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Introduction
Comfort properties of the fabrics come out as the rst consideration
of the user after their color factor with the developing technology and
the consciousness of users. Production of the functional products
compatible with the movements and daily life of users has become
very important. Cotton materials are the best evaluated products in
terms of comfort. Parameters that affect the comfort characteristics
of fabrics have been studied by many researchers.1‒9 The clothing
comfort depending on these parameters is evaluated within the
clothing physiology.10‒12
The aim of the science of clothing physiology is to create rules for
clothing design and production which will not affect the comfort and
working efciency of the person negatively and even contribute to the
healing, taking into consideration the fashion and sales parameters.10
Clothing comfort is dened as the user’s feeling of being physically
and psychologically comfortable within the current working
conditions of user.10,13‒15
Different techniques such as correlation analysis, regression
analysis, factor analysis, articial neural networks, and fuzzy logic
were used to analyze the comfort of clothes.10 Correlation analysis
determines the relationship and direction between variables. In
regression analysis, the function of relationship can be determined
by considering dependent and independent variables.10,16 Factor
analysis is a technique that allows obtaining a small number of but
independent variable sets by combining moderately or highly related
variables with each other. It is thus possible to reduce many variables
to a few clusters or sizes.10 The multiple logic system that determines
the realization rates of daily living variables is expressed as fuzzy
logic. In fuzzy logic, innite number of propositions can be made.
In fuzzy logic, innite number of gray between black and white is
evaluated.10,17
It is difcult to estimate comfort with these statistical methods,
which are used due to nonlinear relations between comfort and fabric
parameters. This non-linear relationship is explained by Pontrelli.
In his study, Pontrelli has declared that comfortable feeling is due
to the invisible parameters such as environmental factors depending
on the relationship between environment and fabric characteristics,
psychological and physiological factors depending on purpose
and appearance of fabrics as well as the user’s experience and
expectations.18 In order to overcome this restriction, the relationship
between fabric structures and comfort properties is evaluated with
articial neural network. Articial neural networks are systems that
can learn using examples and determine the reaction to environmental
effects.
Articial neural networks are used in learning, associating,
classifying, generalizing, characterizing and optimizing operations
similar to those that the human brain can do.10 Detailed information
on the basic principles of articial neural networks and their work has
been given in many articles.19
The human nervous system in the working system of articial
neural networks is taken into consideration and three layers (input,
intermediate, and output) are introduced into the network. The
measured parameters form the input layer and are used to train the
system. The intermediate layer is the part of training itself and is
used to obtain output. The output layer expresses the outputs that are
reached through the input parameters considered.10,20
Articial neural networks have advantages such as short-time
application and self-training according to new situations. Articial
neural networks can be classied as forward-feed, back-feed, and
forward-feed-back articial neural networks.10,21,22 It is preferred to
use forward feed-back propagation neural networks, when the comfort
characteristics are determined.
Wong et al. reported that articial neural networks, which can
be used in evaluating the comfort of clothes, are fast, exible and
predictive techniques in terms of self-training ability and also
estimation of sensory comfort.10,23
In his study, Tokarska used articial neural networks to evaluate
permeability of fabrics depending on some constructional parameters
such as fabric density and twisting parameters of warp and weft
yarns. As a result, he stated that the input parameters affected air
permeability.24 In a study, thermal conductivity and thermal absorbency
J Textile Eng Fashion Technol. 2018;4(1):1‒8. 1
© 2018 Turker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and build upon your work non-commercially.
A research on the investigation of physical
properties of polyester/cotton fabrics
Volume 4 Issue 1 - 2018
Erkan Turker, Necla Yaman Turan
Department of Textile Engineering, Usak University, Turkey
Correspondence: Prof. Dr. Necla Yaman Turan, Usak
University, Textile Engineering Department, Usak, Turkey, Tel
902762212121/2735, Email yaman.necla@gmail.com
Received: April 18, 2017 | Published: January 08, 2018
Abstract
The aim of this study is to reach closer results to the actual measurements by using
artificial neural networks in evaluating the properties of single and double layer woven
fabrics that affect the end user. The variation of the most important parameters such
as breaking strength, weight, thickness, tearing strength properties to meet the end
user’s needs depending on the yarn count and the weaving type was investigated. In
the fabrics produced in this study, cotton yarn was used as weft yarn in three different
numbers and 90 denier polyester yarns were used as warp yarn. Plain, twill, panama,
warp rips and satin were used as weaving type. Fabrics were weaved in three different
weft frequencies. Evaluations using artificial neural networks were found to give
closer results to the actual measurements.
Keywords: cotton, comfort, breaking strength, tear strength, thickness, weight
Journal of Textile Engineering & Fashion Technology
Research Article Open Access
A research on the investigation of physical properties of polyester/cotton fabrics 2
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8.
DOI: 10.15406/jteft.2018.04.00115
parameters were estimated to determine the effect of raw material and
knitting construction on comfort properties by using articial neural
networks.25 In another study, weaving construction type, densities
and counts of warp and weft yarns, weight and thickness data were
used as input material and two articial neural networks were used
in estimating the thermal resistances of the fabric.22 Majumdar used
articial neural network to estimate of thermal conductivity of bamboo
and cotton fabrics. Fabric knitting structure, yarn count, ratio of raw
material, fabric thickness and weight were used as input parameters
in the articial neural networks.10,26 Park et al. used seven mechanical
parameters measured with KES-F system as input parameters,
and articial neural network was used to estimate tactile sensation
of users.10,27 Yaman et al. used articial neural network in the hand
evaluation of the bulky materials. Friction, compression, thermal
properties both wet and dry forms and also wettability properties of
disposable diapers were used as input parameters in their study.19 The
effect of the plasma treatment on the comfort characteristics of the
fabric has also been examined and it has been shown that the surface
properties of the fabric determine the comfort parameters.28
In this study, articial neural networks were used to estimate the
breaking and tearing behaviors of fabrics by using the yarn number,
frequency, weight and thickness parameters of single and double
layered fabrics with different constructions woven from polyester/
cotton yarns by considering the cost factor and the speed required by
the age.
Materials and methods
Ne 16/1, 20/1 and 30/1 100% cotton yarns were used as weft yarns
while 90 denier 100% polyester yarns were used as warp yarns in the
fabrics produced in this study. All fabrics were weaved in 160cm reed
width and with 160/2 reed number. The fabrics were weaved as single
and double layer fabrics by using plain, twill, rips, panama and satin
weavings. Fabrics were produced at weft frequencies of 20-60 wire/
cm depending on the type of weaving and yarn counts. The fabric
widths were measured after 24hours from the end of the weaving
process. The technical characteristics of the fabrics produced in this
study are given in Tables 1-5.
Table 1 Properties of the plain fabrics
Single-layered (weaving factor:0.5) Double-layered (weaving Factor: )
Ne 16
Code P101 P102 P103 P201 P202 P203
Width 158 159 158.8 153 155 156.5
Warp Fre 32.4 32.2 32.9 33.5 33 32.7
Weft Fre 25 22 19 39.4 34.9 30.5
Ne 20
Code P104 P105 P106 P204 P205 P206
Width 157.5 157 158 152 153 156
Warp Fre 32.5 32.6 32.4 33.7 33.5 32.8
Weft Fre 26 28 23 44.1 34.9 23.2
Ne 30
Code P107 P108 P109 P207 P208 P209
Width 156.5 157.5 157.5 150 152 154
Warp Fre 32.7 32.5 32.5 34.1 33.7 33.3
Weft Fre 32 29 26 52 41.9 32.7
Table 2 Properties of twill fabrics
Single-layered (Weaving factor:0.5) Double-layered (Weaving factor: )
Ne 16
Code T101 T102 T103 T201 T202 T203
Width 155.5 155 156 155 155.2 156
Warp Fre 32.9 33 32.8 33 33 32.8
Weft Fre 30 27.5 24.2 50.5 40.4 30.5
Ne 20
Code T104 T105 T106 T204 T205 T206
Width 153 156 156,5 154 154.5 155
Warp Fre 33.5 32.8 32.7 33.3 33.1 33
Weft Fre 36 33 30 56 45.9 36.3
Ne 30
Code T107 T108 T109 T207 T208 T209
Width 153 153 153.5 154 154.3 155
Warp Fre 33.5 33.5 33.4 33.3 33.2 33
Weft Fre 44 40.8 37.1 68.7 55 41.5
A research on the investigation of physical properties of polyester/cotton fabrics 3
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8.
DOI: 10.15406/jteft.2018.04.00115
Table 3 Properties of panama fabrics
Single-layered (Weaving factor:0.5) Double-layered (Weaving factor: )
Ne 16
Code P101 P102 P103 P201 P202 P203
Width 156 156 157 150 153 155
Warp Fre 32.8 32.8 32.6 34.1 33.5 45
Weft Fre 29.7 26.9 24.3 61 45 30
Ne 20
Code P104 P105 P106 P204 P205 P206
Width 155.5 156 157 150 152 154
Warp Fre 32.9 32.8 32.6 34.1 33.7 33.3
Weft Fre 32.7 29.7 26.9 67.1 50.5 32.7
Ne 30
Code P107 P108 P109 P207 P208 P209
Width 153 153 154 152 153.5 154.5
Warp Fre 33.5 33.5 33.3 33.7 33.4 33.1
Weft Fre 41.9 39 36.2 81.6 61 41.9
Table 4 Properties of rips fabrics
Single-layered (Weaving factor:0.5) Double-layered (Weaving factor: )
Ne 16
Code R101 R102 R103 R201 R202 R203
Width 158 158.5 158.5 156 157 157
Warp Fre 32.3 32.3 33.3 32.8 32.6 32.6
Weft Fre 27.5 24.3 20.9 57 44.1 31
Ne 20
Code R104 R105 R106 R204 R205 R206
Width 158.2 158.5 158.8 156 157 157
Warp Fre 32.4 32.3 32.2 32.8 32.6 32.6
Weft Fre 30.7 27.5 24.9 60.3 52 37.1
Ne 30
Code R107 R108 R109 R207 R208 R209
Width 157 157.3 158 154 156 157
Warp Fre 32.6 32.6 32.4 33.3 32.8 32.6
Weft Fre 37.1 34.4 31.5 78 57 37.1
Table 5 Properties of satin fabrics
Single-layered (Weaving factor:0.5) Double-layered (Weaving factor: )
Ne 16
Code S101 S102 S103 S201 S202 S203
Width 154 153.5 153.2 151 152 153
Warp Fre 33.3 33.4 33.4 32.9 33.7 33.5
Weft Fre 28.2 31.1 34.4 56.5 45 34.4
Ne 20
Code S104 S105 S106 S204 S205 S206
Width 155 155.6 156 150 152 153
Warp Fre 33 32.9 32.8 34.1 33.7 33.5
Weft Fre 31.9 35.9 39 61.1 47.1 39
Ne 30
Code S107 S108 S109 S207 S208 S209
Width 154.5 154.6 155 152 153.6 154
Warp Fre 33.1 33.1 33 33.7 33.3 33.3
Weft Fre 39 42.5 47 74.4 61.1 47.1
A research on the investigation of physical properties of polyester/cotton fabrics 4
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8.
DOI: 10.15406/jteft.2018.04.00115
Physical and comfort tests were conducted at 20±2˚C temperature
and 65±5% relative humidity. The frequencies of the fabrics were
counted by threading from samples cut to contain at least 100 yarns
in the amount of frequency using the Method a specied in TS 250
EN 1049-2. Thickness measurement was performed with a hand type
thickness gauge at three different points of the fabric and the average
of the obtained values was taken. Thickness measurement was made
with 3 repetitions. In the determination of the weights of the fabrics,
15x 15cm2 pieces were cut from 3 different parts to represent all
samples. The pneumatically operated square meter cutter cuts 100cm2
of the sample and weighed it on a digital precision scale. Fabric
tensile strength was measured according to TS EN ISO 13934-1, three
samples were taken on both sides (in the weft and warp direction) to
include different weft and warp threads on the fabric. Samples were
taken from the edges of the samples to a thickness of 50mm. After
setting the speed of the elongation rate constant (CRE) device to
100mm/min and the distance between jaws to 200mm, the samples
were tested with pre-stress of 2-5N. The data was automatically
retrieved from the device. Tear strength test was fullled according to
TS EN ISO 13937-2. Three samples were taken in both directions (in
the weft and warp direction) to include different weft and warp threads.
The specimens were subjected to a tear test after the elongation rate
constant (CRE) was set to 100mm/min at a distance of 100mm from
the jaws. The data was automatically retrieved from the device.
The weaving factor values of the fabrics are calculated according
to the following formula, which we derived from the weaving factor
formula given in the literature.28 The calculated weaving factors were
evaluated as input parameters in this study and used in articial neural
networks. Articial neural networks were performed to estimate
breaking and tearing strength of the fabrics by using weft and warp
density, weft yarn number, weaving type and weaving factor as input
parameters.
Results
The weights and thicknesses of the woven fabrics were measured
as indicated in the material section and are shown in Figures 1-3.
Figure 1 Weight values of the single-layered fabrics (g/m2).
It has been observed that the weights of fabrics produced in the
work increased with the increase of warp and weft frequency and
decreased with the increase of weft yarn number. It has been observed
that the number of fabric types and number of fabric layers play a role
in the change of fabric thickness. It is seen that the most important
parameter in the change of the fabric width is the connection type in
the fabric braid. Amount of connection type in fabric constructions
are given in Table 6.
Figure 2 Weight values of the double-layered fabrics (g/m2).
Figure 3 Thickness values of the single and double layered fabrics (mm).
It is observed that the most important parameters in changing the
fabric width are the weft density, the number of fabric layers, and the
type 1 and type 2 connection quantities. Fabric width stretch is larger
in the fabrics having more type 5 connection than having more type 1
connection’. For this reason, the fabrics having lowest width are plain
fabrics, while the fabrics that have the largest fabric width are rips.
The tensile strengths in the weft and warp direction of the fabrics
weaved in the study are shown in Figure 4 & 5), and the tearing
strengths are shown in Figure 6 & 7.
In the regression analysis, it was seen that the weft tensile strength
of both single and double-layered fabrics was affected negatively by
the weft yarn numbers while being affected from the weft density in
the positive direction. It was determined that the effect of the weaving
factor on the fabric tensile strength was negligibly small. The reason
why there was no change in the warp tensile strengths was that the
same warp set was used in all fabrics.
A research on the investigation of physical properties of polyester/cotton fabrics 5
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8 .
DOI: 10.15406/jteft.2018.04.00115
Table 6 Amount of the connection type in the fabrics (%)
Single-layered Double-layered
Connection Plain Twill Panama Rips Satin Plain Twill Panama Rips Satin
Type 1 100 25 25 - 20 25 64 31.3 25 65
Type 2 - 50 - - 80 50 25 37.5 50 30
Type 3 - 25 25 50 - 25 11 29.6 25 5
Type 4 - - 25 - - - - - - -
Type 5 - - 25 50 - - - 1.6 - -
Figure 4 Warp and weft direction tensile strength of single-layered fabrics
(N).
Figure 5 Warp and weft direction tensile strength of double-layered fabrics
(N).
The change in warp tensile strength values arises only from
differences in fabric width due to weaving type. The statistical
evaluation showed that the weft tearing strength was inuenced by
the fabric layer number, weft yarn number, weft connection type, and
weft and warp frequency. The higher type 5 and type 1 connection
type affects the weft tearing strength positively. In type 5 and type 1
connection, the weft yarn has been held between two warp yarns and
they have become closer to each other, which makes tearing more
difcult. As the number of fabric layers increases, the tearing of the
weft yarns will have become more difcult because of the increase
in the bunch formation ratio. Weft yarn number and weft frequency
parameters affect the negative direction.
Figure 6 Warp and weft direction tearing strength of single-layered fabrics
(N).
Figure 7 Warp and weft direction tearing strength of double-layered fabrics
(N).
The reason why weft tearing was not occur in double-layered
panama fabrics was the overlaying of a large number of weft threads
due to the low threading frequency.
Articial neural networks application
Articial neural networks were applied by taking the yarn number,
weft frequency, warp frequency and fabric layer number parameters
as input parameters and weight, thickness, tensile strength and
tearing strength as output parameters. The sigmoid transfer function,
( )
1
1
x
fx e
−
=
+
, was used in the study. Separate articial neural
A research on the investigation of physical properties of polyester/cotton fabrics 6
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8 .
DOI: 10.15406/jteft.2018.04.00115
network procedures have been run for weight, thickness, tensile and
tearing strength.
Evaluation of fabrics weight and thicknesses parameters with
articial neural networks: While weft yarn number, weft and
warp density, number of layer and weaving factor were used as
input parameters, thickness and weight values measured were used
as output parameters to evaluate fabric weight and thickness with
articial neural networks.
The sigmoid algorithm was used in the hidden layer for articial
neural networks in this study. The structure of the applied articial
neural networks is shown in Figure 8. Relationship between measured
and calculated with articial neural networks is shown in Figures 9 &
10 for fabric weight and thickness, respectively.
The square root of the error squares (RMSE) values, calculated
as formula
( )
1
n
iHÖ
xx
RMSE N
=
∑−
=
, and are given in Table 7. As
you seen in Table 7, the fabric weight and thickness values calculated
using articial neural networks were close to real/measured values at
R2=0.9946 and R2=0.9916, respectively.
Because the calculated RMSE values are very low, it can be said
that the weight and thickness parameters calculated with articial
neural networks reect the actual measurement results.
Figure 8 Articial neural networks model for fabric weight and thickness
(MLP 5:5-9-1:1).
Figure 9 Relationship weight values between measured and calculated with
articial neural networks.
Evaluation of tensile strength parameter with articial neural
networks: While weft yarn number, weft and warp density, weaving
factor, thickness and weight were used as input parameters, tensile
strength both warp and weft direction measured were used as output
parameters to evaluate fabric tensile strength properties with articial
neural networks.
Table 7 Calculated RMSE values for fabric weight and thickness
Weight Thickness
Articial Neural Networks 3.300043 0.021592
Lineer Regression 12.32627 0.049799
The sigmoid algorithm was used in the hidden layer for articial
neural networks in this study. The structure of the applied articial
neural networks is shown in Figure 11. Relationship between measured
and calculated with articial neural networks is shown in Figure 12.
Figure 10 Relationship thickness values between measured and calculated
with articial neural networks.
Figure 11 Articial neural networks model for warp and weft tensile
strength (MLP 6:6-11-1:1).
Figure 12 Warp and weft tensile strength values calculated with articial
neural networks (N).
A research on the investigation of physical properties of polyester/cotton fabrics 7
Copyright:
©2018 Turker et al.
Citation: Turker E, Turan NY. A research on the investigation of physical properties of polyester/cotton fabrics. J Textile Eng Fashion Technol. 2018;4(1):1‒8 .
DOI: 10.15406/jteft.2018.04.00115
The RMSE values for both warp and weft tensile strength are
given in Table 8. As you seen in Table 8, the fabric tensile strength
values calculated both weft and warp directions by using articial
neural networks were close to real/measured values at R2=0.9988 and
R2=0.9969, respectively.
Because the calculated RMSE values are very low, it can be
said that tensile strength parameters calculated with articial neural
networks reect the actual measurement results.
Table 8 The RMSE values calculated for warp and weft tensile strength
Weft direction Warp direction
Articial Neural
Networks 10.85848 1.597895
Lineer Regression 91.53256 19.12542
Evaluation of tearing strength parameter with articial neural
networks: While weft yarn number, weft and warp density, weaving
factor, thickness and number of layer were used as input parameters,
tearing strength both warp and weft direction measured were used as
output parameters to evaluate fabric tearing strength properties with
articial neural networks.
The sigmoid algorithm was used in the hidden layer for articial
neural networks in this study. The structure of the applied articial
neural networks is shown in Figure 13. Relationship between
measured and calculated with articial neural networks is shown in
Figure 14.
Figure 13 Articial neural networks model for warp and weft tensile strength
(MLP 6:6-9-1:1).
Figure 14 Warp and weft tearing strength values calculated with articial
neural networks (N).
The RMSE values for both warp and weft tearing strength are
given in Table 9. As you seen in Table 9, the fabric tearing strength
values calculated both weft and warp directions by using articial
neural networks were close to real/measured values at R2=0.9964 and
R2=0.9944, respectively.
Because the calculated RMSE values are very low, it can be
said that tensile strength parameters calculated with articial neural
networks reect the actual measurement results.
Table 9 The RMSE values calculated for warp and weft direction
Weft direction Warp direction
Articial Neural
Networks 1.412265 1.921667
Lineer Regression 10.19919 9.177846
Discussion
a. It is seen that the fabric density values affect the fabric weight in
the positive direction while the yarn counts affect the negative
direction.
b. It is seen that the most important parameter in the change of the
fabric width is the connection type in the fabric braid.
c. The most important parameter for fabric width is the count of
type 5 connection.
d. The most important parameters for tensile strength is weft den-
sity. On the other hand, weaving factor can be neglect for this
parameter because of very small effect.
e. It is seen that the counts of type 5, type 1 connection and layers
of the fabrics affect tearing strength of the all fabrics positively.
f. The fabric weight, fabric width, tensile and tearing strength
values calculated with articial neural networks were found to
be closer to real values.
Acknowledgments
This study was supported by UBAP06 2015/TP004 Project, Usak
University. Some tests were made in the UBATAM in Usak University.
Conict of interest
Author declares there is no conict of interest in publishing the
article.
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DOI: 10.15406/jteft.2018.04.00115
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