Download The Surface Roughness Measurement for Textiles Fabrics by

The Surface Roughness Measurement for Textiles Fabrics
by a Non-Contact Method for Tactile Perception
Kyung Hee PARK*, Young Ha KWON*, Kyung Wha OH**
College of Advanced Technology, Kyung Hee University, Yongin city,
Gyeonggi-do, 449-701, Korea, [email protected]
** Department of Home Economics Education, Chung Ang University,
Seoul, 156-756, Korea, [email protected]
Abstract: The research for objectifying the handle of textile fabrics is a very important factor in the textile and garment
manufacturing and retailing industries.
Handle is influenced by mechanical properties of textile fabrics and the surface
roughness. The KES-F system is standard objectified method among different measurement methods.
But the measurement
of surface roughness applied the KES-F which is contact method disturbs an accurate measurement for analysis of tactile
perception. Hence we measure the surface roughness of textile fabrics without deformation by a non-contact method applied
the laser displacement sensor with a resolution of 1 µm and a accuracy of 0.01% and high precisely controlled linear motor for
translated at constant speed.
Since the textile fabrics are woven and knitted with various colored yarn and its surface treated
with chemical treatment to make luster, we analyze the effect of color and luster in order to reduce the optical errors.
And the
results of surface roughness of various textile fabrics compared with the surface characteristic values (MIU, MMD) measured by
KES-F system. The data is controlled on computer and executed FFT by using MATLABTM for obtain roughness, the mean
values and mean deviation from thirty plain and twill woven fabrics for men’s suit in winter.
We will present the comparison
between the surface roughnesses of textile fabrics measured by a non-contact method with the subjective adjective tactile
expression to understand the tactile perception.
1. Introduction
The KES-F System supplies data for objective measurement to analyze the handle of textile fabrics [1].
This method determined subjective expression by measuring physical and mechanical properties of textile fabrics
and expressed a handle of objectified textile fabrics by analyzing a mutual correlation.
The KES-F system is the
method to measure surface roughness that moves woven fabrics when the fabric surface by a fixed pressure using
a piano wire. And then the data that measured this method as hairiness was pressed down presented density and
thickness of used yarn and formation of textile fabrics. Recently, Ramgulam et al design a non-contact method
applied laser displacement sensor.
But it presents comparison with KES-F system and example of an anisotropy
for fabrics [2].
Human's touch is feeling by a tiny stimulus such as vibration, pressure and frictional. Hairiness of surface of
textile fabrics has a fixed height and density and the thread under hairiness.
That is, human touch feeling is
diverse according to mechanical properties of hairiness. And consequently a non-contact method by using laser
displacement sensor that can measure the surface roughness without pressing down hairiness is used for a more
objective expression of the handle of textiles.
2. Experiment
Fig.1 shows a non-contact roughness measuring system that is composed of a laser displacement sensor with
high precision and linear motor controlled by magnetic force.
Laser displacement sensor measures the distance
between textile fabrics and itself by using laser triangulation techniques.
is projected from a laser diode to the object.
The sensor used for the present work is capable of measuring the
distance (and hence the height of the object) with a resolution of 1 µm.
a stage that is mounted on a linear motor.
interfaced with a microprocessor.
A beam of light with 0.3 mm diameter
The fabric sample is placed and fixed on
The stage is equipped with a linear motor and the whole system is
In order
to compensate for any undulation of the
fabric, tensions are given on both ends of
textile fabrics as 20 gf/cm.
The
sensor
measures
the
surface
roughness over a 2 cm length along the warp
and weft directions because the textile
fabrics have the anisotropy.
The sample
moves with 1 mm per a second, and then
stores 200 data per a second with a
microprocessor.
Fig.1 Schematic Diagram of a Non-Contact Roughness Measuring System
2.1 Calibration
Laser displacement sensor is applied to measure surface roughness of fabrics by a non-contact technique.
It
supports enough precision to measure surface state as surface roughness, surface hairiness and irregularity of
yarns.
But the non-contact roughness measuring system is united with measuring instruments therefore it is
positively necessary to calibrate the laser displacement sensor with whole system and analyze errors.
Measuring
system is calibrated as the laser sensor that is attached on mechanical micrometer moves up and down 100 µm
each.
Errors are calculated from measured voltage and full-scale output range (FSO).
Linearity error is 2.26%
(FSO), zero drift error is 2.42% (FSO) and hysteresis error is 1.61% (FSO) as static calibration results of
measuring system.
Total complex error of measuring system is calculated as equation(1) from results that are
obtained through static calibration [3].
(1)
That is, σ is total complex error, σ1 is linearity error, σ2 is zero drift error and σ3 is hysteretic error.
It
shows total complex error is within 3.7% in case of white.
2.2. Effects of Surface Color
Measurement by using laser displacement sensor measures object by reflects of lights and triangular
technique. Hence it could be occurred error according to condition of surface. Especially, fabrics are woven
and knitted various colored yarns and dyed various colors.
Therefore, it is necessary to analyze effects of colors
when surface roughness is measured by using laser displacement sensor.
To analyze effects of colors, white, cyan, magenta, yellow and black papers and cotton are calibrated in the
same manner that is static calibration as standard samples.
In case of papers, black is lowest total complex error,
1.66 % (FSO), and total complex error of magenta is comparatively high, 4.99% (FSO).
complex error of white is 3.7% (FSO) and rubine red is 1.56% (FSO).
In case of cotton, total
It shows that laser displacement sensor is
capable of measuring surface roughness within 5 % errors without concerning colors.
3. Surface Roughness Measurement and Analysis
Man’s suit fabrics are used to measure hairiness and surface roughness.
photographs of fabrics with hairiness.
Fig. 2 shows optical microscopic
It is
possible to observe the surface qualitative.
Fig.2 (a) is comparatively flat and has less
hairiness than Fig.2 (b).
Fig.2 (b) is rough and
has more hairiness than Fig.2 (a).
The former is
1 mm
soft and smooth and the latter stiff and coarse.
The mean value of hairiness of the first is 8.72
and the second is 16.11.
It figures the former
X 200
X 200
1 mm
(a)
Fig. 2 Optical Microscopic Photographs of Fabrics
(b)
fabric has 2 times hairiness.
3.1. Surface roughness
The non-contact roughness measuring system measures surface roughness of 20 fabrics for man’s winter
suits.
Standard mean deviation (SMD) value is calculated to figure quantitative surface roughness from
measured data.
The measurement by using a non-contact method can occur errors.
so mean value can be changed by a tiny wrinkle.
Because fabrics are flexible,
Therefore, Surface roughness spectrum for calculating mean
value is divided 4 sections and then values are calculated from the mean of each SMD and mean value.
It
includes not only the value of surface roughness but also the effect of hairiness. Standard mean deviation as the
surface roughness, SMD, can be calculated with heights, Hi, and measured with respect to any datum at N different
points as equation(2).
H represents the mean value of heights.
Fig.3 shows warp direction profile of Fig.2 Each value of SMD is 0.07 mm and each value of height; a
difference of high and low from measured data is 0.9 mm and 2.88 mm. The latter has a larger value than
the former. It means the latter is rougher than the other.
(a)
Fig.3 Surface Roughness Profiles in warp direction of Fig.2-(a) and (b) respectively
(b)
3.2. Effects of Hairiness
The non-contact method by using laser displacement sensor that can measure the surface roughness without
pressing down hairiness and gliding is different from KES-F system.
But a beam of light with 0.3 mm diameter
is projected from a laser diode to the object and mean of roughness is recorded in data. So, effects of hairiness are
analyzed. The number of hairiness is measured image processor and counted lattice per length of unit [4]. In the
relation between hairiness and SMD and heights, Fig.4 shows the plot of the value that has a linear correlation
with hairiness and the correlation coefficient comparatively has a high value.
4
0.3
R = 0.72
Heights(mm)
SMD(mm)
R = 0.7
0.2
0.1
3
2
1
0
0.0
6
9
12
15
18
6
Hairiness(units/cm)
(a)
Fig.4 Relation between Hairiness and (a) SMD and (b) Heights
9
12
15
18
Hairiness(units/cm)
(b)
3.3. Analysis of Fabric Structure
A spectrum that is measured by a non-contact roughness measuring system involves information of hairiness,
density of warp and weft and pattern.
Therefore, measured spectrums and structure of fabrics are compared each
other to find out reliability of a non-contact roughness measuring system.
2.43 Hz
2.82 Hz
(a)
(b)
Fig.5 FFT spectrograms of Fig.3-(a) and (b)
Fast Fourier Transform analyzes surface roughness spectrums that are shown in Fig. 3 and the results are
showed [5]. We analyze the FFT spectrogram in MATLABTM and determine the peak points near the warp and
weft densities.
FFT can confirm the period of height. Fig.5-(a) has surface roughness as 2.82 Hz in fabric with
1 mm and Fig.5-(b) have surface roughness as 2.43 Hz in fabric with 1 mm.
The clear peak is analyzed that it is
related with twisted yarn, hairiness and density of principle directions. In order to verify the FFT results, the
frequency of the surface roughness were measured by the magnifying lens and human eye.
The surface
frequencies are compared with warp and weft density (No. of yarn/mm) that is shown in table 1.
The
errors between measured results and densities are less than in any directions.
fabric surface is mixed with bright colors, error is between 10% and 15%.
Especially, in the case of
On the contrary, in the case of
fabric surface is mixed with dark colors, error is less than 10%.
Table1. Errors of Relation between FFT Peak Value and Density
Sample
number
FFT peak
(Hz)
Warp direction
Weft density
(No. of yarn/㎜)
Errors
(%)
FFT peak
(Hz)
Weft direction
Warp density
(No. of yarn/㎜)
Errors
(%)
1
2.42
2.4
0.83
2.51
2.4
4.58
2
2.83
3
5.67
2.73
3
9
3
2.75
2.8
1.79
2.52
2.6
3.08
4
3.01
3
0.88
3.12
3.2
2.5
5
3.03
3
1
2.25
2.2
2.27
6
2.45
2.8
12.5
3.15
3
5
7
1.83
2
8.5
1.03
1
3
8
0.92
1
8
1.31
1.2
9.17
9
2.82
3
6
2.32
2.4
3.33
10
1.56
1.9
17.89
0.91
0.8
13.75
11
2.45
2.2
11.36
1.34
1.4
4.29
12
0.94
1
6
0.82
0.8
2.5
13
1.28
1.2
6.67
0.86
0.8
7.5
14
1.25
1.2
4.17
0.81
0.8
1.25
15
1.08
1.1
1.82
0.93
0.9
3.33
16
2.43
2.6
6.54
2.43
2.6
6.54
17
1.01
1
1
1.02
0.9
13.33
18
1.13
1.2
5.83
0.78
0.8
8
19
0.73
0.8
8.75
0.94
1
6
20
1.49
1.4
6.43
2.41
2.6
7.81
4. Conclusion
The experiments show the surface roughness measuring system applied laser displacement sensor by a noncontact method can be successfully used for the measurement of geometrical surface roughness. Then, we confirm
that a range of errors to be occurred by effect of colors is within 5%. In case of dark colors, particularly, error is
reduced and on the contrary bright colors have possibility to be increased errors. The possibility of objective
evaluation for fabrics is presented by qualitative analysis of surface roughness spectrum.
Hairiness is a factor to
increase SMD of surface roughness and also it is confirmed as the factor for the objective expression of the handle.
Moreover, the period of surface roughness is analyzed through FFT results. The difference that compares
frequencies analyzed the surface roughness spectrum by FFT with densities of fabric from warp and weft
directions is within 15%. Finally, laser displacement sensor is useful to measure a surface roughness by a noncontact method in reliance generally.
References
1. Sueo Kawabata, "The Standardization and Analysis of Hand Evaluation", The Textile Machinery Society of
Japan, 2nd Ed., 31-34(1997).
2. R.B. Ramgulam, J. Amirbayat, and I. Porat, J. Text Inst., 84, 99-106(1993).
3. Richard S. Fidliola and Donald E. Beasley, “Theory and Design for Mechanical Measurement", John Wiley &
Sons, Inc., 2nd Ed., 13-22(1995).
4. Y.H. Kwon, Korean Journal of the Science of Emotion & Sensibility, 12, 9-14(2000).
5. Delores M. Etter, "Engineering Problem Solving with MATLAB", 2nd Ed., Prentice Hall, Inc., 241-248, (1997).