Using Neural Network Method to Solve Marker Making ...

8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08)
Santander, Cantabria, Spain, September 23-25, 2008

Using Neural Network Method to Solve Marker Making “Calculation of
Fabric Lays Quantities” Efficiency for Optimum Result in the Apparel

Industry

MAHMUT KAYAR*, YELDA OZEL**
* Textile Education, Marmara University, Technical Education Faculty Goztepe Campus 34722 Istanbul

TURKEY
** Electrical Education, Marmara University, Technical Education Faculty Goztepe Campus 34722 Istanbul

TURKEY
[email protected] , [email protected]

Abstract: - Using Artificial Neural Network (ANN) method aims to obtain the highest productivity for “calculation of
fabric lays quantities” process in cutting department of apparel industry. The calculations of fabric lays quantities
(FLQC) provide us with the exact length of the marker making. Fitting the marker making with spreading table and at
the same time the possibility of spreading more than one marker making on the same table, shows the importance of
estimating the length of the marker making (MM). A system was created by applying the ANN methods to marker
making length data used in the industry. Using different possible calculations of fabric lays quantities findings, marker
making lengths are approximated resulting in that the most optimum “calculations of fabric lays quantities” have been
decided.

Key-Words: - Apparel Industry, calculating of fabric lays quantities, marker making length , Artificial Neural Network,
back propagation algorithm.

1 Introduction continues. As a result, the loss of time the by operator is
removed.
In today's increasingly global and competitive clothing In respond to variations in style, quantities, and quick
marketplace, it is imperative that apparel companies find responses, apparel manufacturers are beginning to
new techniques for the apparel production, especially experiment with new manufacturing concepts and taking
cutting and sewing processes. Cutting is a very important advantage of different systems as ANN for speed and
process in production line such as sewing process. productivity for decision making.
Decision making tools are important in the sense that Recently, ANN method has been successfully developed
they reduce the time needed for decision making and and applied to solving complex situation in some
speed up and increase the productivity of the work. different areas. This paper applies ANN system to
Decision making can be defined as; the study of estimate FLQC model to solve the time delay problem in
identifying and choosing alternatives based on the values the fabric-spreading and cutting operation. Fig. 1 is a
and preferences of the decision maker [1]. schematic of the experimental system as below.
While examining the period until the stage of fabric
cutting, choosing the most optimum “calculation of CoA-gpepnaerreal tio n
fabric lays quantities” is very important because Inpdouwsterry
preparing the marker making, fabric spreading and fabric p la nt
cutting is done according to the calculation of fabric lays
quantities. Marker making
In this process estimating the length of each possible Elneenrgghyt dvaatlaues
marker making helps in clarifying the following:
Data Processing
1. The concordance of the marker making with the
spreading table. Trai ning Tes tin g

2. Spreading more than one fabric on the same Fig. 1 Flow chart of experimental system
spreading table.

As a result the time loss in preparing the marker making
disappears, and a more productive use of the spreading
table is insured.
In addition, while the cutting process is being carried out
on one side, on the other side the spreading process

ISSN: 1790-2769 219 ISBN: 978-960-474-007-9

8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08)
Santander, Cantabria, Spain, September 23-25, 2008

2 APPAREL FLQC Calculation of Lays Quantities can be defined as the
following; the calculation of how many pieces of each
When apparel production is studied, the actual process size that can be drawn on the marker making.
occurs fallowing two steps (Processes in cutting room The marker making is: the graphic which is laid out on
can be shown as Fig. 2). These steps are cutting and the top of the spread out fabric, and which shows how
sewing. In general cutting is; the separation of a physical the fabric will be cut to ensure the most economic use of
object, or a portion of a physical object, into two it.
portions, through the application of an acutely directed While preparing the calculation of the lay quantities, the
force [2]. Cutting process in the apparel industry can be following factors should be taken into consideration.
defined as the fallowing; the lay textile surface (fabric, These are:
lining ets.) is separated to form all pieces of a garment
by using cutting pattern. This process is done with the 1. Technical factors: size distribution, fabric’s type
help of cutting instrument and machinery. The place and characteristic, cutting system’s knife length,
where the cutting process is done is called the Cutting the number of fabric layers, and the spreading
Room. The processes in the cutting room are lined up as table’s length.
below.
2. Indirect factors: workmanship, time, wastage,
1. Calculation of Lays Quantities coordination between spreading and cutting
2. Marker Making (MM) system.
3. Fabric Spreading
4. Fabric Cutting Technical factors reflect the technical limits of the
After receiving the production information, such as calculation of lay quantities and indirect factors reflect
sizes breakdown, styles, color, quantities of the orders, the limits of productivity of the following processes
etc, the first of all ‘calculation of fabric lays quantities” which are: MM, fabric spreading and fabric cutting.
is made. So fabric-lay planning will be conducted Because of that, the “calculation of fabric lays
which determine the numbers of fabric lays for quantities” has to be done to optimize workmanship,
spreading. The marker making operator produces time, wastage and the coordination between spreading
marker making and delivers it to the spreading and cutting system in the latter processes as marker
operators for fabric-spreading. After this, spreading making, fabric spreading and fabric cutting.
fiber is cut by cutting operator [3].
Table 1. Example for calculation of lay quantities
Fig 2. Schematic of Operations Fabric-cutting Department
Size 8 10 12 14 16
Distribution

Order 510 1020 1020 1530 1020
Quantity for

each size

Calculation of
sizes quantity (6) (12) (12) (18) (12)

on MM

Table 2. Technique values of example

Fabric’s Type and 30/2 Suprem (230 g/m2)
characteristic

Cutting System Automatic Cutting
System (Cutter)
Maximum Spreadable fabric 90 units*
quantity

Spreading Table Lenght 30 m

Size distrubition 8-10-12-14-16

*(It is determined by according to the harmony between
fabric’s type and characteristic with cutting system’s knife

height)

In this study an application using ANN method was
prepared to estimate beforehand the effects of marker
making length’s accordance with the spreading table
length in terms of technical factors, and the affects of
marker making length on ensuring the coordination
between fabric spreading and fabric cutting in terms of

ISSN: 1790-2769 220 ISBN: 978-960-474-007-9

8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08)
Santander, Cantabria, Spain, September 23-25, 2008

indirect factors. Example of this application is illustratedModel Experimental data used in the study total 230 taken from
as Table 1 and Table 2.type the working system for four months. %30 of the data
In the above example the results of calculation of layGender used in modeling were used during the training of the
quantities of a long sleeve man’s shirt determine the network while %30 of them were used to validate the
number of the fabric layers as 85. According to the Fabric system. Then %30 of them were tested to solve the
calculation above, size 8 = 6, size 10=12, size 12=12, width (m) problem.
size 14=18 and size 16= 12 time is drawn on marker Estimation Data was organized so that more reliable results could be
making, and on one layer 60 sizes takes place. The obtained and changed into numerical values in order for
marker making length which will be prepared Output the network to understand them. Before the separation of
according to the calculation of lay quantities can be Error the data to be used during the training and testing, data
estimated with the help of the data which have been selections were carried out randomly. Thus, the system
confirmed by ANN system as below Table 3: is trained with data reflecting the parameters of the
whole system and random data was selected to be able to
Table 3. Technique values of example achieve the best result.
The neural network structure has three layers of neurons
Size Distribution as shown in Fig. 4. The input layer consists of seven
8 10 12 14 16 neurons, ie, gender, sleeve length, size distribution (8,
10, 12, 14, 16), fabric width. The next is hidden layer
2* 1** 2 4 4 6 4 1.76 11.18 11.16 0.0209 and it has ten neurons. The output layer contains one
neuron and it represents the length of marking making.
* Long sleeve, ** Man The back propagation-training algorithm is used to train
the network. The experimental data generated is used to
Marker making length can be estimated with the help of train and validate and test the network. After the network
the data which have been confirmed by ANN system training, it can be used for decision making for the most
(table 3) as 3 x 11.1809 = 33.54 m. So It can be shown optimum marker making length.
that marker making length is longer than spreading
table’s length. So calculation of lay quantity has to Forward Output Layer Backwards
prepare as 4-8-8-12-8 and 2-4-4-6-4 twice instead of 6- Network Hidden Layer Error
12-12-18-12 which is showed in Table 1. Otherwise the Activation
marker making figure can be showing in Fig. 3 Prop agation
belonging to calculation of 2-4-4-6-4. And it is clear
that marker making’s length is almost the same with Input Layer
estimated length using ANN method.
Fig 4. ANN model structure.
Fig. 3 Marker making picture
With the aim of modeling to cover all working
3 Methodology conditions, this experimental data obtained from size
distribution to be used in training the network, as well as
3.1 ANN Solution of the System input parameters, ‘fabric width’ and ‘model type’(short
sleeve and long sleeve) values that have been stated in
apparel industry and that can be used as input parameters
were added.
As training algorithm that determines the application
process and one of the significant factors, back
propagation algorithm Levenberg-Maquardt was used.
Marquardt change parameter was determined 2.4979 in
artificial neural network. Marquardt parameter
accelerates the zero error approach of the neural
network.
In return for the given input, the output calculated by the
network is compared with the real (desired) output. The
gap between the output of the network and the real
output is calculated as error. The average of the total of

ISSN: 1790-2769 221 ISBN: 978-960-474-007-9

8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08)
Santander, Cantabria, Spain, September 23-25, 2008

the fault is attempted to minimize. This value to be

minimized MSE (Mean Squared Error) enable the

network to have smaller weight and performance values

that is one of the factors affecting the training

performance. In this study, the best result was obtained

by the use of mean squared error function. MSE can be

formulized as follow:

∑ ∑MSE = 1 Q e(k)2 = 1 Q (t(k) − y(k))2
Q k =1 Q k =1
(1)

Here, e(k) refers to the difference between the target Fig. 5 Error reduces with epochs
output and ANN output, y(k): real output, t(k): ANN
output [4-7]. The ANN is tested with seventy seven patterns. Fig. 6
Activation function that is to affect the results to reflect shows the comparison between target and tested output.
the modeling best was determined after the It is seen from the figure that target and the tested output
normalization process of input and output data in order are approximately the same.
to prevent the adverse effects following the excessive
swinging results that were fed by the network.
In the formed single hidden layer artificial neural
network model, the minimum error value was achieved
with the use of tangent hyperbolic activation function.
Different training algorithms and activation functions
were selected in order to evaluate the result correctly and
to be able to compare them [8-9].

3.2 ANN Results
In Table 4, MSE values obtained from the secret phase
number of test result and mean squared error results and
absolute change values were provided. The closer the
absolute error closes to zero, the better the system
reflects the truth. As seen, ANN model average appear
with % 9.206 error, closer to 1 in comparison to ANN
model.

Table 4. Comparison of the result based on hidden
layer

MSE (Training values) 0.016831 Fig. 6 Tested results
9.206
%Mean Squared Error 0.9422 The aim of ANN model is to increase learning and to
(Test Values) minimize the error level. The higher the number of secret
phase, the more time is required.
R2(Absolute Change)

In Figure 5, the training and validating as a function of
epochs is shown. The network is trained within 14
epochs met the goal of error of 0,001. The best
validation performance is 0.06831 at epoch 8.

ISSN: 1790-2769 222 ISBN: 978-960-474-007-9

8th WSEAS International Conference on SIMULATION, MODELLING and OPTIMIZATION (SMO '08)
Santander, Cantabria, Spain, September 23-25, 2008

Fig. 7 Taining Regrassion With this new approach, learning systems of artificial
neural networks can be concluded to be used in the
Fig. 7 and Fig. 8 show trained and tested regressions of apparel industry.
neural network.
References:
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Introduction to Decision Making, July 28, 1998)
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Fuzzy Concept in the Apparel Industry, IFSA World
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Conference, 2001. Joint 9th, 25-28 July 2001
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[5] HAYKIN, S., Neural Network, A Comprehensive
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[7] Y. Birbir, H.S. Nogay, Y. Ozel, Estimation of Low
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[8] D. E. Rumelhart, B. Widrow and M. A. Lehr., The
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Fig. 8 Tested regrassion

4 Conclusion

The artificial neural network based system has been
proposed in this paper to solve the calculation of fabric
lays quantities efficiency for optimization in the apparel
industry. The main advantage of this technique is that it
avoids the complicated mathematical modeling. The
performance of the network is judged with the real time
data. It is seen from the result that this methodology used
to solve marker making problems works excellently.

ISSN: 1790-2769 223 ISBN: 978-960-474-007-9