International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01
125
Customization of Starfish Technology in
the Production of Cotton-Knit Fabrics: A Practical
Approach
A.K.M. Mobarok Hossain1 , Dr.A.B.M. Zohrul Kabir2
1
Assistant Professor, Department of Textile Technology, Ahsanullah University of
Science and Technology, Dhaka, Bangladesh
2
Professor,Islamic University of Technology,Gazipur,Bangladesh
Abstract --
Demands for Cotton-knitted garments have been
increasing steadily since 70s as consumers worldwide recognized
their comfort and adaptability for all types of regular, leisure and
sportswear. While processing order for the buyer, knit-garment
makers generally specify their requirements in terms of grams
per square meter (GS M), fabric width and shrinkage (both
length and width),based on mostly buyer’s requireme nts and
processing capability; for a finished knitted fabric of a particular
shade. The fabric suppliers (particularly the knitters), on their
part, have to choose knitting variables like machine gauge
(defined by no. of loop-forming needles per unit circumference of
the machine), yarn count (a measure of yarn fineness) & stitch
length (length of yarn in a loop).The choice of these knitting
variables is important in order to meet the quality specification of
the buyers.
S ometimes the combination of the requirements as demanded on
the finished knitted fabric, is quite impossible to achieve. For this
reason it is very common for knitted-fabric manufacturers to
carry out a fairly large numbers of sample trials when they are
required to develop a new product. These trials can consume
considerable amount of time and raw materials, and cause
considerable disruption to production schedules, before a
satisfactory solution is found. Research works have been carried
out worldwide for developing a practical system for reliably
predicting the shrinkage and dimensional properties of finished
Cotton–knitted fabrics. The most recognized effort may be that
of IIC (Recent CTI) termed as S TARFIS H. It is a computer
program, and a body of know-how which can demonstrate how
to engineer cotton circular knits so that the quality and the
performance can be right first and on time.
To use S TARFIS H with the simplest option , the user has to give
input variables of machine gauge, yarn count and stitch length
mainly as well as specifying a target value of GS M and fabric
width or shrinkage. In case of target GS M and fabric width, the
S TARFIS H gives shrinkage as outputs and in case of target
shrinkage; the software gives GS M and fabric width as outputs.
But as S TARFIS H outputs represent the results developed from
many industrial trials of different countries, the user just gets the
standard average values of GS M, Width and S hrinkage of a
particular fabric from S TARFIS H. S o to customize this software
in a particular factory, the results given by S TARFIS H has to be
calibrated according to the factory results. Though the software
provides a self-calibration method which is more experimental, a
quick calibration procedure will definitely be more users friendly
and support the application of this software more practically in a
real factory situation.
In this work, first, the relationship between S TARFIS H inputs
(yarn count and stitch length) and outputs (GS M/Width) has
been established through a set of multiple linear regression
models for each specific machine gauge. The models thus
developed have a high degree of correlation ship. Consequently,
the regression models can be used as a substitute of S TARFIS H
to predict outputs with a high accuracy under similar
environment. S econdly S TARFIS H predicted results have been
compared with recorded results of Beximco Knitting Limited (A
renowned knitting factory of Bangladesh) using standard
statistical measures in order to customize S TARFIS H as a real
factory case. It was observed that the mean absolute percentage
error (MAPE) is less than 5% for all machine gauges. The
findings thus clearly establish a quite advantageous approach for
applying such technology for the selection of decision variables.
Index Term-- Knitting, Gauge, Yarn Count, S titch Length,
GS M, S hrinkage
1.
INTRODUCTION
Knitting is a process of fabric manufacturing by interlocking
series of loops of one or more yarns. Knitted fabrics are used
to produce garments that cover every part of the human body,
in a wide range of garment types from socks, caps, gloves and
underwear to upper and lower body garments varying from Tshirts to formal jackets. The dramatic increase in the
popularity of knit fabrics during the last three decades
provides a vivid example of the interrelationships between
lifestyle, technology and fashion. The high degree of stretch
and comfort that knit cloth brings to close-fitting garments,
coupled with excellent wrinkle resistance, makes them
eminently suitable to the modern consumer s demands.
Unlike weaving, knitting cannot commence with any type of
yarn. Knitting requires a relatively fine, smooth , strong yarn
with good elastic recovery properties. Cotton yarn is proven
worldwide as particularly suitable for knit garments like
underwear, outerwear, sportswear and socks. Though the
development of synthetic fibers brought revolution to the
clothing industry, cotton-knitted fabrics have always enjoyed
great popularity among all kinds of knitted fabrics. Due to
unprecedented competition in the global apparel indust ry
customers are demanding better quality in terms of improved
performance (e.g. lower shrinkage and better retention of
shape and performance). The International Institute for Cotton
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in1988 introduced to the industry the results of an extensive
research program into the shrinkage and dimensional
properties of finished knitted cotton fabrics. The package is
marketed as a computer program called STARFISH.
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The fabric for this process is invariably knitted on circular
knitted machines. These machines vary in diameter ,
gauge(the number of needles per inch) and production
capacity. After knitting it goes to wet processing unit for
coloration and minor adjustments in finished dimensions. Also
fabric stability and handles are improved in finishing.Garment
pieces are cut from finished piece goods fabric, laid up
(spread) on to cutting tables . Marker portrays the way in
which pieces of a garment are laid out on the fabric for cutting
.The marker is laid out to a particular width of a fabric and
within an optimum length , and may represent only one size or
a mixture of two or more sizes. All parts of the garments other
than the trims are cut from the lay. Each garment pieces has all
edges cut, hence the term fully cut. The garments are
assembled by seaming machines and trims are added where
appropriate.
2.
OBJECTIVES
The objectives of this study can be summarized as
follow:
 To study the STARFISH Technology and its relevance
with knit manufacturing.
 To search for any type of possible relationship between
input and output parameters by analyzing STARFISH
results.
 To compare some relevant R&D (Research and
Development) records of a standard knit factory with
STARFISH outputs with the help
of statistical
analysis.
 To propose the calibration for customization of
STARFISH for that factory and thus establishing a
feasible general way for customization of this technology
for all circular weft-knit plants under similar production
environment.
3. 2.TRADITIONA L PRODUCT DEVELOPM ENT
PROCEDURE PRACTICED BY KNITTING PLANT
After getting fabric specification from garment manufacturing
unit, knit-plant goes for sample development. A simple flowchart is shown below.
3. THEORITICAL BACKGROUND
3.1.FLOW CHART OF KNITTED GARMENTS
MANUFACTURING
All knitted garments can be classified into four categories
according to general
Production methods:
(1)Fully cut
(2)Stitch shaped cut
(3)Fully fashioned
(4)Integral
Among these fully cut garments cover the widest range of
different types of garments, including men's, women's and
children's underwear ,swimwear, sportswear and leisurewear.
The production sequence of such garments is shown below.
Yarn receiving

Circular knitting of fabric

Scouring, bleaching and/or dyeing

Pressing, calendaring or decatizing
or stentering (finishing}

Marker making

Laying up (spreading) of fabric

Cutting

Assembly

Examine and mend

Finish press
Fabric
manufacturing
(Knitting) unit
Wet
processing
unit
Garments
manufacturi
ng unit
Fig. : Production sequence of knitted garments.
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1. Garments manufacturing unit
Function: Supplies fabric specifications,
like;
A) Fabric type (Plain Jersey, , Rib etc)
B) Fiber type (Cotton, Silk, Wool etc).
C) Fabric properties (Shade,
Shrinkage, GSM, Width etc).
2.Knit- Factory management
Function: Gives order to research and
development center of the plant for sample
development. Takes necessary steps for arranging
raw materials and executing sample development.
3.Research and development center of the factory
4. Knitting, dyeing and finishing section
Function: Takes sample development program
.
Search for similar existing R&D records
If found
If not found
Give program for
similar development
Gives trial sample programs to
knitting dyeing and finishing
section
Submits the sample
to the garment
manufacturing unit
Developed sample is OK
(Required properties have
been achieved
Developed sample is not
OK (Required properties
have not been achieved)
Fig. Rolls of different teams in the research and development (R & D) chain of a knit fabric
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Note : Sample development programs are totally controlled by
factory management for factories which do not have R&D
centers
4. EXPERIM ENTAL STEPS
The work progressed by completing the following steps
sequentially.
-Searching records of those specific samples from R&D
Department of Beximco Knitting Limited (a renowned and
standard Knit factory of Bangladesh), processing of which are
very much similar to STARFISH guided processing sequence.
-Obtaining simulated test results from STARFISH for two
output variables (i.e. GSM and Width) keeping the third
output (i.e. Shrinkage) same as the factory result.
-Finding out the deviations between STARFISH results and
factory results by standard statistical measures.
- Finding out mathematical relations between input and output
variables by analyzing the STARFISH results with the help of
MATLAB and WINQSB software so that those can be u sed
anywhere for prediction without the help of a computer.
- Forecasting the possible factory outputs with the help of
calibrated STARFISH outputs
5. STARFISH COM PUTER PROGRAM
The name STARFISH is contracted from the phrase “START
as you mean to FINISH” .It embodies the principle that, in
order to know how to produce a knitted fabric with the desired
dimensions and performance, we must first have an accurate
knowledge of the finished product. The STARFISH Kit was
first made commercially available in 1988 after several years
of preliminary testing and development in the industry. The
collection of new data and the development of improved
analytical techniques for the interpretation of the data base is
being continued by Cotton Technology International (CTI). A
simplified operational procedure of STARFISH software
(Version 5.03) is shown below
 Software Operation
Presetting
:
Units
Targets (Shrinkage/ GSM & Width)
Giving Inputs:
Fabric type
Yarn type (combed, carded etc.)
Knitting machine (specified by gauge,
diameter & no. of needles)
Yarn count
Stitch length
Wet process route (Dyeing machine type:
Jet, Winch etc.)
Nominal depth of shade (White, medium,
deep etc.)
Target values specified: (Values of GSM& Width /Shrinkage)
Getting outputs: (Values of Shrinkage /GSM& Width)
128
Note:
►For a particular fabric of a specified shade, major and
dominating inputs are yarn count, stitch length and knitting
machine.
►Generally buyer's requirement is more rigid on Shrinkage as
it is the most sensitive issue from the consumer's point of
view. So shrinkage is generally selected as target.
6.
CUSTOM ERS SPECIFICATION AND STARFISH
For a finished knitted fabric, the “customer” is the person or
organization, which decides the final performance of the
product. It may be a store group, a garment maker, a converter
or a retail division of a vertical company. The customer
usually sets out his requirement in the form of a specification,
which calls for combination of properties
like* GSM
* Width and
* Shrinkage
Sometimes this combination of properties is quite impossible
to achieve in practice. It is a well-known fact that the demands
of customers are often based largely upon wishful thinking
rather than solid experience of the product that they have in
mind. In the case of a new product this is almost inevitably the
case and is to be accepted as a fact of life – part of the process
of product evolution and improvement in response to market
opportunities. But problem arises when the demanded weight,
width and shrinkage values are mutually incompatible. Even
the customer may ask for better shrinkage on an existing
quality without allowing any changes in weight and width! If
the manufacturer has access to STARFISH and thus has the
calibrated result, then the specification can be checked and the
customer can be informed of what it is possible to achieve
without depending on sample making. Also the customer may
be offered various practical alternatives to choose if he
wishes.
7.
DATA COLLECTION
7.1 DATA SORTING FROM EXISTING R&D RECORDS
OF BEXIMCO KNITTING LIMITED
For customization of STARFISH, the first step is to find out
those production or sample records, processing of which are
very much similar to STARFISH guided processing sequence.
For this purpose the most standard and popular knitted fabricPLAIN JERSEY was selected and the recent production and
sample records of Beximco Knitting Limited were considered.
MS Excel Auto filter Option was used for doing this Also Log
books were checked manually for accuracy. The other unique
characteristics of these records are1)
The fabrics were knitted and processed in Beximco
Knitting Limited with the yarn of Padma Textiles
Limited- a sister concern of Beximco. So a high
consistency is expected in measuring production variables
and outputs.
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2)
3)
4)
129
atmospheric conditions. Therefore, the results achieved
are comparable with STARFISH generated results.
Samples were knitted in four type of knitting machines
and one type of dyeing machine. Knitting machines are of
18G30D (18 Gauge and 30” Diameter}, 20G30D,
24G30D and 28G30D respectively. Dyeing machines
are of jet type.
Samples were only solid dyed in medium deep shade
(between 4-6% dyestuff) and no reprocess or additional
chemical treatment was carried out.
Samples were subjected to a reference relaxation
procedure
(similar
to
that
of
STARFISH
recommendation) for property measurements in standard
7.2 CHART OF APPLICABLE PRACTICAL DATA FROM
BEXIMCO KNITTING LIMITED
The following tables show the list of all data that were found
as standards for the desired purpose .The collected data was
arranged knit-machine wise as shown in table (i)to table
(iv)for better understanding and implementation.
T ABLE I
DATA FROM KNITTING MACHINE TYP E : 18G, 30”D, 1728 NEEDLES
Knitting Machine Type: 18G, 30"
Dia., 1728 Needles
Yarn Count
Stitch Length
GSM
Width
Length Shrinkage (%)
Width Shrinkage (%)
1
15/1
3.32
272
56
5.3
4.3
2
16/1
3.31
231
59.5
3.6
4.2
3
17/1
3.26
231
56
4.3
6
4
17/1
3.38
222
58
4
5
5
20/1
3.14
195
54
2
7
6
20/1
3.02
205
53
5
5
7
20/1
3.07
202
54.5
2.7
6.3
8
20/1
2.78
217
51
3.5
6.8
T ABLE II
DATA FROM KNITTING MACHINE TYP E : 20G, 30”D, 1944 NEEDLES
Knitting Machine Type : 20G, 30"
Dia., 1944 Needles
Yarn Count
Stitch Length
GSM
Width
Length Shrinkage (%)
Width Shrinkage (%)
1
20
2.83
217
58
5
5
2
20
2.79
216
56
5
5
3
20
2.94
200
58
5
5
4
20
3.01
190
60
5
5
5
20
2.9
209
57.5
2.4
6.3
6
22
2.8
200
56
5
5
T ABLE III
DATA FROM KNITTING MACHINE TYP E : 24G, 30”D, 2256 NEEDLES
Knitting Machine Type: 24G, 30"
Dia., 2256Needles
Yarn Count
Stitch Length
GSM
Width
Length Shrinkage (%)
Width Shrinkage (%)
1
20
3.02
207
64
5
2.3
2
20
2.96
202
64
5
5
3
20
3.15
198
69
7
7
4
20
2.91
218
67
2.3
7
5
22
3.1
198
69.5
6
7.7
6
24
2.77
174
64.5
1.7
7.5
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24
3
157
70.5
6.8
8.6
8
26
2.63
177.5
61
5
5
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130
T ABLE IV
DATA FROM KNITTING MACHINE TYP E : 28G, 30”D, 2640 NEEDLES
Knitting Machine Type: 28G, 30" 1
Dia., 2640 Needles
26
Yarn Count
2.61
Stitch Length
GSM
180
Width
69
Length Shrinkage (%)
4.9
Width Shrinkage (%)
4.2
2
3
4
5
30
2.77
146
68
5
4
30
2.85
144
73.5
3.2
7.2
34
2.69
120
72
8.4
12.5
40
2.45
129
63
9.3
7
the consumer’s point of view it has been specified as target in
this project work rather than GSM & width. Tables (v) to (viii)
show the STARFISH outcomes for the sorted applicable data
obtained from the factory as mentioned in section 7.2 .
7.3 STARFISH GENERATED RESULTS.
For making a comparison between original factory output and
STARFISH output, similar values of inputs and target to the
software were given to get values of outputs. It may be
mentioned that as shrinkage is the most sensitive issue from
T ABLE V
ST ARFISH RESULTS FOR SAME VALUES OF FACTORY INP UTS FROM 18G MACHINES
Knitting Machine Type: 18G, 30" Dia.,
1728 Needles
1
Yarn Count
15/1
Stitch Length
3.32
GSM (Factory)
272
GSM (Starfish)
240
WIDTH (Factory)
56
WIDTH (Starfish)
61
Length Shrinkage (%) Target
5.3
Width Shrinkage (%) Target
4.3
2
16/1
3.31
231
232
59.5
60
3.6
4.2
3
17/1
3.26
231
216
56
58.8
4.3
6
4
17/1
3.38
222
213
58
60.6
4
5
5
20/1
3.14
195
196
54
57.1
2
7
6
20/1
3.02
205
200
53
54.4
5
5
7
20/1
3.07
202
199
54.5
55.8
2.7
6.3
8
20/1
2.78
217
213
51
52.5
3.5
6.8
T ABLE VI
ST ARFISH RESULTS FOR SAME VALUES OF FACTORY INP UTS FROM 20G MACHINES
Knitting Machine Type: 20G, 30" Dia.,
1944 Needles
1
Yarn Count
20
Stitch Length
2.83
GSM (Factory)
217
GSM (Starfish)
210
WIDTH (Factory)
58
WIDTH (Starfish)
58.6
Length Shrinkage (%) Target
5
Width Shrinkage (%) Target
5
2
20
2.79
216
213
56
58.1
5
5
3
20
2.94
200
204
58
60.1
5
5
4
20
3.01
190
200
60
61.1
5
5
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5
20
2.9
209
209
57.5
60.4
2.4
6.3
6
22
2.8
200
195
56
57.1
5
5
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T ABLE VII
ST ARFISH RESULTS FOR SAME VALUES OF FACTORY INP UTS FROM 24G MACHINE
Knitting Machine Type: 24G, 30" Dia.,
2256Needles
1
Yarn Count
20
Stitch Length
3.02
2
20
2.96
3
20
3.15
4
20
2.91
5
22
3.1
6
24
2.77
7
24
3
8
26
2.63
GSM (Factory)
GSM (Starfish)
WIDTH (Factory)
WIDTH (Starfish)
Length Shrinkage (%) Target
Width Shrinkage (%) Target
202
203
64
70.1
5
5
198
185
69
74.6
7
7
218
207
67
70.8
2.3
7
198
173
69.5
72.9
6
7.7
174
183
64.5
66.4
1.7
7.5
157
161
70.5
70.8
6.8
8.6
177.5
177
61
61.4
5
5
207
205
64
69.1
5
2.3
T ABLE VIII
ST ARFISH RESULTS FOR SAME VALUES OF FACTORY INP UTS FROM 28G MACHINE
Knitting Machine Type: 28G, 30" Dia.,
2640 Needles
1
Yarn Count
26
Stitch Length
2.61
GSM (Factory)
180
GSM (Starfish)
179
WIDTH (Factory)
69
WIDTH (Starfish)
70.9
Length Shrinkage (%) Target
4.9
Width Shrinkage (%) Target
4.2
8.
DATA A NALYSIS
8.1 ANALYSIS OF STARFISH PREDICTION SYSTEM
STARFISH is commercial costly software and the manual or
software itself does not reveal any mathematical technique by
which the predictions are generated. It was thought that by
considering the major knitting variables as independent
variables and software outputs as dependent variables, a
multiple linear regression model could be formed. So the three
independent variables were taken as knitting machine, yarn
count and stitch length and two dependent variables were
GSM & width, which were of our interest. Knitting machines
located in a particular factory can be divided generally into
certain categories. So classifying the data according to knitmachine wise and thus eliminating one independent variable
an easily understood model of multiple linear regression
model could be formed where independent variables were
yarn count and stitch length and dependent variables were
GSM & Width taking into mind that shrinkage was our target.
Both MATLAB and WINQSB software were used for
performing multiple linear regressions.
2
30
2.77
146
151
68
71.3
5
4
3
30
2.85
144
145
73.5
75.1
3.2
7.2
4
34
2.69
120
121
72
74.7
8.4
12.5
5
40
2.45
129
119
63
63.8
9.3
7
independent variables for a particular gauge were taken as
inputs. Then for each pair values of yarn count and stitch
length, values of dependent variables i.e. GSM and width
from STARFISH software were taken as outputs. The detailed
operation sheets have been attached in APPENDIX-A.
Summarized results of regression models from MATLAB for
different specific target shrinkages have been shown in table
(ix)to (xii) which are arranged based on knitting machine type
i.e. gauge wise. The first row of a table shows the target
shrinkages obtained from a similar type of table of section 7.3
and other rows show the corresponding regression results.
1.
Knitting Machine Type: 18 gauge, 30 inch dia., 1728
needles (18G 30”D 1728N)
Input range
Yarn Count
(Ne) = 14-22
; Selected values : 14, 16
,18, 20, 22, 24 Stitch Length (mm) =2.78-3.38
; Selected
values : 2.78, 2.96, 3.14, 3.32, 3.38
8.2 APPLICATION OF MATLAB
MATLAB is a high performance language for technical
computing. In this thesis work MATLAB has been employed
to build multiple regression models for each specific target
shrinkage of a particular gauge machine.To work with this
software, at first, some randomly selected values of yarn count
and stitch length that covers the full practical range of these
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T ABLE IX
SUMMARIZED RESULTS FROM MAT LAB REGRESSION MODELS FOR 18G MACHINE
Note :
1.
Output
Target (Length
GSM
Shrinkage and
Regression
Width
Regression
coefficient
Max
Shrinkage)
coefficient for
Constant
for
stitch
Err.
yarn count
length
Width
Regression Regression
coefficient coefficient
Max
Constant
for
yarn for
stitch
Err.
count
length
5.3%X4.3%
3.6X4.2%
4.3%X6%
4%X5%
2%X7%
-11.12
-11.32
-11.02
-11.16
-11.18
-58.08
-59.56
-57.58
-57.98
-58.79
601.47
614.1
596.54
603.38
606.09
10.68
10.95
10.81
11.04
10.88
-0.629
-0.628
-0.64
-0.634
-0.648
5%X5%
2.7%X6.3%
3.5%X6.8%
-11.08
-11.16
-11.02
-57.89
-61.32
-57.58
599.34
613.15
596.54
10.71
10.56
10.81
Approximately suitable count for a particular type of
machine (Gauge):
(Gauge) 2
Ne =
18
If Gauge = 18, then Ne = (18) 2 /18 =18
2.
3.
Practically used/useable count range in 18 gauge
machine = 14-22
Machine setting (VDQ No.) for minimum and
maximum stitch length in existing 18 Gauge machines
are 140 and 163 respectively.
2.
12.29
12.32
12.565
12.403
12.715
29.45
29.251
29.802
29.593
30.101
0.421
0.415
0.446
0.375
0.408
-0.634
12.403
29.593
0.375
-0.643
12.908
29.039
0.532
-0.644
12.689
30.057
0.4
· If VDQ = 115, then Stitch Length = [115 X
41.8(Constant)]/1728(No. of needles) = 2.78
· If VDQ = 140, then Stitch Length = [140 X
41.8(Constant)]/1728(No. of needles) = 3.38
Knitting Machine Type: 20 gauge, 30 inch dia., 1944
needles (20G 30”D 1944 N)
Input range
Yarn Count
(Ne) = 18-28
; Selected values: 18, 20,
22, 24, 26, 28 Stitch Length (mm) =2.47-3.11 ; Selected
values: 2.47, 2.63, 2.79, 2.95, 3.11
T ABLE X
SUMMARIZED RESULTS FROM MAT LAB REGRESSION MODELS FOR 20G MACHINE
Output
Target (Length
GSM
Shrinkage and
Regression
Width
Regression
Shrinkage) coefficient for coefficient Constant
for stitch
yarn count
length
5% X5%
2.4%X6.3%
-7.57
-7.7
-55.73
-56.25
522.26
529.20
Max
Err.
9.73
10.33
Width
Regression Regression
coefficient coefficient
Constant
for yarn
for stitch
count
length
-0.507
-0.517
13.521
13.698
30.398
30.897
Max
Err.
0.470
0.5
Note :
1. Approximately suitable count for a particular type of
machine (Gauge):
(Gauge) 2
Ne =
18
If Gauge = 20, then Ne = (20) 2 /18 =22.22 ≈ 22
3.
Machine setting (VDQ No.) for minimum and
maximum stitch length in existing 20 Gauge machines
are 115 and 145 respectively.
· If VDQ = 115, then Stitch Length = [115 X
41.8(Constant)]/1944(No. needles) = 2.47
· If VDQ = 145, then Stitch Length = [145 X
41.8(Constant)]/1944(No. of needles) = 3.11
2. Practically used/useable count range in 20 gauge
machine = 18-28
3.
Knitting Machine Type: 24 gauge, 30 inch dia., 2256
needles (24G 30”D 2256 N)
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133
Input range
Yarn Count
(Ne) = 20-32
; Selected values : 20, 22,
24, 26, 28, 30, 32 Stitch Length (mm) =2.59-3.02 ; Selected
values : 2.59, 2.71, 2.80, 2.91, 3.02
T ABLE XI
SUMMARIZED RESULTS FROM MAT LAB REGRESSION MODELS FOR 24GMACHINE
Output
Target (Length
GSM
Shrinkage and
Regression
Width
Regression
coefficient
Max
Shrinkage)
coefficient for
Constant
for
stitch
Err.
yarn count
length
Width
Regression Regression
coefficient coefficient
Max
Constant
for
yarn for
stitch
Err.
count
length
5% X2.3%
5%X5%
6.8%X8.6%
1.7%X7.5%
7%X7%
6% X7.7%
2.3% X7%
-0.521
-0.536
-0.559
-0.553
-0.548
-0.551
-0.548
-6.15
-6.00
-5.65
-6.03
-5.75
-5.76
-6.03
-50.14
-49.34
-46.49
-49.87
-46.78
-47.57
-49.48
476.83
466.01
439.06
469.49
445.05
448.19
468.38
Note :
1. Approximately suitable count for a particular type of
machine (Gauge):
(Gauge) 2
Ne =
18
If Gauge = 24, then Ne = (24) 2 /18 =32
2.
Practically used/useable count range in 24 gauge
machine = 20-32
3.
Machine setting (VDQ No.) for minimum and
maximum stitch length in
existing 24 Gauge
machines are 140 and 163 respectively.
7.97
7.86
7.28
8.32
8.04
7.24
8.50
14.407
14.879
15.497
15.215
15.218
15.268
15.219
35.563
36.372
37.762
37.609
37.127
37.540
37.127
0.457
0.507
0.516
0.498
0.477
0.472
0.477
· If VDQ = 140, then Stitch Length = [140 X
41.8(Constant)]/2256(No. of needles) = 2.59
· If VDQ = 163, then Stitch Length = [163 X
41.8(Constant)]/2256(No. of needles)= 3.02
4.
Knitting Machine Type: 28 gauge, 30 inch dia., 2640
needles (28G 30”D 2640 N)
Input range
Yarn Count
(Ne) = 26-40
; Selected values : 26, 30,
32, 34, 40 Stitch Length (mm) =2.45-2.93 ; Selected values :
2.45, 2.57, 2.69, 2.81, 2.93
T ABLE XII
SUMMARIZED RESULTS FROM MAT LAB REGRESSION MODELS FOR 28GMACHINE
Output
Target
(Length GSM
Shrinkage
and Regression Regression
Width Shrinkage) coefficient coefficient
Max
Constant
for
yarn for
stitch
Err.
count
length
Width
Regression Regression
coefficient coefficient
Max
Constant
for
yarn for
stitch
Err.
count
length
3.2%X7.2%
8.4%X12.5%
9.3%X7%%
5%X4%
4.9% X4.2%
-0.497
-0.524
-0.496
-0.480
-0.480
-3.88
-3.48
-3.66
-3.96
-3.97
-43.5
-38.5
-40.67
-44.17
-43.83
387.62
345.74
363.73
393.95
393.41
5.81
5.21
5.99
6.15
6.23
16.6
17.617
16.667
16.133
16.1
117801-6464 IJET-IJENS © February 2011 IJENS
42.688
45.171
42.327
41.041
41.240
0.541
0.541
0.531
0.475
0.465
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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01
Note :
1. Approximately suitable count for a particular type of
machine (Gauge):
(Gauge) 2
Ne =
18
If Gauge = 28, then Ne = (28) 2 /18 = 43.5 ≈ 43 or 44
2. Practically used/useable count range in 28 gauge
machine = 26-40
3. Machine setting (VDQ No.) for minimum and
maximu m stitch length in
existing 28 Gauge machines are 155 and 185
respectively.
· If VDQ = 155, then Stitch Length = [155 X
41.8(Constant)]/2640(No. of needles) = 2.45
· If VDQ = 185, then Stitch Length = [185 X
41.8(Constant)]/2640(No. of needles)
=2.93
8.3Exploring means of MATLAB result 1.By using the
values of regression co-efficient for yarn count and stitch
length, and intercept a regression equation can be
formed .For example, for a target length shrinkage of
2.7% and width shrinkage of 6.3% if existing 18 Gauge
machines are used then regression equations for GSM
and Width will be GSM = 613.15-11.16 Y.C(Yarn
Count).- 61.32 S.L(Stitch Length) Width=29.039- 0.643
Y.C.+12.908 S.L.2. Maximum error represents the
maximum deviation between the predicted outputs by
MATLAB regression equations and STARFISH original
outputs. For example, for a target shrinkage of 7%X7%
if existing 24 Gauge machines are used then maximum
error for GSM will be 8.04 and maximum error for width
will be 0.477.It should be noted that MATLAB does not
indicate the point where the maximum error occurs. But
putting the values from input range into the regression
equation it is found that maximum error occurs for the
extreme values of outputs. For example consider the
regression equations for GSM and Width at 5%X5%
shrinkage target in 20 Gauge machines.
134
Now error (deviation) = 63.8-63.32 = 0.48 (Maximum
value) [most accurately 0.4705≈0.470: see APPENDIX-A]
= 0.75 % only
8.4 Application of WINQSB
WINQSB is an another advanced mathematical software. It
gives a detailed Regression summary with analysis of
variance. Regression equation can also be found directly from
here. A typical example has been shown in table (xiii)
GSM = 522.26 - 7.57 Y.C. – 55.73 S.L∙ ∙ ∙ ∙ ∙ ∙ ∙ (1)
Width = 30.398 - 0.507 Y.C. +13.521 S.L ∙ ∙ ∙ ∙ (2)
When yarn count (Y.C.) is minimum, i.e. 18 and stitch length
is minimum, i.e. 2.47 STARFISH gives GSM = 258
(maximu m value)
Equation (1) gives
GSM
= 522.26 - 7.57 X 18 – 55.73 X 2.47 = 248.35
(maximu m value)
Now error (deviation) = 258-248.35= 9.65 (Maximum value)
[most accurately 9.7286 ≈9.73: see APPENDIX-A]=3.74 %
only
Again when yarn count (Y.C.) is minimum, i.e. 18 and stitch
length (S.L.) is maximum, i.e. 3.11 STARFISH gives Width
= 63.8 (maximu m value)
Equation (2) gives Width = 30.398 - 0.507 X 18 + 13.521 X
3.11= 63.32 (maximu m value)
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135
T ABLE XVIII
AN EXAMP LE OF WINQSB GENERATED REGRESSION RESULTS
Knitting Machine Type : 18G 30"D 1728N
Target : L.S.Х W.S.=2.7% Х 6.3%
Y.C.
14
14
14
14
14
16
16
16
16
16
18
18
18
18
18
20
20
20
20
20
22
22
22
22
22
S.L.
2.78
2.96
3.14
3.32
3.38
2.78
2.96
3.14
3.32
3.38
2.78
2.96
3.14
3.32
3.38
2.78
2.96
3.14
3.32
3.38
2.78
2.96
3.14
3.32
3.38
GSM
297
282
269
257
250
263
250
239
228
222
237
225
215
205
200
216
205
196
187
182
198
188
180
172
167
WIDTH
55.6
58.2
60.7
63.1
64.2
54.4
56.9
59.2
61.5
62.6
53.3
55.6
57.9
60.1
61.1
52.2
54.5
56.6
58.7
59.7
51.2
53.4
55.5
57.5
58.5
Regression Summary - GSM
Mean
Variable
Standard
Name
Deviation
GSM
221.2
35.42598
Constant
Y.C.
18
2.886751
S.L.
3.116
0.2278157
Se=4.895592
R-square = 0.9824944
Regression
Coefficient
Standard
Error
613.1526
-11.16
-61.31984
R-adjusted =
15.05345
0.3461706
4.386477
0.9809029
Analysis of Variance (ANOVA) -GSM
Source of
Degree of Sum of
Mean
Variability
Freedom Square
Square
Regression
2
29592.73 14796.37
Error
22
527.2701 23.96682
Total
24
30120
Reg. Eq. (GSM) : 613.1526 - 11.16 Y.C. - 61.31984 S.L.
Variable
Name
WIDTH
Constant
Y.C.
S.L.
Mean
57.688
18
3.116
Standard Regression
Deviation Coefficient
3.484408
29.03919
2.886751 -0.6429999
0.2278157 12.90847
Standard
Error
0.7011116
1.61E-02
0.2042993
Se =0.2280113 R-square = 0.9960747 R-adjusted = 0.9957179
Source of
Variability
Regression
Error
Total
Analysis of Variance (ANOVA) - Width
Degree of Sum of
Mean
Freedom Square
Square
2
290.2426 145.1213
22
1.143761 5.20E-02
24
291.3863
Reg. Eq. (Width) :29.03919-0.6429999Y.C.+12.90847 S.L.
8.5 EXPLORING MEANS OF WINQSB RESULTS
1) Regression equation gives a forecasted output value of
GSM or Width for a target shrinkage.
2) The coefficient of variation “ R-Square ” value is a
“goodness of fit” measure. R2 is defined as: R2 =SSR/SST
Where SSR= Regression sum of squares
SST= Total sum of squares
SSE= Sum of square error.
It ranges in value from 0 to 1.
In our case, R2 is giving a measure of the amount of
reduction in the variability of GSM or Width obtained by
using the regressor variables yarn count and stitch length
in the model . For example, R2 =0.9960747 (from
Regression Summary – GSM of shown in the table (xiii)
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3)
meaning approximately 99.61% of the variation in the
GSM values can be explained by using the mentioned
two explanatory variables. However a large value of R2
does not necessarily imply that the regression model is a
good one. Adding a variable to the model will always
increase R2 meaning the SSR has increased. In order to
keep from over massaging the data,an eye must be kept
on the adjusted R2 statistic as the more reliable indication
of the true goodness of fit because it compensates for the
reduction in the SSE due to the addition of more
independent variables. Thus it may report a decreased
adjusted R2 value even though R2 has increased, unless
the improvement in SSR is more than compensated for
by the addition of the new independent variables. In fact,
if unnecessary terms are added, the value of R2 adj will
often decrease. For example consider the GSM
regression model of the table(xiii). The adjusted R2 for
the model (R2 adj =0.9957179)
is very close to the
ordinary R2 (R2 =0.9960407)indicating a true goodness of
fit. When R2 and R2 adj differ dramatically there is a good
chance that non-significant terms have
been included in the model.
Standard Error (Se) represents the amount of scatter in the
actual data around the regression line and is very similar
in concept to the SSE . Once we have Se value, we can
take advantage of a rough thumb rule that is based on the
normal distribution and states that we have 68%
confidence the actual value of GSM or Width would be
within +/-1 Se of our predictable value. Likewise we have
95% confidence that the actual value of GSM or Width
would be within +/- 2 Se of our predicted value. As from
the example of table (xiii) of section 8.4, the
predicted value for Width (when yarn count is18 and
stitch length is 2.78) is : 29.03919-0.6429999X
18+12.90847 X 2.78 or
53.35 [by putting input
values in reg. equation : (Width) =29.039190.6429999Y.C.+12.90847 S.L] Our 68% confidence
interval would be [53.35 – 1(0.23); 53.35+1(0.23)] or
[53.12, 53.58] Our 95% confidence interval would be
[53.35 – 2(0.23); 53.35+2(0.23)] or [52.89, 53.81]
136
9. DETERM INATION OF ERROR RANGE FOR
CUSTOM IZATION OF STARFISH
9.1 STARFISH GUIDELINE AND ITS APPLICABILITY
It has been already mentioned earlier that STARFISH model
prediction equations have been developed from many
industrial trials and represent average values for typical wet
processing routes from the actual values for yarn count and
stitch length which the operator has chosen to enter. So
calibration is required to give predictions which apply directly
to own industrial situation. STARFISH prediction model
provides calibration routines which allow to modify the
predictions which STARFISH makes by establishing
calibration factor through increasing or decreasing courses and
wales per unit found in the reference state. But it is too much
experimental and consumes significant resources for
monitoring no. of courses and wales found practically in the
reference state. So to follow STARFISH guideline a sufficient
number of new developments have to be made which will then
be subjected to deep examination for reliable estimates of
reference courses and Wales. Though it will give the most
accurate calibration but factory people generally don’t
observe or keep recods of such parameters. They are also
generally not interested to work with such outputs which are
not major concern of most buyers.. So it will be a better
approach if STARFISH customization is done by determining
error range from available practical outputs with that of
STARFISH outputs. Such customization will also be easily
understood and welcomed by most knitters.
9.2 COMPARISON OF STARFISH PREDICTION WITH
PRACTICAL RESULTS
Tables (xiv)-(xvii) show the deviations between STARFISH
predictions and practical outputs for similar values of target
shrinkage(obtained from section 7.3 ).
Based on such statistical measures calibration can be done
according to knit machine (gauge) wise.
8.6 REMARK ON THE APPLICATION OF MATLAB AND
WINQSB
It is now clear that a multiple regression model satisfactorily
supports the STARFISH prediction system. If we calculate the
R2 and R2 adj for each individual trial then it may be found that
the values lie within the range 0.97-0.99 both for GSM and
Width. Once the outputs from the practical range of inputs for
a particular target from the software are obtained, then
regression equation can be established so that it can be used
confidently for prediction of outputs without the use of
computer. This may save time and help taking instant decision
while the knitter is outside his normal desk. Also these
equations may provide valuable support to other knitters
which are still out of the reach of such technology.
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138
T ABLE XIV
DETERMINING CALIBRATION FOR ST ARFISH P REDICTION FOR KNITTING
MACHINE TYP E : 18 GAUGE , 30 INCH DIA ., 1728 NEEDLES (18G 30”D 1728N)
GSM
Obs.No. STARFI
SH
(Original
)
Predictio
n
1
240
2
232
3
216
4
213
5
196
6
200
7
199
8
213
Practica AE
l
(Factory
)
272
231
231
222
195
205
202
217
32
1
15
9
1
5
3
4
WIDTH
MA APE MAP STARFI
E
E
SH
(Original
)
Predictio
n
13.3
61
0.4
60
6.9
58.8
4.2
60.6
8.75 0.5
3.88 57.1
2.5
54.4
1.5
55.8
1.88
52.5
Practical AE MA APE
(Factory)
E
MAP
E
56
59.5
56
58
54
53
54.5
51
3.91
5
0.5
2.8
2.6
3.1
1.4
1.3
1.5
8.2
0.83
4.77
4.29
2.27 5.43
2.57
2.33
2.86
AE
= Absolute Error
MAE = Mean Absolute Error
APE = Absolute Percentage Error
MAPE = Mean Absolute Percentage Error
MAE=5.43 and MAPE=2.54.
calibration is +/-5.43 or +/-2.54%
So
revised
STARFISH
Discussion of the results:
● While achieving target shrinkage by existing 18G machines,
STARFISH (original) calibration should be considered as +/2.27 or +/-3.91%
●Ignoring factory results for data no.1 we get revised
MAE=1.88 and MAPE=3.3%. So revised STARFISH
calibration is +/-1.88 or +/-3.
For Width
For GSM
● While achieving target shrinkage by existing 18G machines,
STARFISH calibration should be considered as +/-8.75 or +/3.88%
●Ignoring factory results for data no.1 (which is quite
unexpected and may be due to some catastrophic situations or
improper process monitoring ) we get revised
T ABLE XV
DETERMINING CALIBRATION FOR ST ARFISH P REDICTION FOR KNITTING MACHINE TYP E : 20G GAUGE , 30 INCH DIA ., 1944 NEEDLES (20G 30”D
1944N)
GSM
Obs.No. STARFI
SH
(Original
)
Predictio
n
1
210
2
213
3
204
4
200
5
209
6
195
Practica AE
l
(Factory
)
217
216
200
190
209
200
7
3
4
10
0
5
WIDTH
MA APE MAP STARFI
E
E
SH
(Original
)
Predictio
n
3.33
58.6
1.41
58.1
1.96
60.1
5
61.1
4.83 0
2.38 60.4
2.56
57.1
Discussion of the results:
For GSM
● While achieving target shrinkage by existing 20G machines,
STARFISH calibration should be considered as +/-4.83 or +/2.38%
Practical AE MA APE
(Factory)
E
58
56
58
60
57.5
56
0.6
2.1
2.1
1.1
2.9
1.1
1.02
3.61
3.49
1.80
1.65 4.8
1.93
MAP
E
2.77
For Width
● While achieving target shrinkage by existing 28G machines,
STARFISH calibration should be considered as +/-1.65 or +/2.77%
117801-6464 IJET-IJENS © February 2011 IJENS
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International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01
138
T ABLE XVI
DETERMINING CALIBRATION FOR ST ARFISH P REDICTION FOR KNITTING MACHINE TYP E : 24 GAUGE , 30 INCH DIA ., 2256 NEEDLES (24G 30”D 2256N)
GSM
Obs.No. STARFI
SH
(Original
)
Predictio
n
1
205
2
203
3
185
4
207
5
173
6
183
7
161
8
177
Practica AE
l
(Factory
)
207
202
198
218
198
174
157
177.5
2
1
13
11
25
9
4
0.5
WIDTH
MA APE MAP STARFI
E
E
SH
(Original
)
Predictio
n
0.97
69.1
0.49
70.1
7.03
74.6
5.31
70.8
8.19 14.45 4.49 72.9
4.91
66.4
2.48
70.8
0.28
61.4
Practical AE MA APE
(Factory)
E
MAP
E
64
64
69
67
69.5
64.5
70.5
61
4.69
5.1
6.1
5.6
3.8
3.4
1.9
0.3
0.4
7.38
8.7
7.51
5.37
3.32 4.66
2.86
0.42
0.65
Discussion of the results:
For GSM
● While achieving target shrinkage by existing 24G machines,
STARFISH calibration should be considered as +/-8.19 or +/4.49 %
●Ignoring factory results for data no.5 (which is quite
unexpected and may be due to some catastrophic situations or
improper process monitoring ) we get revised MAE=5.78 and
MAPE=3.07. So revised STARFISH calibration is +/-5.78 or
+/- 3.07 %
For Width
● While achieving target shrinkage by existing 24G machines,
STARFISH calibration should be considered as +/-3.32 or +/4.69%
●Ignoring factory results for data no.5 we get revised
MAE=3.31 and MAPE=4.69. So revised STARFISH
calibration is +/- 3.31 or +/- 4.69%
T ABLE XVII
DETERMINING CALIBRATION FOR ST ARFISH P REDICTION FOR KNITTING MACHINE TYP E : 28 GAUGE ,
30 INCH DIA ., 2640 NEEDLES (28G 30”D 2640N)
GSM
Obs.No. STARFI
SH
(Original
)
Predictio
n
1
179
2
151
3
145
4
121
5
119
Practica AE
l
(Factory
)
180
146
144
120
129
1
5
1
1
10
WIDTH
MA APE MAP STARFI
E
E
SH
(Original
)
Predictio
n
0.59
70.9
3.31
71.3
0.69
75.1
3.6 0.82 2.76 74.7
8.4
63.8
Discussion of the results:
For GSM
● While achieving target shrinkage by existing 28G machines,
STARFISH calibration should be considered as +/-3.6 or +/2.76%
Practical AE MA APE
(Factory)
E
69
68
73.5
72
63
1.9
3.3
1.6
2.7
0.8
2.68
4.63
2.13
2.06 3.61
1.25
MAP
E
2.86
For Width
● While achieving target shrinkage by existing 28G machines,
STARFISH calibration should be considered as +/-2.06 or +/2.86%
117801-6464 IJET-IJENS © February 2011 IJENS
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10. A PPLYING CALIBRATION PRACTICALLY:
A CHIEVING TARGET SHRINKAGES
Now the question arises-how one should compare the practical
outputs with STARFISH as it is impossible to guess before
production what shrinkage value will be obtained from the
finished product. The answer lies on the mathematical
definition of shrinkage. It is well known that GSM, width and
shrinkages are dependent on knitting variables. One cannot
alter them without changing knitting variables but conditional
adjustments could be made with the help of finishing
technology. For example, if a finished fabric sample has a
length of 200 cm and width of 100 cm showing shrinkage 7%
Х 2% then one can say that fabric sample will show 5%Х5%
shrinkage if length and width are adjusted to (200-200X
0.07)X (100/95) and (100-100X 0.02)X (100/95) i.e. 195.79
cm and 103.16 cm. As consumers and so customers always
take shrinkage as the most rigid issue one has to adjust th e
shrinkage level to customer's standard (e.g.5%Х5% for Plain
Jersey) before predicting through calibrated STARFISH. In
this way one can also avoid storing so many regression
equations for GSM and width keeping the most preferred
ones.
11. CONCLUSION OF THE W ORK
The results of analysis obtained from this thesis work are:
1. STARFISH prediction system can be explained through a
multiple regression model very satisfactorily. The values of R2
and R2 adj found through the regression analysis lie above 0.95
both for GSM and Width. This means that the regression
models can be used as a substitute of this software very
effectively for any type (gauge) of knitting machine.
2. As STARFISH predictions may not coincide with a
particular factory result, some standard statistical measures
like MAE , MAPE may be adopted to determine the error
range as a part of STARFISH customization .During this
project thesis MAE and MAPE for each gauge machine of
Beximco Knitting Limited were calculated and the findings
are summarized below.
i)
While using 18G machines , MAPE for STARFISH
would lie within 2.5% in case of GSM and within
3.3% in case of width
ii)
While using 20G machines , MAPE for STARFISH
would lie within 2.4% in case of GSM and within
2.8% in case of width .
iii)
While using 24G machines , MAPE for STARFISH
would lie within 3.1% in case of GSM and within
4.7% in case of width .
iv)
While using 28G machines, MAPE for STARFISH
would lie within 2.8 % in case of GSM and within
2.9% in case of width.
12.
END W ORDS
Although cotton-knit fabrics have been manufactured for
decades, prediction of GSM, width and shrinkage is still
regarded as the most widespread and difficult problem with
the performance of such fabrics. In fact, very few people in the
industry know how to calculate the weight, width and
139
shrinkage after dyeing and finishing of a given quality of
knitted fabric before it has ever been manufactured. The resu lt
is that, all over the world, product development of cotton
knits is carried out on a “ trial and error” basis followed by
adjustment and re-adjustment during successive batches of
bulk production. By customization of STARFISH
Technology, the factory management of a knit plant can save a
great deal of time and money by answering many questions
and eliminating unworkable ideas before financial, physical
and human resources are committed. As shown in this work,
the production management of Beximco Knitting Limited now
can predict satisfactorily about its factory outputs with the
help of calibrated STARFISH results. Though the calibration
was done only for Plain Jersey fabric of medium deep shade
but the calibration procedure is similar for all types of cottonknitted fabric of every shade. Again the regression model of
STARFISH software, developed in this work, would be a
fantastic tool for a knit-manufacturer as he can consult it
confidently outside of the computer desk. Though calibration
task involved in this work was based on limited data, the
factory management can utilize the software to find what
GSM and width will be derived from a typical knitting
machine for a particular dyed fabric. The predictions would be
more accurate if the management generates more STARFISH
recommended data for upgrading calibrations. As the last
words it must be remembered that STARFISH does not
remove the need for production of a sample prior to full-scale
manufacturing. So it is necessary to make a trial piece or two
and have them processed. One needs to make sure that they
conform to what is expected by taking measurements. Then he
should get the customer to approve the samples, examining
both performance and aesthetics. Also during full-scale
production, samples from the bulk should be tested. The
customer is demanding for a particular output and the
manufacturer can not go without submitting practically what
the customer wants.
A CKNOWLEDGM ENTS
The present work was supported by Beximco Knitting
Limited.The authors would like to thank the employees of
Beximco Knitting Limited for their co-operative hands.
[1]
[2]
[3]
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[5]
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[8]
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The STARFISH Approach to High Quality Cotton Knitgoods (Usermanual Version 88:1)-International Institute for Cotton (IIC); U.K.,
1988
The Production of High Quality Cotton Knitgoods-UNIDO Seminar
Report; U.K., 1984
Brackenbury,T.: Knitted Clothing Technology -1 st Edition,Blackwell
Science Limited;U.K.,1992
Spencer,D.J.: Knitting Technology -3 rd Edition, Woodhead Publishing
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Eppen,G.D,Gould,F.J,Schmidt,C.P.,Moore,J.F.,Weatherford:Introducto
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117801-6464 IJET-IJENS © February 2011 IJENS
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