperature or humidity. The available results are verified with that available from a powerful microscope. Results are

available on the monitor showing diameter spectrogram calculated for a specific length of the yarn, counters displaying the number of

neps available in that yarn for that specific length.

The future goal of this work is to acquire the image of the yarn from a spinning frame with the help of a high resolution web camera

and to process the same with advanced software to characterize the yarn which in turn dictates the fabric appearance.

Index Terms: Yarn parameterization; Image Processing; Diameter Spectrogram; Neps; Machine Vision.

I. INTRODUCTION

Yarn regularity is an important feature to assess its quality. A

higher quality requires an arrangement of fibres in which the

number of fibres in each cross section (longitudinal variation)

is close to constant.

Irregularity is used to evaluate variation in several

characteristics along a strand (yarn, roving, and sliver) and

unevenness measures the mean variation in linear density of a

strand or a part of it.

Thus to judge efficiency of yarn processing that influences

final fabric appearances, there are level of unevenness beyond

which the yarn is unacceptable [1].

For detection of such irregularities electronic capacitance

tester and optical tester are still applied as most convenient,

reliable and rugged method of testing but they have several

limitations such as humidity and temperature dependency of

capacitive sensor, reduction of efficiency with ageing for

optical sensor etc. Moreover, these systems performs the

measurement with 8 mm resolution but yarn irregularities

evaluated in 1mm range is of most importance for correctdetection of defects and imperfection as most of the

irregularities occur at a very short length between 1 to 4 mm.

To develop a system which can check irregularities of the yarn

in 1mm range, J. G. Pinto et al first proposed the online

measurement of the yarn in a spinning frame with real time

control [2].

Carvalho et al [3] improvised the existing methods with the

incorporation of other important yarn characteristics like

Integral deviation rate (IDR), Deviation rate (DR), Periodic

false, Fibre wavelength and Production fault etc.

In 2006, along with the measurement of evenness of the

yarn using capacitive sensor, hairiness analysis using optical

sensor are also adopted by Carvalho and his research group [4-

7].

Later in 2008 and 2009, a complete system was developed

which included modular format for integrating simultaneously

yarn hairiness, mass regularity and diameter measurement butonly in the laboratory scale [8-10].

A comparative study was carried out by the researchers to

conclude about the performance of two sensors such as

capacitive and optical [11, 12].

After all this developments in the sensor design and with

the successful application of image processing and artificial

intelligence in several branches of engineering and technology,

in 2009, Carvalho et al attempted to apply image processing to

find out accurate results of above yarn parameters and some

added features such as snarl length, number of cables, fibre

orientation etc [13-14].

In this work, effort has been taken to apply digital imageprocessing for finding diameter spectrogram and occurrence of

neps in a single system.

The developed system can simultaneously eradicate the

spurious effects of environmental changes as well as resolution

problem of the existing industrial system. The proposed

method seems to be cost effective as a low cost USB web

camera has been used for capturing the image.



Roy, Sengupta and Sengupta 73

II. THEORATICAL CONSIDERATION OF TEXTILE

PARAMETERS

There is a direct relationship between the variation of yarn

mass and the yarn diameter shown in Fig.1. This fact allowsthe possibility for determining yarn irregularity based on yarn

diameter measurement. The yarn linear mass is generally

expressed in tex which represent the mass of yarn(g) over a

kilometer (km). Thus the calculation of diameter can be

obtained by d(mm) = 0.037 sqrt (tex) [tex is the yarn linear

mass]. This relation also allows the possibility of determining

yarn irregularities based on yarn diameter also. Type of yarn

fault (NEPS) has been classified in Fig. 2.

Sensitivity with respect to diameter measurement is defined as

the yarn diameter value used to detect a particular fault

referenced to yarn diameter average. Neps are treated as those

places where there is 200% percentage increase in the average

diameter and that too exists for 4 millimeters in length.

III. HARDWARE FOR IMAGE PROCESSING

In order to keep the total cost of the system at an acceptable

level, a low cost USB web CMOS camera is used. The

flowchart for the image processing hardware is shown in the

Fig. 3. The camera has the high quality CMOS sensor with the

resolution of 12 megapixels and has 5G wide-angle lens. It

also incorporates 6 LEDs with brightness controller.

In order to obtain higher contrasts for the yarn geometry relief,

the illuminated yarn surface must be as close as possible to the

light source.

III. STEPS ASSOCIATED WITH IMAGE PROCESSING

SOFTWARE

This section demonstrates the way to process the captured

image of different section of the yarn for characterization. Fig.

4 shows the system as a whole (without monitor).The section

which is dedicated for image acquisition is shown in Fig. 5.

The camera is taking the snap after each 5 seconds and the

captured image is interfaced with the PC for further processing

via USB connector. As the motor is made on, the yarn starts

passing in front of the camera and the captured images are fed

to the image processing software to extract various features.

The speed of the motor can be varied from 1rpm to 75 rpm in

the present set up.

Fig. 1. Yarn Configuration Example.

Fig. 2. Type of yarn Faults (Neps).

Fig. 3. Flowchart of Image Processing Hardware.

Fig. 4. System as a whole.

Fig. 5. Image Acquisition.




A. Image analysis

Images are captured with the light transmitted by the LEDs

mounted on the camera (6 LEDs are there on the both sides of the aperture). The environment where the images are captured

should be free from the effect of change in the intensity of the

light.

For extracting better information from an image of a moving

yarn, high resolution with correct magnification is required.

The camera which is used here has the high quality CMOS

sensor with 12mega pixels still image resolution and a 5G

wide lens for sharp and clear pictures. The arrangement of the

light along with the background has been so chosen that the

yarn can be subjected to sufficient lights. An original captured

image of cotton is shown in the Fig. 6.

IV. DETERMINATION OF DIAMETER

SPECTROGRAM

The captured image is first made horizontal by rotating it at

900.The image is then calibrated in millimeter and transformed

into gray scale by extracting the luminance plane and further

thresholding it to get the binarised image. The binary image is

then filtered to achieve the core of the yarn. The image is then

clamped at 1mm gap to extract the diameter of the yarn core.

Clamp function basically calculates the difference between the

upper and the lower edges of a specific clamp. The process has

been described below in the each sub section and the flowchart

is shown in the Figure 7.

A. Converting the measurement values into the real world

units

The distances between clamps are available in terms of pixels.For converting the measurements values into real world unit a

reference image has been taken which was calibrated in the

laboratory with the help of a microscope. This calibration has

been fed into the present system and every snap of the yarn

which was already processed is calibrated by this reference

image.

B. Conversion of image from coloured to gray scale

Image acquired is converted into gray scale image by

extracting the luminance plane of the image. The coloured

image captured by the camera has the information about the

Red, Green and Blue Components. For converting the image

into the grey scale image, definition of the colour space wasused. The NTSC has defined the colour space as

Where Y denotes the luminance plane, I denote the Hue

plane, Q denotes the saturation. Within these only Y represents

the luminance plane. So conversion is done only by extracting

the luminance plane. Fig. 8.shows the original image of the

yarn and Fig. 9. is the grayscale image.

Fig. 6. Cotton.

Fig. 7. Flow chart of diameter detection

Fig. 8. Original Image.




C. Conversion from gray scale to a binary image

For extracting the region of interest from Fig. 9 the imagehas been binarised by thresholding the image. Thresholding

the image helps to extract the core of the yarn which is used

for measuring the diameter. Fig. 10 shows the thresholded

image. Here the whole background of the image has been

thresholded as this code is only concerned with the diameter of

the yarn.

The image has been auto thresholded and it was maintained

throughout the test.

D. Finding the yarn core for diameter detection

To determine the diameter of the yarn, the yarn core has to

be properly filtered from the binary image with the special

types of the filters. Here Robert filter has been used to

properly extract the yarn core from the binarized image.

Extraction of the yarn core is basically for the detection of the

edges from the binarized image. Fig. 11 shows the yarn core

separated from the whole yarn image.

E. Eliminating the particles from the extracted Yarn core

Fig.11 shows the extracted yarn core with the particles.

These particles are to be filtered out by the particle filter from

the perimeter of the yarn without disturbing the core of the

yarn. The maximum value for eliminating the particles are

taken as 45 pixels where as the minimum are kept in the 0

pixels. Fig. 12 shows the yarn core without the particles.

A. Segmentation and Detection of edges of the binary

image

For detecting the diameter of the yarn, the binarised yarn

core is being segmented into 20 slices with the help of the

clamp tool placed at specific distances. The camera is

capturing the image of 20mm yarn at a time and it is being

sliced into a 20 equal halves. Now these clamps have the upper

and the lower edges and calculating the distance between these

edges gives the diameter of that region. The same procedure is

being used for the rest of the clamps to get the respective

diameter and the average with 20 diameter values are used to

obtain an average diameter. The diameters with the variation

of number of clamps for varying resolution are also tested.

V. DETERMINATION OF THE NUMBERS OF NEPS

Neps is special types of yarn fault and important parameter for

yarn characterization. Neps are specifically calculated in those

areas which are 200% thicker than the calculated average

diameter and that too exists for 4mm region. The average

diameter is calculated by the above process which is being

multiplied by 2 to get the threshold value for calculating the

neps. The counters are then used for calculating the total

numbers of the Neps.

VI. RESULTS AND DISCUSSION

Tests are performed with the cotton and jute yarn and the

results are shown in the Table 1.The results shows that the

cotton is not uniform than that of jute. Test experiments were

performed for 78 centimeters of the yarn in both the cases. The

diameter spectrogram has been shown in the Figure

13.Diameter Spectrogram shows the variation of the diameter

along the specific length of the yarn

Table 1. Number of Neps.

Type of yarn Length of the

yarn

Neps

Cotton 78 centimetres 2

Jute 78 centimetres 1

Fig. 10. Thresholded Image.

Fig. 11. Yarn core after binarization.

Fig. 12. Filtered Image of the yarn core.Fig. 13. Diameter Spectrogram.




VII. CONCLUSION AND FUTURE WORK

In future some parameters are to be taken care of such as the

threshold value for binarisation of the image. It varies from

yarn to yarn and is responsible for the correct evaluation of theresult. Effort will be taken such that the threshold parameters

can change automatically using artificial intelligence. Efforts

will be also taken such that the latency between the captured

image and the processed image should have to be low such

that the proper application in the real time environment can be

done

ACKNOWLEDGEMENT

The authors are grateful to Department of Science and

Technology, Govt. of India (DST, GOI), Instrument

Development Program me (IDP) for financial support of the

work [vide order no.IDP/IND/2010/25 dtd.17 th Aug, 2011].

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