customization of starfish technology in the production of cotton-knit fabrics

International Journal of Engineering & Technology IJET-IJENS Vol: 11 No: 01 125

117801-6464 IJET-IJENS © February 2011 IJENS

I J E N S

Customization of Starfish Technology in

the Production of Cotton-Knit Fabrics: A Practical Approach

A.K.M. Mobarok Hossain1, Dr.A.B.M. Zohrul Kabir

2

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 (GSM), fabric width and shrinkage (both

length and width),based on mostly buyer’s requirements 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.

Sometimes 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 STARFISH. 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 STARFISH 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 GSM and fabric

width or shrinkage. In case of target GSM and fabric width, the

STARFISH gives shrinkage as outputs and in case of target

shrinkage; the software gives GSM and fabric width as outputs.

But as STARFISH outputs represent the results developed from many industrial trials of different countries, the user just gets the

standard average values of GSM, Width and Shrinkage of a

particular fabric from STARFISH. So to customize this software

in a particular factory, the results given by STARFISH 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 STARFISH inputs

(yarn count and stitch length) and outputs (GSM/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 STARFISH

to predict outputs with a high accuracy under similar

environment. Secondly STARFISH 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 STARFISH 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, Stitch Length,

GSM, Shrinkage

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 T-

shirts 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



I J E N S

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.

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. 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

Fig. : Production sequence of knitted garments.

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.

3. 2.TRADITIONAL PRODUCT DEVELOPMENT

PROCEDURE PRACTICED BY KNITTING PLANT

After getting fabric specification from garment manufacturing

unit, knit-plant goes for sample development. A simple flow-

chart is shown below.

Fabric

manufacturing

(Knitting) unit

Wet

processing

unit

Garments

manufacturi

ng unit



I J E N S

1. Garments manufacturing unit

2.Knit- Factory management

3.Research and development center of the factory

4. Knitting, dyeing and finishing section

Fig. Rolls of different teams in the research and development (R & D) chain of a knit fabric

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).

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.

Function: Takes sample development program

.

Give program for similar development

Gives trial sample programs to knitting dyeing and finishing

section

Developed sample is OK (Required properties have

been achieved

Developed sample is not OK (Required properties have not been achieved)

Submits the sample to the garment

manufacturing unit

Search for similar existing R&D records

If found If not found



I J E N S

Note : Sample development programs are totally controlled by

factory management for factories which do not have R&D

centers

4. EXPERIMENTAL 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 used

anywhere for prediction without the help of a computer.

- Forecasting the possible factory outputs with the help of

calibrated STARFISH outputs

5. STARFISH COMPUTER 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)

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. CUSTOMERS 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 fabric-

PLAIN 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 are-

1) 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.



I J E N S

2) Samples were knitted in four type of knitting machines

and one type of dyeing machine. Knitting machines are of

18G30D (18 Gauge and 30” Diameter}, 20G30D,

24G30D and 28G30D respectively. Dyeing machines

are of jet type.

3) Samples were only solid dyed in medium deep shade

(between 4-6% dyestuff) and no reprocess or additional

chemical treatment was carried out.

4) Samples were subjected to a reference relaxation

procedure (similar to that of STARFISH

recommendation) for property measurements in standard

atmospheric conditions. Therefore, the results achieved

are comparable with STARFISH generated results.

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 TYPE: 18G, 30”D, 1728 NEEDLES

Knitting Machine Type: 18G, 30"

Dia., 1728 Needles 1 2 3 4 5 6 7 8

Yarn Count 15/1 16/1 17/1 17/1 20/1 20/1 20/1 20/1

Stitch Length 3.32 3.31 3.26 3.38 3.14 3.02 3.07 2.78

GSM 272 231 231 222 195 205 202 217

Width 56 59.5 56 58 54 53 54.5 51

Length Shrinkage (%) 5.3 3.6 4.3 4 2 5 2.7 3.5

Width Shrinkage (%) 4.3 4.2 6 5 7 5 6.3 6.8

T ABLE II

DATA FROM KNITTING MACHINE TYPE: 20G, 30”D, 1944 NEEDLES

Knitting Machine Type : 20G, 30"

Dia., 1944 Needles 1 2 3 4 5 6

Yarn Count 20 20 20 20 20 22

Stitch Length 2.83 2.79 2.94 3.01 2.9 2.8

GSM 217 216 200 190 209 200

Width 58 56 58 60 57.5 56

Length Shrinkage (%) 5 5 5 5 2.4 5

Width Shrinkage (%) 5 5 5 5 6.3 5

T ABLE III

DATA FROM KNITTING MACHINE TYPE: 24G, 30”D, 2256 NEEDLES


Dia., 2256Needles 1 2 3 4 5 6 7 8

Yarn Count 20 20 20 20 22 24 24 26

Stitch Length 3.02 2.96 3.15 2.91 3.1 2.77 3 2.63

GSM 207 202 198 218 198 174 157 177.5

Width 64 64 69 67 69.5 64.5 70.5 61

Length Shrinkage (%) 5 5 7 2.3 6 1.7 6.8 5

Width Shrinkage (%) 2.3 5 7 7 7.7 7.5 8.6 5



I J E N S

T ABLE IV DATA FROM KNITTING MACHINE TYPE: 28G, 30”D, 2640 NEEDLES


Dia., 2640 Needles

1 2 3 4 5

Yarn Count 26 30 30 34 40

Stitch Length 2.61 2.77 2.85 2.69 2.45

GSM 180 146 144 120 129

Width 69 68 73.5 72 63

Length Shrinkage (%) 4.9 5 3.2 8.4 9.3

Width Shrinkage (%) 4.2 4 7.2 12.5 7

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

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 .

T ABLE V

STARFISH RESULTS FOR SAME VALUES OF FACTORY INPUTS FROM 18G MACHINES

Knitting Machine Type: 18G, 30" Dia.,

1728 Needles 1 2 3 4 5 6 7 8

Yarn Count 15/1 16/1 17/1 17/1 20/1 20/1 20/1 20/1

Stitch Length 3.32 3.31 3.26 3.38 3.14 3.02 3.07 2.78

GSM (Factory) 272 231 231 222 195 205 202 217

GSM (Starfish) 240 232 216 213 196 200 199 213

WIDTH (Factory) 56 59.5 56 58 54 53 54.5 51

WIDTH (Starfish) 61 60 58.8 60.6 57.1 54.4 55.8 52.5

Length Shrinkage (%) Target 5.3 3.6 4.3 4 2 5 2.7 3.5

Width Shrinkage (%) Target 4.3 4.2 6 5 7 5 6.3 6.8

T ABLE VI

STARFISH RESULTS FOR SAME VALUES OF FACTORY INPUTS FROM 20G MACHINES


1944 Needles 1 2 3 4 5 6

Yarn Count 20 20 20 20 20 22

Stitch Length 2.83 2.79 2.94 3.01 2.9 2.8

GSM (Factory) 217 216 200 190 209 200

GSM (Starfish) 210 213 204 200 209 195

WIDTH (Factory) 58 56 58 60 57.5 56

WIDTH (Starfish) 58.6 58.1 60.1 61.1 60.4 57.1

Length Shrinkage (%) Target 5 5 5 5 2.4 5

Width Shrinkage (%) Target 5 5 5 5 6.3 5



I J E N S

T ABLE VII STARFISH RESULTS FOR SAME VALUES OF FACTORY INPUTS FROM 24G MACHINE


2256Needles 1 2 3 4 5 6 7 8

Yarn Count 20 20 20 20 22 24 24 26

Stitch Length 3.02 2.96 3.15 2.91 3.1 2.77 3 2.63

GSM (Factory) 207 202 198 218 198 174 157 177.5

GSM (Starfish) 205 203 185 207 173 183 161 177

WIDTH (Factory) 64 64 69 67 69.5 64.5 70.5 61

WIDTH (Starfish) 69.1 70.1 74.6 70.8 72.9 66.4 70.8 61.4

Length Shrinkage (%) Target 5 5 7 2.3 6 1.7 6.8 5

Width Shrinkage (%) Target 2.3 5 7 7 7.7 7.5 8.6 5

T ABLE VIII

STARFISH RESULTS FOR SAME VALUES OF FACTORY INPUTS FROM 28G MACHINE


2640 Needles 1 2 3 4 5

Yarn Count 26 30 30 34 40

Stitch Length 2.61 2.77 2.85 2.69 2.45

GSM (Factory) 180 146 144 120 129

GSM (Starfish) 179 151 145 121 119

WIDTH (Factory) 69 68 73.5 72 63

WIDTH (Starfish) 70.9 71.3 75.1 74.7 63.8

Length Shrinkage (%) Target 4.9 5 3.2 8.4 9.3

Width Shrinkage (%) Target 4.2 4 7.2 12.5 7

8. DATA ANALYSIS

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 knit-

machine 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.

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

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



I J E N S

T ABLE IX SUMMARIZED RESULTS FROM MATLAB REGRESSION MODELS FOR 18G MACHINE

Target (Length

Shrinkage and

Width

Shrinkage)

Output

GSM Width

Regression

coefficient for

yarn count

Regression

coefficient

for stitch

length

Constant Max

Err.

Regression

coefficient

for yarn

count

Regression

coefficient

for stitch

length

Constant Max

Err.

5.3%X4.3% -11.12 -58.08 601.47 10.68 -0.629 12.29 29.45 0.421

3.6X4.2% -11.32 -59.56 614.1 10.95 -0.628 12.32 29.251 0.415

4.3%X6% -11.02 -57.58 596.54 10.81 -0.64 12.565 29.802 0.446

4%X5% -11.16 -57.98 603.38 11.04 -0.634 12.403 29.593 0.375

2%X7% -11.18 -58.79 606.09 10.88 -0.648 12.715 30.101 0.408

5%X5% -11.08 -57.89 599.34 10.71 -0.634 12.403 29.593 0.375

2.7%X6.3% -11.16 -61.32 613.15 10.56 -0.643 12.908 29.039 0.532

3.5%X6.8% -11.02 -57.58 596.54 10.81 -0.644 12.689 30.057 0.4

Note :

1. Approximately suitable count for a particular type of

machine (Gauge):

(Gauge) 2

Ne =

18

If Gauge = 18, then Ne = (18) 2

/18 =18

2. Practically used/useable count range in 18 gauge

machine = 14-22

3. Machine setting (VDQ No.) for minimum and

maximum stitch length in existing 18 Gauge machines

are 140 and 163 respectively.

· If VDQ = 115, then Stitch Length = [115 X

41.8(Constant)]/1728(No. of needles) = 2.78




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 MATLAB REGRESSION MODELS FOR 20G MACHINE

Target (Length

Shrinkage and

Width

Shrinkage)

Output

GSM Width

Regression

coefficient for

yarn count

Regression

coefficient

for stitch

length

Constant Max

Err.

Regression

coefficient

for yarn

count

Regression

coefficient

for stitch

length

Constant Max

Err.

5% X5% -7.57 -55.73 522.26 9.73 -0.507 13.521 30.398 0.470

2.4%X6.3% -7.7 -56.25 529.20 10.33 -0.517 13.698 30.897 0.5

Note :


machine (Gauge):

(Gauge) 2

Ne =

18


/18 =22.22 ≈ 22


machine = 18-28


maximum stitch length in existing 20 Gauge machines

are 115 and 145 respectively.


41.8(Constant)]/1944(No. needles) = 2.47







I J E N S

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 MATLAB REGRESSION MODELS FOR 24GMACHINE

Target (Length

Shrinkage and

Width

Shrinkage)

Output

GSM Width

Regression

coefficient for

yarn count

Regression

coefficient

for stitch

length

Constant Max

Err.

Regression

coefficient

for yarn

count

Regression

coefficient

for stitch

length

Constant Max

Err.

5% X2.3% -6.15 -50.14 476.83 7.97 -0.521 14.407 35.563 0.457

5%X5% -6.00 -49.34 466.01 7.86 -0.536 14.879 36.372 0.507

6.8%X8.6% -5.65 -46.49 439.06 7.28 -0.559 15.497 37.762 0.516

1.7%X7.5% -6.03 -49.87 469.49 8.32 -0.553 15.215 37.609 0.498

7%X7% -5.75 -46.78 445.05 8.04 -0.548 15.218 37.127 0.477

6% X7.7% -5.76 -47.57 448.19 7.24 -0.551 15.268 37.540 0.472

2.3% X7% -6.03 -49.48 468.38 8.50 -0.548 15.219 37.127 0.477

Note :


machine (Gauge):

(Gauge) 2

Ne =

18


/18 =32


machine = 20-32


maximum stitch length in existing 24 Gauge

machines are 140 and 163 respectively.




41.8(Constant)]/2256(No. of needles)= 3.02



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 MATLAB REGRESSION MODELS FOR 28GMACHINE

Target (Length

Shrinkage and

Width Shrinkage)

Output

GSM Width

Regression

coefficient

for yarn

count

Regression

coefficient

for stitch

length

Constant Max

Err.

Regression

coefficient

for yarn

count

Regression

coefficient

for stitch

length

Constant Max

Err.

3.2%X7.2% -3.88 -43.5 387.62 5.81 -0.497 16.6 42.688 0.541

8.4%X12.5% -3.48 -38.5 345.74 5.21 -0.524 17.617 45.171 0.541

9.3%X7%% -3.66 -40.67 363.73 5.99 -0.496 16.667 42.327 0.531

5%X4% -3.96 -44.17 393.95 6.15 -0.480 16.133 41.041 0.475

4.9% X4.2% -3.97 -43.83 393.41 6.23 -0.480 16.1 41.240 0.465



I J E N S

Note :


machine (Gauge):

(Gauge) 2

Ne =

18


/18 = 43.5 ≈ 43 or 44


machine = 26-40


maximum stitch length in

existing 28 Gauge machines are 155 and 185

respectively.




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.

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

(maximum value)

Equation (1) gives

GSM = 522.26 - 7.57 X 18 – 55.73 X 2.47 = 248.35

(maximum 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 (maximum value)

Equation (2) gives Width = 30.398 - 0.507 X 18 + 13.521 X

3.11= 63.32 (maximum value)

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)



I J E N S

T ABLE XVIII AN EXAMPLE 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. S.L. GSM WIDTH Regression Summary - GSM

14 2.78 297 55.6 Variable Mean Standard Regression Standard

14 2.96 282 58.2 Name Deviation Coefficient Error

14 3.14 269 60.7 GSM 221.2 35.42598

14 3.32 257 63.1 Constant 613.1526 15.05345

14 3.38 250 64.2 Y.C. 18 2.886751 -11.16 0.3461706

16 2.78 263 54.4 S.L. 3.116 0.2278157 -61.31984 4.386477

16 2.96 250 56.9 Se=4.895592 R-square = 0.9824944 R-adjusted = 0.9809029

16 3.14 239 59.2

16 3.32 228 61.5

16 3.38 222 62.6 Analysis of Variance (ANOVA) -GSM

18 2.78 237 53.3 Source of Degree of Sum of Mean

18 2.96 225 55.6 Variability Freedom Square Square

18 3.14 215 57.9 Regression 2 29592.73 14796.37

18 3.32 205 60.1 Error 22 527.2701 23.96682

18 3.38 200 61.1 Total 24 30120

20 2.78 216 52.2

20 2.96 205 54.5 Reg. Eq. (GSM) : 613.1526 - 11.16 Y.C. - 61.31984 S.L.

20 3.14 196 56.6

20 3.32 187 58.7

20 3.38 182 59.7 Variable Mean Standard Regression Standard

22 2.78 198 51.2 Name Deviation Coefficient Error

22 2.96 188 53.4 WIDTH 57.688 3.484408

22 3.14 180 55.5 Constant 29.03919 0.7011116

22 3.32 172 57.5 Y.C. 18 2.886751 -0.6429999 1.61E-02

22 3.38 167 58.5 S.L. 3.116 0.2278157 12.90847 0.2042993

Se =0.2280113 R-square = 0.9960747 R-adjusted = 0.9957179

Analysis of Variance (ANOVA) - Width

Source of Degree of Sum of Mean

Variability Freedom Square Square

Regression 2 290.2426 145.1213

Error 22 1.143761 5.20E-02

Total 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)



I J E N S

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 R

2 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 (R

2=0.9960407)indicating a true goodness of

fit. When R

2 and R

2adj differ dramatically there is a good

chance that non-significant terms have

been included in the model.

3) 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) =29.03919-

0.6429999Y.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]

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 R

2adj 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.

9. DETERMINATION OF ERROR RANGE FOR

CUSTOMIZATION 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.



I J E N S

T ABLE XIV DETERMINING CALIBRATION FOR STARFISH PREDICTION FOR KNITTING

MACHINE TYPE: 18 GAUGE, 30 INCH DIA., 1728 NEEDLES (18G 30”D 1728N)

Obs.No.

GSM

WIDTH

STARFI

SH

(Original

)

Predictio

n

Practica

l

(Factory

)

AE MA

E

APE MAP

E

STARFI

SH

(Original

)

Predictio

n

Practical

(Factory)

AE MA

E

APE MAP

E

1 240 272 32

8.75

13.3

3.88

61 56 5

2.27

8.2

3.91

2 232 231 1 0.4 60 59.5 0.5 0.83

3 216 231 15 6.9 58.8 56 2.8 4.77

4 213 222 9 4.2 60.6 58 2.6 4.29

5 196 195 1 0.5 57.1 54 3.1 5.43

6 200 205 5 2.5 54.4 53 1.4 2.57

7 199 202 3 1.5 55.8 54.5 1.3 2.33

8 213 217 4 1.88 52.5 51 1.5 2.86

AE = Absolute Error

MAE = Mean Absolute Error

APE = Absolute Percentage Error

MAPE = Mean Absolute Percentage Error

Discussion of the results:

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

MAE=5.43 and MAPE=2.54. So revised STARFISH

calibration is +/-5.43 or +/-2.54%

For Width


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.

T ABLE XV DETERMINING CALIBRATION FOR STARFISH PREDICTION FOR KNITTING MACHINE TYPE: 20G GAUGE, 30 INCH DIA., 1944 NEEDLES (20G 30”D

1944N)

Obs.No.

GSM

WIDTH

STARFI

SH

(Original

)

Predictio

n

Practica

l

(Factory

)

AE MA

E

APE MAP

E

STARFI

SH

(Original

)

Predictio

n

Practical

(Factory)

AE MA

E

APE MAP

E

1 210 217 7

4.83

3.33

2.38

58.6 58 0.6

1.65

1.02

2.77

2 213 216 3 1.41 58.1 56 2.1 3.61

3 204 200 4 1.96 60.1 58 2.1 3.49

4 200 190 10 5 61.1 60 1.1 1.80

5 209 209 0 0 60.4 57.5 2.9 4.8

6 195 200 5 2.56 57.1 56 1.1 1.93


For GSM



2.38%

For Width



2.77%



I J E N S

T ABLE XVI DETERMINING CALIBRATION FOR STARFISH PREDICTION FOR KNITTING MACHINE TYPE: 24 GAUGE, 30 INCH DIA., 2256 NEEDLES (24G 30”D 2256N)

Obs.No.

GSM

WIDTH

STARFI

SH

(Original

)

Predictio

n

Practica

l

(Factory

)

AE MA

E

APE MAP

E

STARFI

SH

(Original

)

Predictio

n

Practical

(Factory)

AE MA

E

APE MAP

E

1 205 207 2

8.19

0.97

4.49

69.1 64 5.1

3.32

7.38

4.69

2 203 202 1 0.49 70.1 64 6.1 8.7

3 185 198 13 7.03 74.6 69 5.6 7.51

4 207 218 11 5.31 70.8 67 3.8 5.37

5 173 198 25 14.45 72.9 69.5 3.4 4.66

6 183 174 9 4.91 66.4 64.5 1.9 2.86

7 161 157 4 2.48 70.8 70.5 0.3 0.42

8 177 177.5 0.5 0.28 61.4 61 0.4 0.65


For GSM



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



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 STARFISH PREDICTION FOR KNITTING MACHINE TYP E: 28 GAUGE, 30 INCH DIA., 2640 NEEDLES (28G 30”D 2640N)

Obs.No.

GSM

WIDTH

STARFI

SH

(Original

)

Predictio

n

Practica

l

(Factory

)

AE MA

E

APE MAP

E

STARFI

SH

(Original

)

Predictio

n

Practical

(Factory)

AE MA

E

APE MAP

E

1 179 180 1

3.6

0.59

2.76

70.9 69 1.9

2.06

2.68

2.86

2 151 146 5 3.31 71.3 68 3.3 4.63

3 145 144 1 0.69 75.1 73.5 1.6 2.13

4 121 120 1 0.82 74.7 72 2.7 3.61

5 119 129 10 8.4 63.8 63 0.8 1.25


For GSM



2.76%

For Width



2.86%



I J E N S

10. APPLYING CALIBRATION PRACTICALLY:

ACHIEVING 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 the

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 WORK

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


2.8% in case of width .

iii) While using 24G machines , MAPE for STARFISH


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 WORDS

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

shrinkage after dyeing and finishing of a given quality of

knitted fabric before it has ever been manufactured. The result

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 cotton-

knitted 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.

ACKNOWLEDGMENTS

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.

REFERENCES [1] STARFISH Manual and the software (version 5.03)-Cotton Technology

International (CTI); U.K., 1992 [2] The STARFISH Approach to High Quality Cotton Knitgoods (User-

manual Version 88:1)-International Institute for Cotton (IIC); U.K.,

1988 [3] The Production of High Quality Cotton Knitgoods-UNIDO Seminar

Report; U.K., 1984

[4] Brackenbury,T.: Knitted Clothing Technology -1st Edition,Blackwell

Science Limited;U.K.,1992 [5] Spencer,D.J.: Knitting Technology -3

rd Edition, Woodhead Publishing

Limited; U.K., 2001

[6] Eppen,G.D,Gould,F.J,Schmidt,C.P.,Moore,J.F.,Weatherford:Introductory Management Science-5

th Edition,Decisioneering Inc.;U.S.,1998

[7] Montgomery, Douglas C: Design and Analysis of Experiments-5th

Edition, John Wiley and Sons, Inc., U.S., 2001.

[8] Mackridakis,S,Wheel Wright,S.C.,Mcgee,V.E.:Forecasting :Methods and Application -2

nd Edition, J ohn Wiley and Sons, Inc.,U.S

customization of starfish technology in the production of cotton-knit fabrics

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