Original article
Prediction of the tactile comfort
of fabrics from functional finishing
parameters using fuzzy logic and
artificial neural network models
Textile Research Journal
2019, Vol. 89(19–20) 4083–4094
! The Author(s) 2019
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0040517519829008
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Melkie Getnet Tadesse1,2,3 , Emil Loghin1, Marius Pislaru1,
Lichuan Wang2, Yan Chen2, Vincent Nierstrasz3 and
Carmen Loghin1
Abstract
This paper aims to predict the hand values (HVs) and total hand values (THVs) of functional fabrics by applying the fuzzy
logic model (FLM) and artificial neural network (ANN) model. Functional fabrics were evaluated by trained panels
employing subjective evaluation scenarios. Firstly, the FLM was applied to predict the HV from finishing parameters;
then, the FLM and ANN model were applied to predict the THV from the HV. The estimation of the FLM on the HV was
efficient, as demonstrated by the root mean square error (RMSE) and relative mean percentage error (RMPE); low values
were recorded, except those bipolar descriptors whose values are within the lowermost extreme values on the fuzzy
model. However, the prediction performance of the FLM and ANN model on THV was effective, where RMSE values of
0.21 and 0.13 were obtained, respectively; both values were within the variations of the experiment. The RMPE
values for both models were less than 10%, indicating that both models are robust, effective, and could be utilized
in predicting the THVs of the functional fabrics with very good accuracy. These findings can be judiciously utilized for
the selection of suitable engineering specifications and finishing parameters of functional fabrics to attain defined
tactile comfort properties, as both models were validated using real data obtained by the subjective evaluation of functional fabrics.
Keywords
fuzzy logic, hand prediction, total hand value, hand value, trained panels, artificial neural network
Quality control, product inspection, the set up of product specification details, and determining the market
value of functional clothing goods should consider the
tactile comfort properties besides other comfort dimensions. The quality of fabric can be judged by means of
the reaction obtained from the sense of touch/handle.
Fabric hand can be defined as people’s sensory
response toward the combined properties of physical,
physiological, psychological, and social reactions
toward fabric. Alternatively, it can be defined as people’s response toward roughness, smoothness, hardness, etc., while the total hand value (THV), which is
the major component of the quality of the fabric,1 is the
perception of the human beings to the total comfortableness or the reverse side of the fabric during wearing.
The tactile perceptions of the textile can be ranked by
trained panels in order to suit the quality needs of the
consumer or to obtain manufacturing parameters, as
claimed by Pense.2 During attaining tactile perceptions
1
Faculty of Textiles, Leather and Industrial Management, ‘Gheorghe
Asachi’ Technical University of Iasi, Romania
2
College of Textile and Clothing Engineering, Soochow University, China
3
Textile Materials Technology, Department of Textile Technology, The
Swedish School of Textiles, Faculty of Textiles, Engineering and
Business, University of Boras, Sweden
Corresponding author:
Melkie Getnet Tadesse, Faculty of Textiles, Leather and Industrial
Management, Gheorghe Asachi Technical University of IASI, 53, D.
Mangeron Blv. Iasi-700050, Romania.
Email: tadesse.melkie@tuiasi.ro/melkiegetnet23@gmail.com
4084
with human skin, the various combinations of design
factors could be provided on the basis of the contact
mechanics of the functional textile fabrics with human
skin in order to get functional fabric products with the
best quality and wearability.
For tactile comfort evaluation, a textile product
could be judged using subjective (human panel) or
objective (instrumental measurement) means or combinations of these two methods. Tactile sensory evaluation, which centers on subjective assessment, has been
applied for grading the tactile comfort of textile materials by employing a defined set of human panels.3–7
The sensory evaluation has been achieved by choosing
textile material surface property descriptors, such as
rough, smooth, soft, and other attributes. However,
subjective evaluation gives only qualitative data unless
analyzed by statistical methods.8 Furthermore, measuring the low-stress mechanical properties of textile products is also possible using Kawabata evaluation system
(KES)9 and fabric assurance by simple testing (FAST)10
methods. The obtained data set using these instruments
can be correlated with subjective evaluations of tactile
comfort (handle).
Some statistical applications, such as principal component analysis (PCA), have been utilized to optimize
and examine the sensory properties.11,12 PCA has been
used to reduce the big data provided by panels
with negligible information loss. Moreover, statistical
techniques, such as the regression model,13,14 Weber–
Fechner law,15 and Steven’s power law,16 have been
utilized to analyze tactile perception. However, there
are a few limitations on these statistical techniques
based on the report by Zeng et al.17 The abovementioned classical computing techniques depend on
a great number of data points or they are unable to
solve complex relations.
Most recently, many scholars have been employed
the fuzzy logic model (FLM) and artificial neural network (ANN) model to predict the hand values (HVs) of
textile materials.11,18–23 Intelligent prediction systems
have the potential to change the subjective evaluation
methods to analyzable data sets. Furthermore, these
methods are more effective, simple, and suitable for
HV prediction. The use of fuzzy logic for the prediction
of the tactile property of textile products is widely
increasing, starting from the its introduction by
Zadeh.24 The knowledge-based information has been
utilized to understand the existing situation in the
fuzzy logic approach.22 The effect of textile finishing
on textile hand can be predicted using a fuzzy logic
system.25
Fuzzy logic applies logical operators and the
‘if–then’ rules to establish the qualitative association
between variables.26 The fuzzy logic system is another
paradigm shift that extends the crisp set concept to a set
Textile Research Journal 89(19–20)
that permits an object that can belong partially to a set,
called a fuzzy set. This means an object in a fuzzy set
may not necessarily to be a member of a set. For example, letting U be a scope of objects x, a fuzzy set A is
defined by the set of unions as follows:
A ¼ fðx, AðxÞÞ=x"U, AðxÞ : U ! ½0, 1
ð1Þ
The scope U is named as the universe of discourse,
and a function A(x) represents the membership function (MF).
In this paper, we extend the application of FLM
and ANN tools from ordinary to functional fabrics.
The FLM and ANN model were applied for the prediction of THV from the sensory attributes (HVs) of
functional fabrics, which were rated by trained panels.
Furthermore, the HVs of the functional fabrics were
predicted from numerous finishing parameters, such
as inkjet printing, three-dimensional (3D) printing,
screen printing, coating, and knitting, utilizing an
input data and the HV using an output data using
fuzzy logic.
Experimental details
Materials
Nine functional fabrics with various preparation
techniques (inkjet printing, screen printing, coating,
3D-printing, integration of smart fiber using a knitting
operation) were chosen for the prediction of HVs and
THVs (Table 1). The substrate (except for samples G
and H) used for the making of functional fabrics was
polyester fabric (158 gsm from Almedahl AB, Sweden);
the structure is plain woven with 22 picks per cm and 30
ends per cm.
Methods
Sensory evaluation. The sensory and overall hand performance tests for the functional fabrics were carried out by
trained panels using visual subjective evaluation (VSE)
and blind subjective evaluation (BSE) scenarios. Ten
panelists, aged between 23 and 54, from GA Technical
University of Iasi (Romania) with a textile background,
were recruited based on their voluntariness and experience. The panelists were brainstormed about the bipolar
attributes, rating scale, handling system, rating
methods, and evaluation protocols based on AATCC
evaluation procedure 5-2011. Defined criterions of sensory bipolar attributes of HVs, such as warm–cool (WC),
itchy–silky (IS), sticky–slippery (SS), rough–smooth
(RS), hard–soft (HS), thick–thin (TT), non-compressible–compressible (NCC), non-stretchable–stretchable
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Tadesse et al.
Table 1. Production details and surface properties of functional fabrics
Sample
Functionality
Weight (g/cm2)
Finishing type; surface properties; intended uses
A
B
C
D
E
F
G
H
I
Photochromic
Conductive
Dielectric
Conductive
Conductive
Conductive
Conductive
Conductive
Thermochromic
182 0.5
185 0.4
–
188 0.2
178 0.3
186 0.3
189 0.4
190 0.5
245 0.5
Inkjet printing; K/S: 1.30 0.21; UV-sensor T-shirts
3D printing; SR: 0.78 0.32 V«1; T-shirts (EM-shielding)
3D printing; onset temp: 150oC; fashion dress (ELD fabrication)
Inkjet printing; SR: 0.168 0.013 kV«1; medical mattresses (sensor)
Coating; SR: 7.98 0.969 V«1; military jacket (sensor)
Screen printing; 4.41 0.396 V«1; T-shirts (ECG connection)
Cotton–steel knitted; SR: 69.1 0.32 V«1; cape (EEG sensor)
PET–copper knitted; SR: 0.16 0.31 V«1; cape (EEG sensor)
Screen printed; K/S: 4.63 0.32; T-shirts (aesthetic effect)
K/S: color space; SR: surface resistance; PET: polyester fabric; EM: electromagnetic shielding; ELD: electroluminescence device; EEG: electroencephalogram; ECG: electrocardiogram; UV: ultraviolet; 3D: three-dimensional.
Table 2. Attributes and scales for total hand value (THV) evaluation
Subjective evaluation for total comfortability (total handle value)
Sample
1
2
3
4
5
Evaluators
E1
E2
The most uncomfortable
Uncomfortable
Medium
Comfortable
Most comfortable
(NSS), heavy–light (HL), and stiff–flexible (SF), were
used to evaluate the HVs. A box with two holes was
provided for BSE, while the panelists were kindly
requested to squeeze, touch, and move back and forth
their hands to rate the given samples. The panelists were
asked first to differentiate among the two bipolar attributes (for instance, whether the fabric feels warm or
cold), then they were invited to rate (for instance, if
they rate warm, then they were asked to give numbers
from 0 to 4; if they rate cold, they were asked to rate
from 6 to 10; on the other hand, if they rate in between
warm and cold, the facilitator provided scale 5, based on
the definition of the scales mentioned below).
A five-point scale (Table 2) was implemented to rate
the THV of the samples for the specific end use (see
Table 1). An 11-point scale was applied for HV evaluation. For instance, in WC HV, scales are defined as
follows: 0: extremely warm; 1: very warm; 2: reasonably
warm; 3: fairly warm; 4: less than fairly warm; 5:
neither warm nor cold; 6: less than fairly cold; 7:
fairly cold; 8: reasonably cold; 9: very cold; and
10: extremely cold.
Figure 1 shows the overall structure of this article.
The coating and inkjet printing parameters were
included as an example to indicate how the FLM prediction uses a data source for HV estimation.
Fuzzy logic modeling. The theoretical aspects of the
fuzzy sets have been defined and explained well by
various textbooks, including Klir and Yuan27 and
Zimmermann.28 The fuzzy sets are an extension of
crisp sets in which variables are right or wrong, short
or long, and 0 or 1. However, in fuzzy logic theory, a
fuzzy set covers a partial MF that varies from 0 to 1 to
describe uncertainty for classes that do not have clearly
defined boundaries. A MF for each fuzzy set can be
generated, which is a typical representation that converts the input, in this case, HV, from 0 to 1 to quantitative values, called ‘fuzzification.’ For this paper, the
Mamdani technique29 was applied to convert the qualitative data set into an interpretable one. Furthermore,
the trapezoidal MFs were chosen for demonstration.
The trapezoidal MF can be defined by four parameters
{a, b, c, d} as follows using min max
xa
dx
trapezoidðx; a, b, c, dÞ ¼ max min
, 1,
,0
ba
dc
ð2Þ
The parameters with a x b, b x c, c x d,
d x determine the X coordinates of the four corners
of the trapezoidal MFs, as shown in Figure 2.
We choose the trapezoidal MF as it applies a simple
formula and has worthy computational efficiency,
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Textile Research Journal 89(19–20)
Figure 1. An illustration showing (a) conductive solution preparation, (b) the making of conductive samples using coating and inkjet
printing, applying various parameters, and (c) subjective evaluation by 10 trained panelists (e.g. rough–smooth bipolar descriptors).
The various finishing parameters and optimized (F1 and F2) rated hand values (HVy) using principal component analysis (PCA) were
utilized to obtain predicted hand values (HVp) using the fuzzy logic model (FLM). Then the HVy values were employed to predict the
total hand value (THV) using the artificial neural network model and FLM, where the Pearson correlation coefficient (Pearson’s r) was
applied to choose the significant HVy values for THV prediction. HV: hand value; PEDOT-PSS: poly(3,4-ethylenedioxythiophene)
polystyrene sulfonate.
A single fuzzy set was obtained by the ‘aggregation’ principle and, consequently, the aggregated fuzzy sets were
transformed into a single crisp output by the defuzzification norm. Amongst the defuzzification techniques, the
centroid scheme is the most prevalent and physically
engaging of all the methods. Hence, the final output is
the fuzzier set. PCA was utilized to reduce the large
dimensions30 and to extract manageable data before
applying fuzzy logic to the prediction of HVs of the
functional fabric.
Figure 2. Trapezoidal membership functions (MFs).
contains four parameters, and is used extensively in realtime implementations. In addition, a straight line MF
has the advantage of simplicity.
Then fuzzy rules were established using phonological
terms, which provide a quantitative representation
of fuzzy input and output data sets. Mamdani rules,
‘if–then,’ were established by utilizing two input variables (HVs) to describe the single output (THV).
Mamdani rules were established as follows
If x isAj, y is Bj and w is Cj then z is Dj
where x, y, w, and z are HV attributes representing three
inputs and one output; Aj, Bj, Cj, and Dj are the qualitative values of the attribute x, y, w, and z, respectively.
Artificial neural network. The ANN has been applied to
predict the subjective comfort of technical textiles.22,31
THVs for each fabric were predicted using an ANN by
applying the feed-forward backpropagation algorithm
(FFBP) method following the rules in Wong et al.22
and Luo et al.,32 and the mean square error (MSE)
was utilized to evaluate the prediction performance.
Gradient descent with momentum and adaptive learning rate training with 1 103 epochs was employed.
The depiction of the ANN function was performed
with max-fail of 1 103. The ratings of WC, IS, etc.,
given by a panel are considered as input vectors and the
overall hand (THV) results given by panelists were
taken as the target values. The sample values were
equivalent to the input values that were implemented
to predict the target values. The network was trained in
MATLABÕ . Similarly, network simulation was carried
out to predict the sample data. In this study, we chose
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Tadesse et al.
the FFBP structure for our simulation architecture.
Since the ANN is flexible and an arbitrary number of
input variables are possible,21,33 10 fabric hand properties were fed into the input layer. Then they propagate
via the hidden layer to reach the output layer.
Furthermore, based on the internal and external information that flows in the network, the ANN is an adaptive system and changes its structure during the
learning phase.34
Selecting significant HVs for THV prediction. In order to
decide the most significant HVs that affect the THV
to the maximum possible, Pearson correlation was
computed and a correlation matrix was established
between the HVs and the THV obtained during subjective assessment. Based on the correlation results,
there are few HVs that can influence the THV at the
highest level, even if all HVs can affect the THV.
Therefore, we chose the top three (strong correlation
with their absolute value) HVs that influence the THV.
Hence, the THV was considered as an output variable,
while HVs provided by panels were considered as input
variables. Table 3 shows the nominated HVs to predict
the THVs of the samples.
As shown in Table 3, the correlations between the
bipolar descriptors to each fabric sample are different.
It is worth mentioning that each fabric sample has different surface properties due to the exploitation of various finishing parameters, such as temperature, chemical
concentration, methods of applications, and much
more. That is why each fabric sample possesses different correlations results.
For example, let y be the actual output THV given
by the panels, p be the predicted value using fuzzy logic,
and z ¼ (z1, z2, z3) are the bipolar descriptors (input
values) chosen by Pearson correlation, respectively.
Table 3. Selected hand value for total hand value prediction
using fuzzy logic modeling
Samples
Selected hand values
Correlations
A
B
C
D
E
F
G
H
I
WC; HL; HS
HL; RS; IS
NSS; HS; WC
HL; SF; RS
SS; HS; IS
SF; HL; SS
IS; WC; RS
HS; SS; NSS
SF; IS; TT
–0.61; –0.51; –0.41
0.77; 0.70; 0.61
0.54; 0.43; –0.32
0.76; 0.67; 0.65
0.70; 0.61; –0.52
0.72; 0.51; 0.38
0.61; 0.51; 0.44
0.56; –0.42; 0.34
–0.50; 0.45; 0.41
WC: warm–cool; HL: heavy–light; HS: hard–soft; RS: rough–smooth; IS:
itchy–silky; NSS: non-stretchable–stretchable; SF: stiff–flexible; SS: sticky–
slippery; TT: thick–thin.
Then, from N fabric samples, we get n output predicted
data denoted by P ¼ (p1, p2. . ., Pn), n actual output data
denoted by y ¼ (y1, y2. . ., yn), and Z ¼ (z1, z2. . ., zn)
input data points. Our goal to this section was to
choose the most significant bipolar descriptors so that
our prediction performance could be superior. After
selecting the relevant HVs, THVs were predicted from
the HVs using the FLM. In the future, a large number
of functional fabrics can be produced so that a large
data set can be used to predict the tactile comfort of the
functional fabric and an adaptive neuro-fuzzy inference
system (ANFIS) can be used to effectively predict the
subjective data.
Results and discussion
The subjective evaluation provides imprecise data
describing the functional fabric hand. Hence, its interpretation with respect to quantifiable data should be
exploited. In this paper, we have considered the subjective evaluation results of functional fabrics prearranged by panels as input data to predict the overall
handle of functional clothing and get computable
data by applying the FLM and ANN model.
Clothing comfort is a complex process that depends
on various psychological sensory perceptions. The psychological perception, on the other hand, is dependent
on various factors, such as gender, age, know-how,
physical location, and professional background. Even
with these differences, the level of agreement between
the panelists was within an acceptable level; the calculated Pearson correlations between the panels were up
to 0.96; 73% of correlations were high linear correlations (0.7 r 1.0), 24% of correlations were significant linear correlations (0.4 r 0.7), and only 2%
were low correlations (r 0.4); no correlations existed
with zero values. Individual sensitivities of the fabric
handle properties, such as WC, IS, HS, RS, and
others, are quite diverse. In order to narrow down
these dissimilarities, we utilized the FLM and ANN
model to quantify the comfort of functional fabrics.
This study indicates the prospect of predicting the
HVs and the THVs of functional fabrics using the FLM
and ANN model. Both models showed an effective
means of predicting the comfort of functional fabrics
where their performances were assured by calculating
the MSE, relative mean percentage error (RMPE), and
STDV. The resultant data may perhaps be utilized for
checking the quality of the functional fabrics produced
by similar procedures with respect to tactile comfort.
Prediction of HV from finishing parameters
In the FLM, fuzzy variables were constructed for each
bipolar attribute. The HVs given by individual panelists
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Textile Research Journal 89(19–20)
for each bipolar attribute were transformed into an
optimized input data (only the first two principal components, PC1 and PC2, were utilized for prediction purposes (for ease of computation). Table 4 shows the
average and standard deviation values of each attribute
for each sample.
Optimized results using PCA are shown in Table 5.
PCA is only utilized to reduce the large data set into an
optimized one. After reducing, the data sets were
selected to predict HVs using the FLM.
R studio software was used to compute PCA. Based
on the PCA rules, those principal components having
at least 1.0 standard deviation must be considered for
further computation. However, in this paper, we only
utilized two principal components for ease of applying
fuzzy logic. It is very clear that as the number of principal components increased, the performance of prediction was enhanced. For this work, the eigenvalues were
calculated from the standard deviations and then variances were computed from the eigenvalues. For
instance, for the WC attribute, the computed variance
for the F1 and F2 components was 60% to explain
the data set.
For the HV, each attribute was predicted utilizing
Mamdani fuzzy logic rules and the trapezoidal MF; for
instance, for fabric A, and for the WC attribute, the
results are displayed in Figure 3.
Mamdani rules were carefully designed based on the
information obtained in the course of sensory perception
during subjective evaluation of each attribute on the
overall quality (THV) of the textile fabric that a greater
number of panelists agreed upon. Hence, optimized
PCA values were converted into three fuzzy MFs,
small, medium, and big, and the fuzzy subsets were
scaled between –1 and +1.11 The output HV scaled
from 0 to 10 was converted into five fuzzy subsets:
very small, small, medium, big, and very big and, finally,
the fuzzy rules were constructed for each bipolar attribute. Five fuzzy sets were selected as it was easier for the
rule construction for the inclusive number of fuzzy sets.
The fuzzy rules are as follows:
1. if (F1 is small) and (F2 is small) then (HV is very
small);
2. if (F1 is small) and (F2 is small) then (HV is small);
3. if (F1 is medium) and (F2 is medium) then (HV is
medium);
4. if (F1 is medium) and (F2 is medium) then (HV is
big);
5. if (F1 is big) and (F2 is small) then (HV1 is medium);
6. if (F1 is big) and (F2 is small) then (HV is big);
7. if (F1 is big) and (F2 is medium) then (HV is big);
Table 5. The optimized input data sets for warm–cool using the
fuzzy logic model
Optimized values
Samples
F1
F2
A
B
C
D
E
F
G
H
I
0.21
0.08
0.33
0.45
0.46
0.38
0.35
0.28
0.28
0.58
0.70
0.24
0.06
0.02
0.04
0.01
0.01
0.34
Table 4. The mean and standard deviation results of hand values as rated by panelists
Fabric samples
Hand
values
A
B
C
D
E
F
G
H
I
WC
IS
SS
RS
HS
TT
NCC
NSS
HL
SF
5.7 0.7
5.2 0.9
3.9 0.9
5.5 1.1
3.0 0.7
5.6 1.2
0.7 0.5
0.5 1.0
5.3 1.1
2.7 1.2
7.6 1.5
3.7 1.4
7.0 1.6
2.6 1.4
1.0 0.9
1.7 1.1
0.6 0.7
0.4 0.9
3.5 1.1
0.8 0.8
7.5 1.3
3.3 1.3
2.4 0.8
3.8 1.0
5.2 0.9
5.6 1.2
1.2 1.0
0.8 0.8
3.3 1.8
3.2 1.2
5.2 1.1
7.1 1.1
7.2 0.9
6.8 1.5
6.7 0.8
4.2 1.4
1.2 0.7
3.0 0.9
6.2 1.0
7.2 1.2
4.7 0.8
6.3 1.2
4.4 0.7
6.2 1.2
5.9 1.1
5.7 1.1
1.6 1.3
1.3 1.6
6.5 1.2
6.0 1.3
5.7 0.8
5.6 1.3
4.3 0.8
4.7 0.7
4.5 1.5
4.6 1.2
1.3 1.2
1.0 1.2
7.0 1.2
6.0 1.8
4.3 0.9
1.8 1.5
5.6 1.3
2.5 0.9
2.1 1.1
4.2 1.1
1.4 0.8
6.7 1.1
6.4 1.8
1.8 1.1
5.9 1.5
2.5 0.8
3.2 0.8
3.6 1.1
3.9 1.2
7.0 1.2
1.3 1.1
7.1 1.0
7.3 1.4
6.1 2.0
5.5 1.2
5.0 1.2
5.5 1.3
5.4 1.1
2.5 1.1
5.4 0.9
0.4 0.5
0.3 0.5
6.0 1.2
2.8 1.3
WC: warm–cool; IS: itchy–silky; SS: sticky–slippery; RS: rough–smooth; HS: hard–soft; TT: thick–thin; NCC: non-compressible–compressible; NSS:
non-stretchable–stretchable; HL: heavy–light; SF: stiff–flexible.
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Tadesse et al.
8. if (F1 is big) and (F2 is big) then (HV is very big).
Figure 4 shows the correlation of the HVs with the
optimized PCA values. Then, all of the mentioned
Figure 3. Fuzzy logic model (FLM) predictions of fabric A for
the warm–cool (WC) attribute. The FLM was applied for sample
A with the WC bipolar descriptor for demonstrations. All the
samples and bipolar descriptors follow similar procedures. The
inputs are the reduced variables using principal component analysis while the output is the hand value (HV) of the sample
provided by human subjects.
values (Table 6) are obtained by applying the fuzzy
logic rules for each fabric and each bipolar descriptor.
From the FLM results, we can explain that the relationship between F1 (obtained by PCA) and HV is proportional, and the peak HV was achieved when the F1
values were between 0.5 and 1. However, the relationship between F2 and HV is not clearly defined. HV
became maximum when F2 was between –0.5 and
+0.5 and minimum otherwise. Accordingly, fuzzy
logic was employed to predict the HVs of each smart
and functional fabric for each attribute.
Thus, the prediction performance of the fuzzy
model on NCC and on NSS bipolar descriptors was
poor (RMPE > 10%). Hence, it was sometimes difficult to establish the fuzzy rules when the HVs were
within the lowest extreme values using a FLM.
However, the fuzzy model has a good performance
in predicting the HVs of the functional fabrics from
finishing parameters, except for two bipolar descriptors (NCC and NSS). Furthermore, the root mean
square error (RMSE) and STDEV values have
nearly equal values, which are the variation of the
actual evaluation.
Prediction performance of fuzzy logic for HVs
In order to evaluate the predicting capability of the
fuzzy logic on the HV using finishing parameters, the
Figure 4. Fuzzy logic model distribution of fabric A (warm–cool) showing (a) and (b) two-dimensional and (c) three-dimensional
surface plots between the reduced input and the output hand value (HV). The surface plot indicates the non-linear relationships
between the input and output data sets.
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Textile Research Journal 89(19–20)
Table 6. Hand value prediction using fuzzy logic from functional finishing parameters
WC
IS
SS
RS
HS
TT
NCC
NSS
HL
SF
FC
y
p
y
p
y
p
y
p
y
p
y
p
y
p
y
p
y
p
y
p
A
B
C
D
E
F
G
H
I
5.7
7.6
7.5
3.6
4.7
5.7
4.3
5.9
5.5
5.4
7.3
7.6
3.8
4.9
5.7
3.9
6
5.7
5.2
3.7
3.3
6.8
6.3
5.6
1.8
2.5
5.0
5.1
3.7
3.4
6.5
6.2
5.6
2.0
2.1
4.9
3.9
7.0
2.4
7.3
4.4
4.3
5.6
3.2
5.5
3.7
6.9
2.5
7.3
4.5
4.1
5.5
3.1
5.9
5.5
2.6
3.8
7.1
6.2
4.7
2.5
3.6
5.4
5.7
2.7
3.8
7.3
6.1
4.8
2.4
3.8
5.9
3.0
1.0
5.2
6.5
5.9
4.5
2.1
3.9
2.5
2.9
0.9
5.3
6.2
6.0
4.4
2.1
3.8
2.5
5.6
1.7
5.5
6.9
5.7
4.6
4.2
7.0
5.5
5.8
2.0
5.5
6.5
5.8
4.8
4.0
7.2
5.5
0.7
0.6
1.2
1.5
1.6
1.3
1.4
1.3
0.4
0.8
0.8
1.4
1.7
1.8
1.0
1.6
1.7
0.8
0.5
0.4
0.8
0.8
1.3
1.0
6.7
7.1
0.3
0.8
0.8
0.9
0.9
0.9
0.9
6.8
7.2
0.8
5.3
3.5
3.3
5.1
6.5
7.0
6.4
7.3
6.0
5.7
3.7
3.1
4.9
6.6
7.2
6.2
7.4
6.1
2.7
0.8
3.2
7.2
6.0
6.0
1.8
6.1
2.8
2.8
0.9
3.2
7.4
6.1
6.1
2.0
6.1
2.7
FC: fabric code, y: actual value given by panelists; p: predicted value using fuzzy logic; WC: warm–cool; IS: itchy–silky; SS: sticky–slippery; RS: rough–
smooth; HS: hard–soft; TT: thick–thin; NCC: non-compressible–compressible; NSS: non-stretchable–stretchable; HL: heavy–light; SF: stiff–flexible.
MSE was calculated according to11
MSE ¼
n
1X
ðy pÞ2
n i¼1
Table 7. Summary of prediction results using the fuzzy logic
model
ð3Þ
where y is the attribute score that is given by the panelists, p is the predicted score obtained using FLMs,
and n is the number of times where a bipolar attribute
was estimated for data point i. Furthermore, the frequently applied measure of the differences between the
values predicted by the FLM and the value given by the
panels could be better explained by the RMSE
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
n
1X
RMSE ¼
ðy pÞ2
n i¼1
ð4Þ
RMSE
RMPE
STDV
Warm–cool
Itchy–silky
Sticky–slippery
Rough–smooth
Hard–soft
Thick–thin
Non-compressible–compressible
Non-stretchable–stretchable
Heavy–light
Stiff–flexible
0.23
0.06
0.19
0.21
0.13
0.22
0.26
0.28
0.21
0.12
0.23
1.31
0.27
2.40
2.12
2.18
23.6
33.5
0.84
3.32
0.24
0.19
0.19
0.19
0.12
0.22
0.20
0.27
0.21
0.10
RMSE: root mean square error; RMPE: relative mean percentage error.
In addition, the average percentages of the errors by
which predictors of the model differ from the actual
values provided by panelists can also be computed
using the RMPE
n
X
y p 100%
RMPE ¼
i¼1 y
n
Bipolar descriptors
ð5Þ
where y is the attribute score rated by panelists, p is
the predicted score while utilizing fuzzy logic, and n is
the number of times where an attribute was estimated.
For each bipolar attributes, the values are shown
in Table 7.
Prediction of THV from HVs using the FLM
In a similar way, the FLM was applied to predict the
THVs using the actual HVs given by the panelists as
input parameters. Figure 5 shows the prediction of the
THV of fabric ‘A’ using the most significant three attributes (WC, HL, and HS), which were selected based on
the Pearson correlation results. The inputs are the
results of the HVs mentioned in Table 4.
Fuzzy rules were developed based on the theoretical
relationship between WC, HL, and HS and the THVs
of the textile fabrics. All the obtained results for each
fabric are displayed in Table 8.
The fuzzy rules are as follows:
1. if (WC is warm) and (HL is heavy) and (HS is hard)
then (THV is non-comfortable);
2. if (WC is warm) and (HL is medium) and (HS is
medium) then (THV is medium);
3. if (WC is medium) and (HL is medium) and (HS is
medium) then (THV is medium);
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Tadesse et al.
Figure 5. Prediction of total hand value (THV) using hand values (HVs) for fabric A using the fuzzy logic model. The THV was
predicted using the three most significant bipolar attributes (HVs) and the selection was supported by Pearson correlation analysis.
The demonstration was made only for sample A and all the samples followed similar procedures to predict the THVs from the HVs.
WC: warm–cool; HL: heavy–light; HS: hard–soft.
Table 8. Total hand value prediction from HV using fuzzy logic (p1) and artificial neural network (p2) models
Fabric
A
B
C
D
E
F
G
H
I
RMPE
RMSE
STDV
y
p1
p2
3.0
3.0
2.9
1.9
1.7
1.7
3.3
3.0
3.3
3.7
3.6
3.8
4.9
4.5
4.6
3.5
3.6
3.4
3.2
3.0
3.2
4.0
4.2
4.0
3.5
3.6
3.5
2.89
2.24
0.21
0.13
0.20
0.12
Note: y is the actual value and p1 and p2 are the predicted values.
RMSE: root mean square error; RMPE: relative mean percentage error.
4. if (WC is warm) and (HL is heavy) and (HS is
medium) then (THV is non-comfortable);
5. if (WC is cool) and (HL is light) and (HS is soft) then
(THV is comfortable);
6. if (WC is cool) and (HL is medium) and (HS is
medium) then (THV is medium);
7. if (WC is medium) and (HL is light) and (HS is soft)
then (THV is comfortable);
8. if (WC is medium) and (HL is medium) and (HS is
soft) then (THV is comfortable).
Using FLMs, the THVs were predicted using
the HVs provided by 10 human trained panelists.
As showed in Table 8, the fuzzy model effectively
predicted the THV by utilizing three HVs, as
the calculated RMPE (2.89%) is less than 10%.
The RMSE is 0.21 and hence the accuracy of the
prediction of the FLM was acceptable and has
almost an equivalent value at the variation of the
experiment (STDV).
Prediction of THV from HVs using the ANN model
The THVs were predicted with the ANN model using
the FFBP and a performance function of the MSE. We
chose this method because the training algorithm subtracts the training output from the target value to get
the error signal. It then adjusts the weights and biases in
the input and hidden layers to reduce the error.
Gradient descent with momentum and adaptive learning rate training was performed. The data were divided
into the input values (the HVs given by the panelists –
the average values were used), the target values (the
THVs given by panelists – the average values were
used), and the sample values (similar to the input
values). In order to have a sufficient number of input
variables, the data obtained by the BSE method were
exploited in addition to the VSE data. The training was
performed with ‘nntool’ (neural network toolbox) using
matlab2017b. The first layer is the input layer; the
hidden layer represents the fuzzy subsets of the input
4092
and the second output layer represents the fuzzy subsets
of the output; the last layer represents the single output.
Figure 6 displays a feed-forward backpropagation type
of ANN. The arrow vector from the input variable
nodes binds the ANN lines together into a bus to
each of N nodes (N is the number of hidden layers) in
the hidden layer.
For the hidden and the output layers, a weight was
allocated to each line. The weights were adjusted to
train the ANN to match the input with assigned
Figure 6. Feed-forward backpropagation artificial neural network (ANN) model showing the details of ANN with inputs, bias
(b), weights (w), hidden layers, the output layer, and the final
output.
Textile Research Journal 89(19–20)
target values. To get the output nodes, the weighted
vectors are summed and put via a sigmoid (S-shaped)
threshold function to go to the last output values
from the given sample values in the hidden layer.
The training was performed until the ANN outputs
a close approximation to the target values; that is,
until a sufficiently minimum MSE value obtained.
The training was performed using nntool using
MATLAB software. After creating the nntool pop-up
window using MATLAB, the input and the target data
were imported. Then, the ANN was trained at 10,000
training epochs and 0.01 functions. After training was
completed, testing, performance evaluation, and simulation were performed against the target value.
The ANN was trained until a sufficiently low value
of MSE was recorded and thus the regression line was
drawn accordingly. The results are shown in Figure 7.
A simple ANN was employed and this ANN
decreases the input variables (bipolar descriptors) first
into a set of input weight vectors and then centers the
hidden layers for each weight function and finally keeps
the output value. From Table 8, it is possible to observe
that the prediction performance of the ANN is quite
efficient in most cases. In spite of fewer input variables
being used, the results are comparable and even better
than the data obtained by Jeguirim et al.11 using an
ANN. The obtained RMPE value using the ANN is
below 10% (2.24%), and hence provides superior prediction performance. In addition, the RMPE value is
less than that obtained by the FLM. Therefore, the
Figure 7. Regression line result based on artificial neural network (ANN) training. The regression line and the highest R values in
training indicate that the data sets were well-trained using an ANN. In addition, the highest R values in validation and testing showing
the ANN was well validated and perfectly tested to predict the comfort of functional fabric.
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Tadesse et al.
prediction performance of the ANN is superior to that
of the FLM. As can be seen from Figure 7, the results of
ANN modeling, maximum R, and linear graph values
were obtained. These confirm that the data sets in the
ANN were well-trained, validated, and tested. The
RMSE values for the neural network for the fabric
hand prediction performance were within 0–1.0.35 The
RMSE value obtained using this method was 0.13,
which is quite a good sign of the efficiency of the hand
prediction using the ANN. In addition, simulation of the
training data against the input was performed and the
predicted data are shown in Table 8. Based on the result,
the prediction performances of the FLM and ANN
model were quite comparable with respect to the
RMSE and RMPE. However, when individual predicted
values were observed, the ANN model is more effective
than that of the FLM, except for fabric E, and predicting
the THV with the ANN is quite simple and out of the
biases of rules, since fuzzy logic rules vary for each individual. Even though the ANN needs more data, the performance is acceptable for this experimental work. The
results confirmed that the data sets that have been predicted using the ANN model and FLM can be used as a
specification for quality control and inspection for
prototype functional fabric production.
Conclusions
Fuzzy logic and ANN modeling were applied to predict
the handle of functional fabrics. The prediction performance of the FLM for both HV and THV was effective;
the RMPE values were less than 10% for most bipolar
attributes and the RMSE values were within standard
deviation values, which are variations of the real value
and the predicted data except for a few data sets.
Therefore, the FLM could be utilized to envisage the
HV of the functional fabrics using finishing parameters
as input data. On the other hand, THVs of the same
fabric can be predicted using HVs given by panel
judges as input data. Moreover, the prediction performance of the ANN on the THV was fairly good and comparable to that of the FLM, where low RMPE and
RMSE values were achieved. These findings indicate
that the FLM and ANN model can solve complex relations, as comfort has many dimensions. Thus, FLM and
ANN-based prediction data sets of functional fabrics
can be used for the development and production of
these fabrics in a flexible way. In the future, the
ANFIS, which combines both the FLM and ANN
model, can be performed using more data sets.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of
this article.
Funding
The authors disclosed receipt of the following financial
support for the research, authorship, and/or publication of
this article: This work was supported by the Erasmus
Mundus Joint Doctorate Programme SMDTex-Sustainable
Management and Design for Textile [Grant Number
n 2015-1594/001-001-EMJD].
ORCID iD
Melkie Getnet Tadesse
319X
http://orcid.org/0000-0002-0781-
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