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A

Dissertation

on

OPTIMIZATION OF INTERNAL STRESSES IN POLYMER

SHEET EXTRUSION USING TAGUCHI TECHNIQUE by

GEO RAJU

(2012PMM5114)

Supervisor

Dr. M. L. MEENA

Assistant Professor

Submitted in partial fulfilment of the requirements for the award of degree of

MASTER OF TECHNOLOGY

IN

MANUFACTURING SYSTEM ENGINEERING

DEPARTMENT OF MECHANICAL ENGINEERING

MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY JAIPUR

JUNE 2014



i

MALAVIYA NATIONAL INSTITUTE OF

TECHNOLOGY JAIPUR

DEPARTMENT OF MECHANICAL ENGINEERINGJawahar Lal Nehru Marg, Jaipur-302017(Rajasthan)

CERTIFICATE

This is to certify that the dissertation on the topic “OPTIMIZATION OF

INTERNAL STRESSES IN POLYMER SHEET EXTRUSION USING

TAGUCHI TECHNIQUE” prepared by GEO RAJU (ID 2012PMM5114) in

partial fulfilment of the requirements for the award of degree of Master of

Technology in Manufacturing System Engineering of Malaviya National

Institute of Technology Jaipur is a bonafide compilation of the candidate’s work based on published literature on the topic.

Dr. M. L. MEENA

Place: Jaipur Assistant Professor

Date: 27 June 2014 Department of Mechanical Engineering

MNIT Jaipur



ii

MALAVIYA NATIONAL INSTITUTE OF

TECHNOLOGY JAIPUR

DEPARTMENT OF MECHANICAL ENGINEERINGJawahar Lal Nehru Marg, Jaipur-302017(Rajasthan)

CANDIDATE’S DECLARATION

I hereby certify that work which is being presented in the dissertation

entitled “OPTIMIZATION OF INTERNAL STRESSES IN POLYMER

SHEET EXTRUSION USING TAGUCHI TECHNIQUE” in the partial

fulfilment of requirements for award of the degree of Master of technology (M.

Tech.) and submitted in Department of Mechanical Engineering of Malaviya

National Institute of Technology Jaipur is an authentic record of my own work

carried out by me during a period from July 2013 to June 2014 under the

supervision of Dr. M. L. Meena, Assistant Professor, Department of Mechanical

Engineering, Malaviya National Institute of Technology Jaipur.

The matter presented in this dissertation embodies the result of my own

work and studies carried out and have not been submitted anywhere else. I further

certify that this is not a plagiarized work and the originality report generated from

the institute licensed ‘turnitin.com’ shows only 37% similarity index.

Place: Jaipur Geo Raju

Date: 27 June 2014 ( 2012PMM5114 )



iii

ACKNOWLEDGEMENT

It gives me great pleasure in conveying my heartiest thanks and profound

gratitude to my dissertation supervisor, Dr. M. L. Meena, Assistant Professor,

Department of Mechanical Engineering, Malaviya National Institute of Technology

Jaipur for providing me with the guidance, advice and support at each and every step in

the completion of this work.

I would like to thank Dr. Rakesh Jain, Professor and Head of Department,


Jaipur for keeping healthy research environment within the department.

I express my sincere gratitude to Dr. G. S. Dangayach, Professor and DPGC

convener, Department of Mechanical Engineering, Malaviya National Institute of

Technology Jaipur for his useful and constructive recommendations on this dissertation.

I express my sincere gratitude to Dr. A. Bhardwaj, Professor and PG coordinator,


Jaipur for his valuable advices during the dissertation work.

I am also thankful to Dr. G. Agarwal, Professor; Dr. A. P. S. Rathore, Professor

and Dr. M. L. Mittal, Associate Professor, Department of Mechanical Engineering,Malaviya National Institute of Technology Jaipur for providing means and support to

pursue this dissertation work.

I would like to express my sincere thanks to Mr. C Padmakumar, Executive

Director, Terumo Penpol Ltd., Thiruvananthapuram, Kerala for their permission to

undertake the project in their industry. I am also thankful to Mr. P. A. Balachandran,

Production Manager, Termo Penpol Ltd., Thiruvananthapuram, Kerala for his valuable

help and guidance in completing my work in the industry. I would like to extend my

thanks to the staff and technicians of the processing department of Terumo Penpol Ltd.,

Thiruvananthapuram, Kerala for assisting me in the data collection and experimentation

work in the plant. I would like to express my gratitude towards Mr. Rahul, Senior

Executive-HR, Terumo Penpol, Thiruvananthapuram, Kerala for helping me in

completing all formalities needed to undertake the project in their company.



iv

I would like to give many thanks to my friends, especially Sujith S, Koushik

G.P. and Sarun Raj, for their help and support in different stages of this dissertation. I

express my sincere thanks to my colleagues and research scholars for their helpful

suggestions and encouragement.

Finally I would like to express my deepest gratitude to my mother, Rosamma

Raju, my sister, Ria Raju and my brother, Alex Raju for being a constant source of love,

understanding and support for me. I am indebted to my father, Raju George, for his care

and love. He worked industriously to support the family and spared no effort to provide

the best possible environment for me to grow up and attend school. Although he is no

longer with us, he is forever remembered. I dedicate this dissertation to him.

Again, this dissertation would have been simply impossible without all of those

who love and care about me.

Geo Raju

(2012PMM5114)



v

ABSTRACT

In polymer manufacturing processes such as extrusion, injection molding,

calendering, vacuum forming, machining etc., thermal and mechanical stresses are

induced to the material which may affect the next in-house process such as

thermoforming, sterilization etc. and could also affect the quality of the finished product.

The finished product may appear acceptable but once it is exposed to severe

environmental conditions or other thermal history any stresses that was processed into the

product becomes evident which may lead to customer dissatisfaction. Extrusion is a

primary polymer processing technique. Extrusion of polymers induces internal stresses to

the material extruded which lowers the impact strength, diminishes the high temperature

performance and could cause environmental stress cracking. Therefore it is greatnecessary to make sure that all precautions are taken to alleviate the unwanted stresses.

The scope of this project is to optimize the internal stresses induced in polymer sheet

during extrusion using Taguchi technique.

The Taguchi design of experiments (DOE) is one of the most effective techniques

to solve industrial problems which involve controlling variation in a process through

design of experiment. Responses are the quality characteristic of the product/process.

Signals and noises are parameters which affects the responses.

This project was undertaken in a blood bag manufacturing company. The

experimentation was carried out in a single screw extruder sheetline used for extruding

flexible polyvinylchloride (FPVC) sheets which is used for making blood bags. In this

experimental work, five factors are considered. These are draw ratio, melt temperature,

die temperature, middle roll temperature and bottom roll temperature. For the

optimisation of sheet extrusion, two levels of factors are used. Minitab 17 software is

used to design the experiments. An L8 orthogonal array which can handle two-levels of

process parameters is used. The stresses induced in polymer sheet are a characteristic of

shrinkage of sheet. So shrinkage is taken as the response value.

The Taguchi analysis and analysis of variance (ANOVA) are carried out with help

of Minitab software. The response table and main effects plot for shrinkage give the

optimal values of process parameters to minimize the internal stresses in polymer sheet



vi

extrusion. The percentage contribution of each parameter is calculated from the ANOVA

table which gives the significance of each parameter. Draw ratio is found to be the most

significant factor affecting internal stresses induced in polymer sheet during extrusion

followed by middle roll temperature, melt temperature and bottom roll temperature. The

influence of die temperature is found to be negligible (0.001%).

The confirmation experiment is conducted with optimum combination of the

factors and their levels determined from the Taguchi analysis. The average shrinkage

obtained from the conformation test is in good agreement with the minimum shrinkage

obtained while conducting the experiment as per L8 orthogonal array; this confirms that

the results have excellent reproducibility.



vii

CONTENTS

CERTIFICATE i

CANDIDATE’S DECLARATION ii

ACKNOWLEDGEMENT iii

ABSTRACT v

CONTENTS vii

LIST OF FIGURES xi

LIST OF TABLES xii

LIST OF ABBREVIATIONS xiii

NOMENCLATURE xvi

1.

INTRODUCTION 1 – 10

1.1

EXTRUSION PROCESS 2

1.2 POLYMER SHEET EXTRUSION 4

1.3 SINGLE SCREW SHEET EXTRUDER 4

1.4 SCOPE OF THE PROJECT 9

1.5 OBJECTIVES OF THE DISSERTATION 9

1.6 OUTLINE OF THE DISSERTATION 10

2.

LITERATURE REVIEW 11 - 21

2.1

INTRODUCTION 11



viii

2.2

OPTIMIZATION METHODS 12

2.2.1

Taguchi Method 12

2.2.2 Artificial Neural Networks 13

2.2.3 Fuzzy Logic 15

2.2.4

Response Surface Methodology 16

2.2.5

Genetic Algorithm 17

2.2.6

Non Linear Modeling 18

2.3

INTERNAL STRESSES IN PLASTIC SHEET EXTRUSION 19

2.3.1 Factors Influencing Internal Stresses in Polymer Sheet

Extrusion

20

2.4 LITERATURE REVIEW SUMMARY 20

3. COMPANY PROFILE 22 – 30

3.1 ABOUT TERUMO PENPOL LIMITED 22

3.2

PRODUCT PROFILE 23

3.3 MANUFACTURE OF BLOOD BAGS 26

3.3.1 Raw Materials 26

3.3.2 Manufacturing Process 26

3.3.3 Specifications of Blood Bags 29

4. EXPERIMENTATION METHODOLOGIES 31 – 49

4.1 DESIGN OF EXPERIMENTS 31

4.2

TAGUCHI METHOD 35



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4.3

ROBUST DESIGN 38

4.4

OVERVIEW OF TAGUCHI METHOD 43

4.5 ANALYSIS OF VARIANCE (ANOVA) 45

4.6 MINITAB 48

4.6.1

Taguchi Experimental Analysis in Minitab 48

4.6.2

Two-way ANOVA in Minitab 49

5.

EXPERIMENTAL WORK 50 – 57

5.1

SELECTION OF PROCESS PARAMETERS AND THEIR LEVELS 50

5.1.1

Responsible Parameters for Stress Induced 50

5.1 POLYMER MATERIAL 52

5.2 EXTRUDER 53

5.3

SHEET DIE 54

5.4

EXPERIMENTAL DESIGN 54

5.5

EXPERIMENTAL PROCEDURE 54

6.

RESULTS AND DISCUSSION 58 – 63

6.1

SIGNAL-TO-NOISE RATIO AND MEAN 58

6.1.1 Calculation of Mean of S/N Ratios 60

6.1.2 Calculation of Mean of Means 61

6.2

ANALYSIS OF VARIANCE (ANOVA) 62

6.3

CONFIRMATION EXPERIMENT 63

7.

CONCLUSION 64 – 65



x

7.1

CONCLUSION 64

7.2

FUTURE SCOPE 65

REFERENCES 66 – 70



xi

LIST OF FIGURES

Figure

No.

Title Page

No.

Figure 1.1 Schematic Diagram of Single Screw Extruder 2

Figure 1.2 Diagram of Sheet Extrusion Line 4

Figure 1.3 Screw Geometry and Functional Zones 6

Figure 1.4 Standard Coat Hanger-Type Die 7

Figure 2.1 Cause and Effect Diagram of Internal Stresses in Polymer Sheet 20

Figure 3.1 Types of Blood Bags Manufactured by Terumo Penpol 24

Figure 3.2 Flow Chart of Manufacturing Process 28

Figure 4.1 Design Process Suggested by Taguchi 38

Figure 4.2 Parameter Diagram of a Product/Process/System 39

Figure 4.3 Quality Loss Function 41

Figure 4.4 L9 (34) Orthogonal Array 43

Figure 4.5 Flowchart of Taguchi Method 44

Figure 6.1 Main Effects Plot for Signal-to-Noise Ratio 60

Figure 6.2 Main Effects Plot for Means 61



xii

LIST OF TABLES

Table

No.

TitlePage

No.

Table 3-1 Product Range of Terumo Penpol 23

Table 3-2 Raw Material used for Manufacturing Blood Bag Components 26

Table 4-1 Independent Variables for Plastic Processes 33

Table 5-1 Control Variables and Their Levels 51

Table 5-2 Specifications of Sheet Grade PVC Pallet used for Sheet Extrusion 52

Table 5-3 Specifications of the Extruder Machine 53

Table 5-4 Specifications of Sheet Die 54

Table 5-5 L8 Orthogonal Array 55

Table 5-6 List of Experiments 55

Table 5-7 Experimental Result for Shrinkage 57

Table 6-1 Signal-to-Noise Ratios and Means for Shrinkage 59

Table 6-2 Response Table for Signal-to-Noise Ratios 60

Table 6-3 Response Table for Means 61

Table 6-4 Analysis of Variance (ANOVA) for Shrinkage 62

Table 6-5 Model Summary 62

Table 6-6 Optimum Values of Extrusion Parameters 63

Table 6-7 Result of Confirmation Experiment 63



xiii

LIST OF ABBREVIATIONS

AC : Alternating Current

Adj MS : Adjusted Mean Squares

Adj SS : Adjusted Sum of Squares

AI : Artificial Intelligence

ANN : Artificial Neural Networks

ANOVA : Analysis of Variance

ASTM : American Society for Testing and Materials

BOV : Break-off Valve

CFD : Computational Fluid Dynamics

CR : Compression Ratio

DEHP : Di-(2 ethyl hexyl) phthalate

DF : Degree of Freedom

DOE : Design of Experiments

e.g. : Exempli Gratia (Latin: For example)

ect. : Et cetera (Latin: and the others)

EDI : Extrusion Dies Incorporated

FEM : Finite Element Method

FPVC : Flexible Polyvinyl Chloride

FUNGTA : Fuzzy Neural-Taguchi Network with Genetic Algorithm

GA : Genetic Algorithm

HDPE : High Density Polyethylene

IAS : Indian Administrative Service

Inc : Incorporation

ISO : International Organization for Standardization

L/D : Length/Diameter

LDPE : Low Density Polyethylene

Ltd : Limited

Max : Maximum

Min : Minimum



xiv

NRC : National Research Council

OA : Orthogonal Array

PC : Polycarbonate

P-Diagram : Parameter Diagram

PP : Polypropylene

Pty. : Proprietary Company

PVC : Polyvinyl Chloride

req. : Requirement

RSM : Response Surface Methodology

R-sq : R-squared

R-sq (adj) : R-squared (adjusted)

R-sq (pred) : R-squared (predicted)

SARL : Société à Résponsabilté Limitée (French: Limited Liability Company)

SNR : Signal-to-Noise Ratio

SS : Sum of Squares

TPL : Terumo Penpol Limited

USP : United States Pharmacopeia

UV : Ultra Violet

VCM : Vinyl Chloride Monomer

XLPE : Cross-linked Polyethylene

Units of Measurement

oC : Degrees Celcius

oF : Degrees Fahrenheit

cm : Centimeter

mm : Millimetre

nm : Nanometer

kg : Kilogram

g : Gram

mg : Milligram

µg : Microgram

lbs : Pounds



xv

h : Hour

min. : Minute

L : Litre

mL : Millilitre

cc : Cubic centimeter

kW : Kilowatt

V : Volt

Hz : Hertz

rpm : Revolutions per minute

N : Newton



xvi

NOMENCLATURE

L : Loss in dollars(money)

y : Measured value of critical product characteristic

m : Target value of critical product characteristic

k : Constant

A0 : Loss due to defective product

Δ0 : Allowed deviation from the target

SNR s : Signal-to-noise ratio for smaller the better

SNR N : Signal-to-noise ratio for nominal the best

SNR L : Signal-to-noise ratio for larger the better

yi : Measured quality characteristics for the ith repetition

n : Number of repetitions in trial

: Mean of measured quality characteristics

S : Standard deviation of measured quality characteristics

A, B, C, … : Control factors

e : Error

SS i : Sum of squares of each factor

SS e : Sum of squares of error

SS T : Total variation

n j : Signal-to-noise ratio of individual experiments

m : Total number of experiments

CF : Correction factor

DF i : Degree of freedom of each factor

V i : Variance of each factor

V e : Variance of error

F i : F-ratio of each factor

SS’ i : Expected sum of squares

P i : P-value of each factor

w : Base length of right triangle

h : Height of right triangle



xvii

a,b,c : Length of three sides of acute triangle

Aa : Area of acute triangle

Ar : Area of right triangle

At : Area of shrunken irregular sample

Ai : Area of pre-shrunken sample

%S : Percentage shrinkage

% : Percentage

ɸ : Phase



1

CHAPTER 1

INTRODUCTION

The twentieth century remains witness to the invention of one of the most

versatile commodity material in the history of material science i.e. man-made polymers

also known as plastics. Plastics play a significant role in our day-to-day life. The ease

with which plastics can be processed makes it a very versatile material. The processing

stages of heating, shaping and cooling may be continuous (production of pipe by

extrusion) or a repeated cycle of events (production of a telephone housing by injection

moulding) but in most cases the processes may be automated and so are particularly

suitable for mass production. Plastics can be processed using a wide range of methods. Inmost cases, the shape of the component and type of plastic, whether it is thermoplastic or

thermosetting, decides which method to be used for the processing of plastics.

Plastics extrusion is a high volume manufacturing process in which

raw plastic material is melted by external heat / frictional heat and conveyed forward by a

screw to the opening of the die, which gives the shape of the required product. Extrusion

process is a continuous process by which many products such as pipe/tubing, weather

stripping, fence, deck railing, window frames, plastic films and sheet, thermoplastic

coatings, and wire insulation can be manufactured. Extrusion of plastics, like injection

molding, is a relatively simple concept, but the design and application of extruders is a

complex field. Extruders are widely used in the commercial polymer processing industry.

Modern extruders consist essentially of a hollow barrel, which is kept under a set

temperature, inside which one or more Archimedes-type screw(s) rotate(s) at controllable

constant speed. The geometry of these machines can vary widely, from single screw

extruders with a screw having a constant square pitch, to multi-screw machines of

intricate design. The most commonly used extruder in the polymer processing industry is

the single screw extruder. Intermeshing twin screw extruders are also quite frequently

used. Counter-rotating twin screw extruders are mostly used for the extrusion of

polyvinyl chlorides (PVC) and co-rotating ones are used for compounding operations.

Figure 1.1 shows the schematic diagram of a single screw extruder.



2

The performance of a given extruder will depend on a number of factors,

including the polymer properties (thermal, physical and rheological), the operating

conditions (screw speed and barrel temperature profile) and machine and die geometries

(Agassant et al., 1996; Rauwendaal, 1986; Stevens and Covas, 1995; Tadmor and Klein,

1970; White, 1990). The setting of optimum operating conditions is necessary for

yielding the best process performance for the manufacture of a certain product.

Traditionally, this is solved based on empirical knowledge, coupled to a trial-and-error

procedure, where tentative extrusion experiments are performed until desirable

performance is obtained. There are typically many parameters that need to be regulated in

the extrusion of polymers. Dimension of the extrudate could be one of the controlled

quality variable used in industry (Previdi et al., 2006; Wellstead, 1998). Viscosity of

polymers in the upstream of the die could be also used as a quality variable. (Liu et al.,

2012; McAfee, 2007; Pabedinskas and Cluett, 1994)

Figure 1.1 Schematic Diagram of Single Screw Extruder

1.1 EXTRUSION PROCESS

In the extrusion of plastics, raw material in the form of pellets or granules is

gravity fed from a hopper which mounted on top of the rear end of the barrel and comes

in contact with the screw. The extrusion process is similar to plastic injection moulding

from the point of the extruder technology but differs in that extrusion process is usually a

continuous process.



3

The rotating screw conveys the plastic granules along the barrel which is heated to

the desired melt temperature. The barrel may consist of three or more independent heater

zones. The heating profile of the barrel is set such that the temperature of the barrel

gradually increases from the rear (where the granules enter the barrel) to the front. The

risk of degradation in plastics because of overheating is lowered by the gradual melting of

the plastic beads as they move along the barrel.

The friction and intense pressure generated inside the barrel produces additional

heat. The melt temperature can be attained without the help of the barrel heaters if the

screw is rotated at a high rpm. The shear produced due to the movement of plastic beads

through the small gap between the barrel wall and the rotating screw could produce

enough temperature to melt the plastics without the help of barrel heaters. Cooling fans

are provided in most extruders to restrict the temperature to a certain point. In certain

cases where air cooling proves to be insufficient, water jackets are provided for absorbing

the extra heat produced so that the temperature could be kept below a set value.

A screen pack is provided at the end of the barrel to remove any contaminants in

the melt as it leave the barrel. The performance of the extrudate can be enhanced by the

fine filtration of the melt. The screens are reinforced with breaker plate to with stand the

high pressures. They also serve the purpose of creating back pressure in the barrel. Back

pressure aids in the proper mixing and uniform melting of the polymer. The melt enters

the die after passing through the screen. As the melt passes through the die, the molten plastics get its final profile. The die must be designed so that the molten plastic evenly

flows from a cylindrical profile, to the product's profile shape. Unwanted stresses may be

induced in the product if the flow the melt inside the die is uneven. Upon cooling, these

induced stresses prove to be a burden as it causes warping. Extrusion process can be used

to produce almost any shape of continuous profile.

After passing through the die, the extrudate is conveyed through a water bath to

get it cooled. As plastics are very good thermal insulators, it is very difficult to cool

plastics quickly. Plastics dissipate heat 2000 times more slowly than that of steel. For

plastic sheet extrusion, a set of cooling rolls are provided and the sheets are cooled by

pulling them through these cooling rolls. In tube or pipe extrusion, the newly formed

molten tube or pipe is kept from collapsing with the help of a sealed water bath which is

acted upon by a carefully controlled vacuum.



4

1.2 POLYMER SHEET EXTRUSION

Film and Sheet are produced by the continuous extrusion of melt plastics through

a horizontal die. According to the definition given by American Society for Testing and

Materials (ASTM), if the thickness of the extruded product is more than 0.25 mm, it is

called sheet and if its thickness is equal to or less than 0.25 mm, the extruded product is

called film.

The molten polymer is spread to predetermined width and uniform thickness using

a sheet die. The die must be designed such that, over the entire width of the die, the exit

velocity distribution of the plastics is uniform. The die design is important so as to obtain

a product of uniform thickness. T-type die, fishtail die and coat-hanger die are the most

commonly used types of die used for sheet extrusion (Bouvier and Campanella, 2014).

The plastic from the die is then placed on a three roll stack. The sheet form passesthrough the bottom and center roll, wraps around back of the center roll and then back

around the top roll. In order to take the heat out of the sheet, these rolls are water cooled.

The plastic then moves down the conveyor to the end of the sheetline. The saw at the end

of the sheetline cuts the sheet to the desired width. The desired surface finish and stress

relieve of the sheet are attained by the various operations employed along the length o

the conveyor.

1.3 SINGLE SCREW SHEET EXTRUDER

Figure 1.2 illustrates the typical layout of a sheet extrusion line.

Figure 1.2 Diagram of Sheet Extrusion Line



5

(a)

The barrel and screw

The root diameter of an extruder screw increases gradually from rear to front. It is

a long steel shaft wrapped with helical flights of constant pitch. The compression ratio

(CR) and length/diameter (L/D) ratio of a typical screw extruder is 3:1 and 24:1

respectively. There are single-stage and two-stage extruder screw. The requirements of

mixing and production output decide which extruder screw to be used. The extruder

screw is housed in a hollow cylinder, viz. extruder barrel. The space between the screw

flights and the inner wall of the extruder barrel is very small. But the spacing given is

constant throughout the barrel.

(b)

The compression ratio

Compression ratio (CR) is the depth ratio of the first flight in the solid conveying

zone to that of the last flight in the metering zone. The CR of a typical extruder screw is

3:1. A higher CR leads to resin degradation and excessive shearing and a lower CR

results in poor intermixing of the melt and inadequate shear.

(c) The length/diameter ratio

The desired L/D for extruder screws is 24:1 or more. The L/D of a screw is 24:1

implies the length of the screw is 24 times the screw diameter. For proper melting and

mixing of the plastic pallets, adequate residence time should be provided which is made

ensured by these dimensions.

(d) The screw design

An extruder screw is basically divided into three different zones.

i.

Feed zone: The plastic pallets fed from hopper are preheated in this zone and

then they are conveyed to the subsequent zones. Sufficient material must be

supplied by the feed zone to the metering zone so that metering zone does

not get starved. But care must be taken also not to overrun the metering zone.

The characteristics of the feedstock, the geometrical features of the screw

and the frictional properties of the screw and barrel with respect to the

polymer material are the major factors that affect the design of the feed zone.



6

ii. Compression zone: The screw depth decreases gradually along the

compression zone. This gradual decrease in diameter will result in the

compaction of plastic. This compaction has mainly two functions. It enables

squeezing of any trapped air pockets back into the feed zone and improves

the heat transfer.

iii. Metering zone: The screw depth is again constant in this zone but less than

the feed zone. In the metering zone, the melt is homogenised (uniform

temperature and pressure) and is supplied at a constant rate to the die.

Figure 1.3 presents the screw geometry and its functional zones.

Figure 1.3 Screw Geometry and Functional Zones

(e) Head zone

The head zone assembly is stationed between the discharge periphery of

the barrel and the die ingress. The head zone assembly usually has one or a

concoction of some of the following:

A gear pump is a rotary-gear implement. Its function is to generate the

required operating pressure and to maintain a constant flow of the polymer. It

helps in regulating the pressure build up in the extruder.

A static mixer that aids the uniform mixing of the molten polymer. It also

helps the melt to attain uniform temperature.



7

A breaker plate reinforces the screen pack which is delicate. A breaker plate

with higher gauge may promote mixing and also increases the back pressure.

It may cause polymer degradation as the breaker plate raise the melt

temperature and also lowers extrusion output.

(f)

Sheet die

The role of the sheet die is to span out the molten polymer to a set width and

constant thickness. A stable die is required for producing polymer sheets of uniform

thickness. Usually sheet die has a standard, coat hanger-type, manifold design. A sheet

die is provided with flexible upper lip and fixed lower lip to regulate final thickness of the

sheet. Figure 1.4 presents a standard coat hanger type die

Figure 1.4 Standard Coat Hanger-Type Die

The following data are required by a die manufacturer for designing a sheet die properly.

Rheological data such as viscosity vs shear rate of the polymer.

Thickness range of final product.

Sheet width.

Throughput rate.

i.

Manifold section: The function of primary manifold is to disperse the molten

polymer at a constant pressure and even flow rate from the middle of the die

to its ends. Teardrop or half-teardrop shape of the manifold cross-section

enables gentle passage of the melt from the manifold section to the pre-land

area. The cross-sectional area of the manifold section decreases in a linear

manner from the middle of the die to its end. This design make sure that

polymer residence time is minimal in the die and hence reduces the chances

of polymer degradation.



8

ii.

Pre-land section: This section distributes the molten plastics from the center

of the die to its outer peripheries.

iii. Secondary manifold: As the melt exits the die, any stresses that are not

allayed in the pre-land will be removed in this section.

iv.

Lip-land section: The function of lip-land section is to fine tune the pressurefor a specific opening range in order to regulate the final sheet thickness.

v. Deckles: Deckles are metal pieces placed either externally or internally at the

extremities of the die lip. The purpose of providing deckles is to produce a

sheet which is narrower than the width of the die. Deckles may cause

stagnation of resin which will result in the degradation of the polymer.

(g)

Polishing roll stack

This system consists of three rolls which are plated with chromium. Cooling

passages are provided in these rolls which help in maximizing the heat transfer and

reducing the side-to-side temperature difference. A Cooling fluid is circulated through the

roll to cool the sheet and it provides a smooth finish to the sheet. Apart from the cooling

fluid, each roll is provided with its own temperature control unit and pump. To get the

desired sheet thickness, roll gaps must be precisely set.

(h)

Cooling conveyor

Cooling conveyor allows sheet to lie flat and the unforced ambient air helps in the

cooling process of the sheet. This helps in reducing the warping of the sheet which may

occur in the final stages of cooling. The cooling rate can be enhanced with the help of

blowers provided on the top or bottom or both sides of the cooling conveyor. The edges

of the sheet are trimmed and cut to the desired final width ahead of the pull rolls.

(i)

Pull roll

Pull rolls consist of two rolls with a rubber-covered surface. They provide good

traction. These rolls are driven at a slightly higher speed than that of finishing rolls with

the help of a variable-speed motor. This difference in speed keeps the sheet rigid and the

sheet keeps a firm contact with the polishing rolls. The speed of the rolls can be varied by

an operator to keep the tension to a minimum and equal on both side of the sheet. The

finished product is either wound on a roll or if heavy gage, sheared and stacked for later

use.



9

1.4 SCOPE OF THE PROJECT

Accordingly, this project aims to focus on the implementation of Taguchi design

of experiments (DOE) technique to reduce stresses induced in polymer sheet during

extrusion process and improve the quality of the product. The main benefits of the

Taguchi DOE technique are listed below:

Taguchi DOE technique is simple to understand by the engineers as well as

workers.

Taguchi DOE technique directly addresses an identified problem.

Taguchi DOE technique gives immediate results.

Taguchi DOE technique can be applied in wide range of industries.

The scope of the project is identified and codified as:

“To improve the process and optimize the stresses induced in the polymer sheet

during extrusion process using Taguchi DOE technique”

1.5

OBJECTIVES OF THE DISSERTATION

This project was undertaken in a blood bag manufacturing plant. The optimization

of the stresses induced in the polymer sheet during extrusion process using Taguchi DOEtechnique is selected as the main theme of this project.

The main objectives of the dissertation are given below:

Study and check the process. Identify and define the problem. Find out the

root causes of the problem.

Select the most significant parameters that influence the quality control

characteristic. Shrinkage is selected as the most representative quality control

characteristic in case of internal stresses induced in polymer sheet during

extrusion.

Conduct the experiment according to the Taguchi DOE technique and record

the response values. Select the appropriate experimental design or orthogonal

array (OA) with appropriate parameter levels.



10

Analyze the data using Minitab software to identify the significant input

parameters. Determine the optimum value of each parameter from the

response tables and graphs.

Validate the optimum values obtained by conducting a confirmation test.

1.6

OUTLINE OF THE DISSERTATION

This dissertation report is divided into seven chapters.

Chapter 1 emphasizes the background of the project work, giving the scope of the

project. A brief account of extrusion process, polymer sheet extrusion and single screw

sheet extruder are also given this chapter. Also the objective and outline of the

dissertation is given in brief.

Chapter 2 presents the literature review of various techniques that researchers had

adopted to optimize the plastic extrusion process. The problems created by internal

stresses in polymer sheet extrusion and its causes are also discussed in this chapter.

Chapter 3 gives a brief account of the company, the manufacturing process and

about the products.

Chapter 4 describes the experimentation methodologies-DOE, Taguchi method,

and robust design. A brief description about analysis of variance (ANOVA) and Minitab

is also given in this chapter.

Chapter 5 describes the experimental work namely selection of parameters and

their levels, materials and equipment used for the experimentation work, experimental

design and procedure and the response values obtained from the experimental work.

Chapter 6 shows the results obtained with the help of Minitab software and

discusses about the contribution of each factor towards the response value.

Chapter 7 gives the conclusion and future scope of this work.



11

CHAPTER 2

LITERATURE REVIEW

2.1

INTRODUCTION

Polymers are categorised into elastomers, thermosets and thermoplastics.

Thermosets and most of the elastomers undergo irreversible chemical change during the

forming process whereas a thermoplastic may be reheated and reformed after it has been

cooled. An irreversible crosslinking of molecules take place in thermosets when heated

above a certain temperature at a given pressure. When heated above a certain temperature

at a given pressure, most elastomers, e.g. rubber compounds, will vulcanize.

Vulcanization is the process of crosslinking in elastomers which occurs above a certain

temperature. It is important to keep the temperature low so that no crosslinking of the

specific polymer or polymer compound occur during the mixing and preforming of

elastomers and during the extrusion and molding of thermosets.

The control of process variables need is necessary to maintain high product

quality during polymer mixing, extrusion, blow molding and injection molding. The

control of process parameters such as melt temperature, extruder barrel pressure and mold

cavity pressure are important in the case of blow molding of thermosets and elastomers to

ensure that the vulcanization or crosslinking does not occur until the appropriate time

during the final forming process (Steward, 1999). For the extrusion process of any

polymer, there are typically many variables that need to be regulated.

Plastic extrusion has been a challenging process for many manufacturers and

researchers to produce products meeting requirements at the lowest cost. The complexity

of extrusion process and the enormous amount of process parameters involved make it

difficult to keep the process under control. The complexity and parameter manipulation

may cause serious quality problems and high manufacturing costs. One of the main goals

of extrusion is the improvement of quality of extruded parts besides the reduction of cycle

time, and lower production cost. Solving problems related to quality has a direct effect on

the expected profit for companies manufacturing plastic products. The dimensional

characteristics and attributes and the mechanical properties of the extrudate are the



12

commonly used quality characteristics in extrusion process. Material related defects,

process related problems, packing and cooling related defects, and post extrusion related

defects are some of the major sources of quality issues in extrusion process. Factors that

influence the quality characteristics of an extrudate can be categorized into four: part

design, die design, machine capability and processing parameters. The part and die design

are considered as non-changeable as they are installed and fixed. But changes in process

parameters due to machine wear, operator fatigue and environmental change can result in

the deviation of the quality characteristics of the extruded product.

2.2 OPTIMIZATION METHODS

Determining optimal process parameter settings critically influences productivity,

quality, and cost of production in the plastic related industries. Previously, production

engineers used either trial-and-error method or Taguchi’s parameter design method to

determine optimal process parameter setting for plastic extrusion. However, these

methods are unsuitable in the present scenario because of the increasing complexity of

product design and the requirement of multi-response quality characteristics. In

manufacturing industries, process parameters need to be optimized routinely in order to

improve the productivity of the firm or the quality of the product. The trial-and-error

method is no longer suitable to determine the process parameters for plastic extrusion as

the trial-and-error approach is time-consuming and consumes much more resources.

Various optimization methods have been adopted by researchers in the determination of

the optimal process parameters for plastic extrusion with a view to minimize the time to

market and to produce extruded parts of consistent quality. Research based on various

approaches, including Taguchi technique, artificial neural networks (ANN), fuzzy logic,

genetic algorithms (GA), non-linear programming and response surface methodology

(RSM) are discussed.

2.2.1 Taguchi Method

Dr. Genichi Taguchi has developed a method based on a set of well-balanced

experiments which gives much reduced variance for the experiment with optimum

settings of control parameters. Thus the collaboration of DOE with optimization of

control parameters to obtain best results is achieved in the Taguchi Method. OA provide a

set of well balanced (minimum) experiments and Taguchi's signal-to-noise ratios (SNRs),

which are log functions of desired output, serve as objective functions for optimization,



13

help in data analysis and prediction of optimum results. Taguchi’s two most important

contributions to quality engineering are the use of Gauss’s quadratic loss function to

quantify quality and the development of robust designs (parameter and tolerance design).

Taguchi’s robust designs have widespread applications upstream in manufacturing to fine

tune a process in such a manner that the output is insensitive to noise factors.

Narasimha and Rejikuar (2013) presented a systematic approach to find the root

causes for the occurrence of defects and wastes in plastic extrusion process. The cause-

and-effect diagram was implemented to identify the root causes of these defects. The

extrusion process parameters such as vacuum pressure, temperature, take-off speed, screw

speed of the extrusion process and raw material properties were identified as the major

root causes of the defects from the cause-and-effect diagram. The quality loss for the

current performance variation was calculated using Taguchi’s principle of loss function

and requirement for improvement was verified. In this paper DOE was applied to

optimize the process parameters for the extrusion of HDPE pipe Ø 50mm and plain pipe

∅ 25mm. Four independent process parameters viz. vacuum pressure, take-off speed,

screw speed and temperature were investigated using Taguchi method. Minitab 15

software was used to analyze the result of the experiment. Based on the result of the

analysis, optimum process parameters were selected.

2.2.2 Artificial Neural Networks

An ANN is an information processing paradigm that is inspired by the way

biological nervous systems, such as the brain, process information. The novel structure of

the information processing system is the key element of this paradigm. It comprises of a

large number of highly interconnected processing elements (neurons). These processing

elements work in unison to solve specific problems. ANNs, like people, learn by

example. An ANN is configured for a specific application, such as pattern recognition or

data classification, through a learning process. Learning in biological systems involves

adjustments to the synaptic connections that exist between the neurons. This is true ofANNs as well.

Neural networks has the ability to derive meaning from complicated or imprecise

data which can be used to extract patterns and detect trends that are too complex to be

noticed by either humans or other computer techniques. A trained neural network can be

thought of as an "expert" in the category of information it has been given to analyze. This



14

expert can then be used to provide projections given new situations of interest and answer

"what if" questions. Neural networks take a different approach to problem solving than

that of conventional computers. Conventional computers use an algorithmic approach i.e.

the computer follows a set of instructions in order to solve a problem. Neural networks

learn by example. They cannot be programmed to perform a specific task.

Advantages of ANN are listed below:

i. Adaptive learning : An ability to learn how to do tasks based on the data given

for training or initial experience.

ii. Self-organization: An ANN can create its own organization or representation

of the information it receives during learning time.

iii. Real time operation: ANN computations may be carried out in parallel, and

special hardware devices are being designed and manufactured which take

advantage of this capability.

iv.

Fault tolerance via redundant information coding : Partial destruction of a

network leads to the corresponding degradation of performance. However,

some network capabilities may be retained even with major network damage.

Huang and Liao (2002) investigated the diameter and thickness swells of the

parison in the continuous extrusion blow molding of HDPE, as a function of the

processing parameters, including the die temperature and flow rate. A back-propagationneural network model was used to predict the parison swells under the effect of sag. A 2-

20-20 neural network architecture with two input nodes, one hidden layer with 20 nodes,

and 20 out-put nodes was utilized. Twenty-eight data sets obtained from experiments

were provided to the neural network as samples, which were divided into 20 sets of

training data and eight sets of testing data. The comparison of the experimentally

determined parison swells with the predicted ones using the trained neural network model

showed very good agreement between the two.

Cirak and Kozan (2009) presented knowledge based and neural network

approaches to wire coating for polymer extrusıon. The dependency of extrusion

process parameters viz. barrel heating zones’ temperatures and screw speed on coating

thickness of wire coating extrusion processes was investigated using ANN. A back-

propagation neural network model was used to predict the coating thickness.



15

Al Rozuq and Al Robaidi (2013) presented an experimental study to investigate

the dependency of extrusion parameter on the coating thickness and degree of

crosslinking of crosslinked polyethylene (XLPE) cable. A three layer back propagation

ANN model was used for the description of wire coating thickness.

2.2.3 Fuzzy Logic

Fuzzy logic (Zadeh, 1965; 1996; 1997) is an analysis method purposefully

developed to incorporate uncertainty into a decision model. Fuzzy logic allows for

including imperfect information no matter the cause. In essence fuzzy logic allows for

considering reasoning that is approximate rather than precise. There are key benefits to

applying fuzzy tools. Fuzzy tools provide a simplified platform where the development

and analysis of models require reduced development time than other approaches. As a

result, fuzzy tools are easy to implement and modify. Nevertheless, despite their “user-

friendly” outlet, fuzzy tools have shown to perform just as or better than other soft

approaches to decision making under uncertainties.

The past few years have witnessed a rapid growth in the number and variety of

applications of fuzzy logic. Fuzzy logic poses the ability to mimic the human mind to

effectively employ modes of reasoning that are approximate rather than exact. In

traditional hard computing, decisions or actions are based on precision, certainty, and

vigour. Precision and certainty carry a cost. In soft computing, tolerance and impressionare explored in decision making. The exploration of the tolerance for imprecision and

uncertainty underlies the remarkable human ability to understand distorted speech,

decipher sloppy handwriting, comprehend nuances of natural language, summarize text,

and recognize and classify images.

With fuzzy logic, mapping rules can be specified in terms of words rather than

numbers. Computing with the words explores imprecision and tolerance. Another basic

concept in fuzzy logic is the fuzzy if–then rule. Although rule-based systems have a long

history of use in artificial intelligence (AI), what is missing in such systems is machinery

for dealing with fuzzy consequents or fuzzy antecedents. In most applications, a fuzzy

logic solution is a translation of a human solution. Thirdly, fuzzy logic can model

nonlinear functions of arbitrary complexity to a desired degree of accuracy. Fuzzy logic is

a convenient way to map an input space to an output space. Fuzzy logic is one of the tools

used to model a multi-input, multi-output system.



16

Oke et al. (2006) optimized the flow rate of the plastic extrusion process in a

plastic recycling plant with the application of a neuro-fuzzy model. The input parameters

viz. effective frictional force between the surfaces of the material and the walls of the

extrusion chamber and diameter of the extrusion chamber determine the rate of flow of

the solid waste material to be recycled through the extrusion chamber. The model is

designed such that the most favourable condition where maximum quantity of solid waste

material is recycled is attained. The linguistic variable serves as the engine of the model

in bringing about relationship between the input and the output parameters to evaluate the

outcome of such relationship. The result obtained indicates the feasibility of applying the

neuro-fuzzy model in plastic recycling extruder process.

2.2.4 Response Surface Methodology

Response surface methodology (RSM) is a collection of mathematical and

statistical techniques for empirical model building. By careful design of experiments, the

objective is to optimize a response (output variable) which is influenced by several

independent variables (input variables). An experiment is a series of tests, called runs, in

which changes are made in the input variables in order to identify the reasons for changes

in the output response.

Originally, RSM was developed to model experimental responses (Box and

Draper, 1987), and then migrated into the modelling of numerical experiments. Thedifference is in the type of error generated by the response. In physical experiments,

inaccuracy can be due, for example, to measurement errors while, in computer

experiments, numerical noise is a result of incomplete convergence of iterative processes,

round-off errors or the discrete representation of continuous physical phenomena (Giunta

et al., 1996; van Campen et al., 1990, Toropov et al., 1996). In RSM, the errors are

assumed to be random.

The application of RSM to design optimization is aimed at reducing the cost of

expensive analysis methods (e.g. finite element method (FEM) or computational fluid

dynamics (CFD) analysis) and their associated numerical noise. The problem can be

approximated with smooth functions that improve the convergence of the optimization

process because they reduce the effects of noise and they allow for the use of derivative-

based algorithms. Venter et al. (1996) have discussed the advantages of using RSM for

design optimization applications.



17

Lebaal et al. (2010) developed a new approach to the optimal design of the die

wall temperature profile in polymer extrusion processes. The optimization method was

based on response surface method. It has a very fast convergence, which is an advantage

when time-consuming flow analysis calculations are involved. DOE needed for the

construction of the response surface was used to evaluate the objective and the constraint

functions on the basis of a FEM. Two DOE were used and the performances of the

optimization results were compared with respect to efficiency and ability to obtain a

global optimum. The effect of the design variables in the objective and constraint

functions was investigated using Taguchi method. The flow analysis results were then

combined.

2.2.5 Genetic Algorithm

GAs are adaptive heuristics search algorithm based on the evolutionary ideas of

natural selection and genetics. As such they represent an intelligent exploitation of a

random search used to solve optimization problems. Although randomised, GAs are by no

means random, instead they exploit historical information to direct the search into the

region of better performance within the search space. The basic techniques of the GAs are

designed to simulate processes in natural systems necessary for evolution, especially

those follow the principles first laid down by Charles Darwin of “survival of fittest”. In

nature, competition among individuals for scanty resources results in the fittest

individuals dominating over the weaker ones. It is better than conventional AI as it is

more robust. Unlike older AI systems, they do not break easily when the inputs are

changed slightly or in the presence of reasonable noise.

Yu et al. (2004) determined the optimal die gap programming of extrusion blow

molding processes using soft-computing techniques. The design objective was to obtain a

uniform part thickness after parison inflation by manipulating the prison die gap openings

over time. Commercial finite element software (BlowSim) from the National Research

Council (NRC) of Canada was used to model the whole process, i.e., the parisonextrusion, the mould clamping, and the parison inflation. A new approach called fuzzy

neural-Taguchi network with genetic algorithm (FUNTGA) was implemented to establish

a back propagation network using a Taguchi’s experimental array to predict the

relationship between design variables and responses. Taguchi’s experimental designs

were employed for the training of a neural network model and the trained network was



18

used as the function generator of the design fitness in the GA. The GA searching

efficiency was enhanced using the introduction of a fuzzy inference of the engineering

knowledge.

Mu et al. (2012) proposed an optimization approach for the processing design in

the extrusion process of plastic profile with metal insert based on finite element

simulation, back propagation neural network and GA. The polymer melts flow in the

extrusion process was predicted using finite element simulation. The simulated results

were extracted for the establishment of neural network. The search for globally optimal

design variable for the extrusion was done using GA with its objective function evaluated

using the established neural network model. The uniformity of outlet flow distribution

was taken as the optimization objective with a constraint condition on the maximum shear

stress. The objective of flow balance was achieved by the optimal design of two

processing parameters including the volume flow rate and the metal insert moving

velocity.

2.2.6 Non Linear Programming

Non-linear programming is the process of solving an optimization problem

defined by a system of equalities and inequalities, collectively termed constraints, over a

set of unknown real variables, along with an objective function to be maximized or

minimized, where some of the constraints or the objective function are non-linear(Bertsekas, 1999). It is the sub-field of mathematical optimization that deals with

problems that are not linear. Modern engineering practice involves much numerical

optimization. Except in certain narrow but important cases such as passive electronic

circuits, engineering problems are non-linear, and they are usually very complicated.

Example problems in engineering include analyzing design trade-offs, selecting optimal

designs and incorporating optimization methods in algorithms and models.

Mamalis et al. (2012) applied multi-parametric optimization to the processing

conditions in a spider die used for the extrusion of HDPE tubes. The parameters

investigated were inlet pressure, inlet temperature of the melt, temperature of the die

walls, and temperature of the spider legs. A CFD based model using the generalized

Newtonian approach was employed, to investigate pressure drop, along with flow and

temperature uniformity in the die. The numerical calculations for the three-dimensional

flow and temperature fields were performed with a finite element based CFD code,



19

Comsol 3.5. The Nelder-Mead nonlinear optimization technique was applied to the

numerical model, in order to pinpoint the processing conditions that result into

maximizing flow homogeneity at the die outlet. The objective function utilized was a

weighted average of the SNRs of flow temperature and velocity at the die outlet.

2.3 INTERNAL STRESSES IN PLASTIC SHEET EXTRUSION

All plastic must be heated to a molten state in order to form the polymer into a

film or a sheet. This is done mostly by extrusion process. The sheet must be cooled after

it is formed into a given thickness and width. The undesirable stresses are introduced

during this cooling process. The driven transport rolls must pull the film or sheet under

tension in order to transport the molten film through the cooling process. This pulling

process causes a stretching in the machine direction which results in the elongation or

shrinkage of the film or sheet. These stresses get locked in when the sheet is cooled. The

film or sheet tends to revert back to its original shape when the film or sheet is reheated in

a subsequent operation. The ability to control or minimize such induced stresses which

are common in all areas of plastic process is important to determine the characteristics of

processes performed on the sheet afterwards (Staats, 1972).

If the internal stresses are not minimized it can cause serious problems while

performing the next in-house operations such as welding of the sheets or thermoforming.

Even though the product may appear acceptable, any internal stresses processed into the product becomes evident when it undergoes another thermal history and it can result in

customer dissatisfaction. The internal stresses have been found to influence important

physical characteristics of the sheet such as flexibility, strength, impact resistance,

dimensional stability etc. and can be used therefore as a criterion for specification

purposes (SPD, 2001). Therefore it is great necessary to make sure that all precautions are

taken to alleviate the unwanted stresses.

The stresses induced in polymer sheet are a characteristic of shrinkage of sheet.

Since many secondary processes such as printing, lamination, embossing, sealing and

fabrication done on plastics involves heat, shrinkages are an important parameter of

plastics. The linear dimension can change when a plastic film or sheet is reheated. This is

typically referred to as shrinkage. The user can infer from shrinkage of the plastic how it

was made. It can be used for quality control or process control as an indication of roll-to-

roll or sheet-to-sheet variation.



20

2.3.1 Factors Influencing Internal Stresses in Polymer Sheet Extrusion

Figure 2.1 highlights the factors that influence the stress induced in polymer sheet

extrusion.

Figure 2.1 Cause and Effect Diagram of Internal Stresses in Polymer Sheet Extrusion

2.4 LITERATURE REVIEW SUMMARY

This chapter presents a review of research in the determination of the process

parameters for plastic extrusion. A number of research works based on various

approaches including mathematical model, Taguchi Technique, ANN, Fuzzy logic, GA,

Non-linear modeling and RSM have been described.

A review of literature on optimization techniques has shown a successful

industrial application of DOE-based approaches for optimal settings of process variables.

Taguchi method is a robust design technique widely used in industries for making the

product/process insensitive to any uncontrollable factors such as environmental variables.

Taguchi approach has helped in reducing the experimental time and cost of product or

process development and quality improvement.



21

ANN and GA are emerging as the new approaches in the determination of the

process parameters for plastic extrusion. A trained neural network system can quickly

provide a set of extrusion parameters according to the results of the predicted quality of

extruded parts. However, the time required in the training and retraining for a neural

network could be very long. By using GA approach, the system can locally optimize the

extrusion parameters even without the knowledge about the process.



22

CHAPTER 3

COMPANY PROFILE

3.1

ABOUT TERUMO PENPOL LIMITED

Terumo Penpol is the largest manufacturer of blood bags in India. With an

installed capacity of 22 million blood bags, the company is one of the largest producers of

blood bags in Asia, other than Japan. In 1985 Peninsular Polymers which was a pioneer in

the field of manufacturing blood bags was set up by Mr. C. Balagopal an ex-Indian

Administrative Service (IAS) officer and his brother Mr. C. Padmakumar. In July 1999

the company formally collaborated with Terumo Corporation, Japan. The name of the

company was changed to Terumo Penpol Limited (TPL) on 01/10/1999.

The company pioneered the manufacture of blood bags in India and then

successfully launched a range of medical electronic products required for blood

transfusion centers. Driven by its strong customer-focus and innovative spirit, the

company has been the market leader ever since it introduced blood bags in India.

Terumo Penpol blood bags are sold in over 64 countries across the world and its

medical equipment division has more than 25000 installations to its credit. Terumo

Penpol has it’s headquarter in Thiruvananthapuram, Kerala and employs 1200 people.

The company has an experienced team of marketing and sales professionals covering the

whole of India.

Terumo Penpol is part of the multi-billion dollar Terumo Corporation, a Japanese

company having its presence in over 150 countries. Terumo has distinguished itself as a

high quality manufacturer of medical products, with 13 factories around the world. The

company generates annual sales of about $4 billion and employs 19000 people

worldwide.

Mission: Contributing to Society through Health Care

Vision: To become a world class medical device manufacturer

adopting customer first approach in all business processes.



23

Quality Policy: To manufacture high quality products meeting customer

requirements.

To design all products in accordance with international

standards.

To ensure high quality at all stages of the supply chain.

Quality Objective: Ensuring strict compliance to good manufacturing

practice.

Improving the product quality through continuous

technological up gradation.

Enhancing the product performance by sustained

analysis of customer feedbacks.

Maintaining high degree of quality awareness among

employees through periodic training programs.

3.2 PRODUCT PROFILE

TPL has a wide range of products. It consists of blood bags, urine bags, blood bag

equipment, blood bag management software etc. Table 3-1 shows the product range of

Terumo Penpol.

Table 3-1. Product Range of Terumo Penpol

Blood collection Processing Storage Issue

Blood bags

Donor station

Blood collection

monitor

Tube sealer

Portable tube

sealer

Composcale +

intelligent expressor

Terumo automatic

component extractor

Terumo sterile

connecting device

Blood storage

cabinet

Platelet agitator

incubator

Deep freezers

Plasma bath

Cryo bath

Blood transfusion

set

Sterile connecting

device



24

i. Blood bags

There are more than 60 variants of blood bags being manufactured by the unit.

They are under the following categories.

a)

Single bag

b) Double bag

c) Triple bag

d) Quadruple bag

Figure 3.1 presents different types of blood bags manufactured by Terumo Penpol.

Figure 3.1 Types of Blood Bags Manufactured by Terumo Penpol

(a)

Single Bag (b) Double Bag

(c) Triple Bag (d) Quadruple bag



25

ii. Portla (Blood bank management software)

Portla significantly improves safety, productivity, quality and cost

effectiveness enabling solution to problems inherent in conventional blood

management methods. It is available in single and multi-user versions.

iii. Blood storage cabinets

It is a unique faced airflow, which maintains temperature of 4±1 oC and point

in the cabinet. It has built in voltage regulator. It has automatic defrosting condensate

evaporator and calibrated digital temperature sense.

iv.

Donor couch

It is designed for the comfort of the donor and the phlebotomist. Design is

based on haemo-dynamic principle (head high foot low position into head low foot

high position).

v.

Blood collection monitor

Ensures better quality blood than that collected using manual mixing methods.

Clamp programmed volume, uniformed oscillation for optimum mixing ensuring

higher yield of components.

vi.

Mini blood storage cabinets

Small capacity which is ideal for small 450mL blood bags power saver for

remote location with frequent power failure.

vii.

Deep freezer

Two models available- 400 cc and 800 cc high durability due to high impact

expocy coating to resist scratches and rusting. Insolating sublids to minimize cold or

during lid opening, clean fitter indicator light warns of fitter clogging.

viii.

IMU gard III-PC

To remove leukocytes and micro aggregates from one unit of packed red cells/

whole blood convenient air vent facility.



26

3.3 MANUFACTURE OF BLOOD BAGS

3.3.1 Raw Materials

Table 3-2 presents raw materials used for the manufacturing different components

in blood bag.

Table 3-2. Raw Materials used for Manufacturing Blood Bag Components

S.No. Raw material Components produced

1 PVC Transfusion port, needle cover, Y connector, moulded

tube for break off valve (BOV)

2 Polycarbonate (PC) Needle holder, Tip cap, Dual connector for Leukocyte,

BOV

3 Polypropylene (PP)Spike, L connector, PP Base, Double end connector,

Spike cover

4 LDPE Spike cover

3.3.2 Manufacturing Process

Blood bag is made by using medical grade PVC resins. The following major steps

are involved in the manufacture of blood bags. Figure 3.2 presents the flow chart of

manufacturing process of blood bags.

(a) Compounding

The process of incorporation of additives such as plasticizers (e.g.- Di-(2

ethyl hexyl) phthalate (DEHP)), Stabilizers, Lubricants etc. in PVC materials in

order to enhance their physical and chemical properties is known as compounding.

(b)

Palletizing

In palletizing, the compounded material is processed using any of the

convenient conventional methods in order to attain cylindrical or granular shape.

(c)

Sheet extrusion

PVC materials are extruded through T die for converting plasticized

material in sheet form. Single screw extruders are employed for this purpose.



27

(d) Injection molding

The polymer is preheated in a cylindrical chamber up to a temperature at

which it melts and it is then forced into a relatively cold closed mold cavity by

means of high pressure.

(e) Tube extrusion

PVC materials are extruded through die for converting plasticized material

into tube form.

(f) Ultra cleaning

It is done to remove dust, mold releasing agents and other oily substance

from the component.

(g)

Welding

High frequency welding techniques are used in the blood bags. PVC sheet

is placed between electrodes and high frequency is applied. During welding,

donor, transfer tubing etc. are kept in correct position.

(h) Labeling

Labels giving information and instructions are pasted on the bags.

(i)

Anti-coagulant preparation and filling

Anti-coagulant is prepared by mixing pyrogen free distilled water with IP

grade chemicals like sodium citrate, citric acid etc. After mixing the solution is

filled in the bag and sent for coagulation.

(j)

Autoclaving

The blood bag contains anticoagulant are autoclaved in specially designedtrolleys.

(k) Drying and aluminium foil packing

After reaching the packing section, packing is done in clean laminar flow,

sterile area to avoid contamination at this stage.



28

PVC

Extrusion PVC

Sheets

Ports

&

Tube

insertion

Bag Welding

Labeling

Filling

Visual

PP Over

Wrapping

Sterilization

Drying

Primary

Packing

Packing

Store

Tube

PortsInjection

Moulding

Label

Needle

PP Bags

Al.

Foil

Bags

Corrugated

Box

Needling

Fig. 3.2 Flow Chart of Manufacturing Process



29

3.3.3 Specifications of Blood Bags

Design requirements

(1) Air content : Not exceeding 10 mL (lot test)

(2) Emptying under pressure : Without leakage for 2 min. (lot test)

(3) Rate of collection : To be filled in less than 8 min.

(4) Collecting and transfer tubes : Withstand a tensile force of 20 N (lot test)

(5) Blood taking needle : Withstand a tensile force of 10 N (lot test)

(6) Suspension : Withstand a tensile force of 20 N for 60

min. at 23±2 oC (lot test)

General requirements

Transparent Virtually colorless

Flexible

Sterile Non pyrogenic Free from toxicity

Non frangible Compatible

Physically stable Chemically stable

Biologically stable Non penetration of micro

organisms

Physical requirements

(1) Transparent

(2) Opalescence : Slightly opalescent

(3) Coloration : None

(4) Thermal stability : Should withstand storage at –80 oC for 24 h

and subsequent immersion in water at 50±2

oC for 20 min.

(5) Vapour transmission : Not more than 2% loss of water on storage

for six weeks

(6) Resistance to leakage

(7) Distortion : Should withstand acceleration of 5000 g for

30 min. at 4 oC and 37 oC.

Chemical requirements

(1) Oxidisable matter (Na2S2O3-

0.01mol/L)

: <2 mL



30

(2) Ammonia : <2 mg/L

(3.a) Acidity (NaOH- 0.01mol/L) : <0.4 mL

(3.b) Alkalinity (HCl- 0.01 mol/L) : <0.8 mL

(4) Residue on evaporation : <3 mg/100mL

(5) Extractable DEHP : <10 mg/100mL

(6) UV absorption (Extinction in the

range 230-360nm)

: <0.2

(7) Ash content : <1 mg/g

(8) Elements Ba,Pb : <1 mg/kg

(9) Elements Cd,Sn : <0.6 mg/kg

(10) Vinyl chloride monomer (VCM) : <1 µg/g

Biological requirements & type test

(1) Cell culture cytotoxicity : National req./ASTM F813

(2) Haemolysis : National req./USP

(3) Acute toxicity : National req./USP

(4) Intracutaneous injection : National req. /USP

(5) Sensitization : National req./ASTM F748

(6) Sterility : As per USP

(7) Pyrogenicity : As per USP

Dimensional requirements

(1) Collecting tube (i) Length - min 800 mm

(ii) Internal diameter - min 2.7 mm

(iii) Wall thickness - min 0.5 mm

(2) Transfer tube (i) Length - min 200 mm

TPL specification s

(1) Collecting tube (i) Length 98 ± 2 cm

(ii) Internal diameter 3 ± 0.5 mm

(iii) Outer diameter 4.35 ± 0.5 mm

(2) Sheet thickness : 0.38 ± 0.02 mm



31

CHAPTER 4

EXPERIMENTATION METHODOLOGIES

Every researcher has to plan and conduct experiments to obtain an adequate

amount of appropriate data, so that he can understand the science behind the observed

phenomenon. The literature review gives an insight into the various methodologies that

can be used to do so. The experimentation methodologies utilized in this work are

explained below.

4.1 DESIGN OF EXPERIMENTS

The term experiment is defined as the systematic procedure carried out undercontrolled conditions in order to discover an unknown effect, to test or establish a

hypothesis, or to illustrate a known effect. When analyzing a process, experiments are

often used to evaluate which process inputs have a significant impact on the process

output, and what the target level of those inputs should be to achieve a desired result

(output). Experiments can be designed in many different ways to collect this information.

DOE is a systematic, rigorous approach to engineering problem-solving that

applies principles and techniques at the data collection stage so as to ensure the

generation of valid, defensible, and supportable engineering conclusions. DOE is also

referred to as designed experiments or experimental design - all of the terms have the

same meaning. Many of the current statistical approaches to designed experiments

originate from the work of R. A. Fisher in the early part of the 20th century.

Experimental design can be used at the point of greatest leverage to reduce design

costs by speeding up the design process, reducing late engineering design changes, and

reducing product material and labour complexity. DOE are also powerful tools to achieve

manufacturing cost savings by minimizing process variation and reducing rework, scrap,

and the need for inspection. The main limitations of DOE are the difficulty to identify the

right process parameters for optimizing the objective function and to choose the

appropriate parameter range in which the relation between inputs and response are linear

(Spina, 2006)



32

There are four general engineering problem areas in which DOE may be applied:

1.

Comparative: In the first case, the interest lies in assessing whether a change

in a single factor could bring about a change to the process as a whole.

2.

Screening/Characterizing : In the second case, the engineer is interested in

finding out which all factors affect the process. Through this engineer could

have an understanding of the process as a whole and could get an idea about

the factors (most important to least important) that affect the process.

3. Modeling : In the third case, the interest lies in functionally modelling the

process. The out-put is modelled as a good-fitting mathematical function

which could make good predictions and estimates of the coefficients in the

function have to be good which could provide maximal accuracy.

4. Optimizing : In the fourth case, the focus is on determining optimal settings of

the process factors. The engineer is interested in determining the level of each

factor that would optimize the process response.

Five steps in DOE process are:

1.

Define the problem

The first step is to properly define the problem. It is essential to have a clear,concise problem along with the details defining the problem (e.g. Defining the ideal

operating window for a process to optimize the cycle time).

2. Design the experiment

Second step in the DOE process is to develop a plan. Independent variables or

factors associated with the process or product are determined. The independent

variables or factors are the parameters that you control or change during the

experiment. These changes can be made to happen by changing settings on machine

or can sometimes be qualitative: on or off, high or low, yes or no. Table 4-1 presents

some independent variables for extrusion, compression molding, blow molding and

injection molding.



33

Table 4-1. Independent Variables for Plastic Processes (The society of plastic

engineers, 2004)

Injection molding Compression molding Blow molding Extrusion

Barrel temperature Preheat temperature Barrel temperature Screw speed

Nozzle temperature Mold temperature Screw speed Barrel temperature

Injection rate Press speed Hydraulic pressure Adapter temperature

Injection pressure Press force Blow pressure Die temperature

Boost pressure Cool time in tool Timer settings Feed rate(starve fed)

Boost time Charge replacement Heat profile Pull-off speed

Boost position Transfer time Stripper delay Material composition

Hold time Melt viscosity Take-off temperature

Hold pressure Melt strengthScrew speed Mold temperature

Mold temperature Adapter temperature

Back pressure Screen pack

Material Vacuum level

For defining the experiments the following checklist may be followed:

Identify all independent variables associated with the process.

Define the factors to be fixed.

Determine the factors to be varied

Define the maximum and minimum limits for each factors being varied.

Determine the number of experimental points for each factor varied.

In any experimental design, a better understanding of how the independent

variables affect the dependent variables will be obtained if the measurement results

for the independent variables are more quantitative.

3.

Data collection

The third step in the DOE process is to run the experiment as defined earlier.

Before starting the experiment all the equipment shall be checked thoroughly and

while experimenting the data shall be collected when the system becomes stable or

attain equilibrium.



34

4. Analyze the data

The fourth step in the process is to analyse the data. It is done by the statistical

analysis of the data, taking into account the confidence limits of the results.

Reliability of the data and validity of the tests is verified by statistics.

5. Report the result

The final step in any DOE process is to summarize and report the results.

Three general types of experimental designs are:

1.

Factorial designs

Factorial designs are used when there are many independent variables in the

DOE and to find out which variables and interactions are significant to the process or

product. Normally, two level factorial designs are used. The number of experiments

required depends on the number of independent variables and the number of levels

(e.g. a DOE that has three independent variables requires 8 experiments whereas in

case of 4 independent variables, the number of experiments required is 16). With

large number of independent variables, fractional factorials are used to minimize the

number of experiments and determine which independent variables are significant for

different dependent responses.

2. Response surface designs

RSM is a collection of mathematical and statistical techniques useful for the

modeling and analysis of problems in which a response of interest is influenced by

several variables. The RSM is widely used as an optimisation, development, and

improvement technique for processes based on the use of factorial designs—that is,

those in which the response variable is measured for all the possible combinations of

the levels chosen of the factors. The application of the RSM becomes indispensable

when, after the significant factors affecting the response have been identified, it is

considered necessary to explore the relationship between the factor and dependent

variable within the experimental region and not only at the borders. Central composite

design and Box-Behnken design are the two most popular response surface designs.



35

3. Mixture experiments

The design factors in a mixture experiment are the proportions of the

components of a blend, and the response variables vary as a function of these

proportions making the total and not the actual quantity of each component. The total

amount of the mixture is normally fixed in a mixture experiment and the component

settings are proportions of the total amount. The component proportions in a mixture

experiment cannot vary independently as in factorial experiments since they are

constrained to sum to a constant (1 or 100% for standard designs). Imposing such

constraints on the component proportions complicates the design and the analysis of

mixture experiments. Although the best known constraint in a mixture experiment is

to set the sum of the components to one (100%), additional constraints such as

imposing a maximum or minimum value on each mixture component may also apply.

Thus DOE is a very powerful technique that helps to understand the effects that

independent variables have on measured dependent variables.

4.2 TAGUCHI METHOD

Dr. Taguchi of Nippon Telephones and Telegraph Company, Japan has developed

a method based on orthogonal array experiments which gives much reduced variance for

the experiment with optimum settings of control parameters. Thus the integration of DOE

with optimization of control parameters to obtain desired results is achieved in the

Taguchi method (Bharti and Khan, 2010). Orthogonal arrays(OA) provide a set of well

balanced (minimum) experiments and Dr. Taguchi's Signal-to-Noise ratios (SNRs), which

are log functions of desired output, serve as objective functions for optimization, help in

data analysis and prediction of optimum results.

Taguchi developed the foundations of robust design and validated its basic

philosophies by applying them in the development of many products. Taguchi method

can be used for optimization methodology that improves the quality of existing productsand processes and simultaneously reduces their costs very rapidly, with minimum

engineering resources and development man hours (Mathivanan et al., 2010). Taguchi’s

two most important contributions to quality engineering are the use of Gauss’s quadratic

loss function to quantify quality and the development of robust designs (parameter and

tolerance design).



36

In general, there are four quality concepts devised by Taguchi:

1.

Quality should be designed into the product from the start, not by inspection

and screening.

Inspection or random testing cannot determine the quality of a product.

Quality is created through implementing engineering techniques from the design

phase and carrying them out through the whole of the production phases. This is often

called an "off-line" strategy.

2. Quality is best achieved by minimizing the deviation from the target, not a

failure to confirm to specifications.

Quality can be achieved by drastically decreasing variations from the target.

The design of the product should be such that it is robust or immune to uncontrollable

environmental factors - noise, temperature, and humidity. This concept deals with the

actual methods that effect quality. Specifying a target value for critical parameters can

reduce variation, and ensuring manufacturing meets the target value with little

deviation, the quality may be greatly improved.

3.

Quality should not be based on the performance, features or characteristics of

the product.

Adding features to a product is not a way of improving quality, but only of

varying its price and the market it is aimed at. The performance and characteristics of

a product, can be related to quality, but should not be the basis of quality. Instead,

performance is a measure of product capability.

4. The cost of quality should be measured as a function of product performance

variation and the losses measured system- wide.

The cost of quality should be measured as a function of product performance,

variation, and the losses measured system. Deviations from a target are measured in

terms of the overall life cycle costs of the product, which include costs of re-working,

inspection, warranty servicing, returns, and product replacement. It is these costs that

provide guidance as to which major parameters need the most controlling.



37

Taguchi suggested that the design process should be seen as three stages (Ozcelik

and Erzurumlu, 2010; Swan, 1998):

Systems design

Parameter design

Tolerance design

Figure 4.1 presents the design process suggested by Taguchi.

System design is the conceptualization and synthesis of a product or process to be

used. The system design stage is where new ideas, concepts and knowledge in the areas

of science and technology are utilized by the design team to determine the right

combination of materials, parts, processes and design factors that will satisfy functional

and economical specifications. To achieve an increase in quality at this level requires

innovation, and therefore improvements are not always made.

In parameter design the system variables are experimentally analyzed to determine

how the product or process reacts to uncontrollable “noise” in the system; parameter

design is the main thrust of Taguchi’s approach. Parameter design is related to finding the

appropriate design factor levels to make the system less sensitive to variations in

uncontrollable noise factors, i.e., to make the system robust. In this way the product

performs better, reducing the loss to the customer.

The final step in Taguchi’s robust design approach is tolerance design; tolerance

design occurs when the tolerances for the products or process are established to minimize

the sum of the manufacturing and lifetime costs of the product or process. In the tolerance

design stage, tolerances of factors that have the largest influence on variation are adjusted

only if after the parameter design stage, the target values of quality have not yet been

achieved. Most engineers tend to associate quality with better tolerances, but tightening

the tolerances increases the cost of the product or process because it requires better

materials, components, or machinery to achieve the tighter tolerances.

Taguchi methodology emphasises the importance of the middle (parameter

design) stage in the total design process; a stage which is often neglected in industrial

design practice. The methodology involves the identification of those parameters which

are under the control of the designer, and then the establishment of a series of

experiments to establish that subset of those parameters which has the greatest influence



38

on the performance and variation of the design. The designer thus is able to identify the

components of a design which most influence the desired outcome of the design process.

Figure 4.1 Design Process Suggested by Taguchi (Mahajan, 2007)

4.3

ROBUST DESIGN

Taguchi’s definition of a robust design is: “a product whose performance is

minimally sensitive to factors causing variability (at the lowest possible cost)”. Taguchi’s

view was that in traditional systems, robustness (or in general, quality) was measured by

some performance criteria, such as meeting the specifications, per cent of products

scrapped, cost of rework, per cent defective, failure rate etc. However, these measures of

performance are all based on make-and-measure policies. They all come too late in the

product development cycle.

Robust design is a systematic methodology to design products whose performance

is least affected by variations, i.e. noise, in the system (system variations here means

variations due to component size variations, different environmental conditions, etc.).

Robust Design method is central to improving engineering productivity. Pioneered by Dr.

Genichi Taguchi after the end of the Second World War, the method has evolved over the

last five decades. Robust Design focuses on improving the fundamental function of the

Parameter Design

Tolerance Design

System Design Traditional R&D

Taguchi method



39

product or process, thus facilitating flexible designs and concurrent engineering. Indeed,

it is the most powerful method available to reduce product cost, improve quality, and

simultaneously reduce development interval.

The Robustness Strategy uses five primary tools:

1.

Parameter diagram (P-Diagram) is used to classify the variables associated

with the product into noise, control, signal (input), and response (output)

factors.

2. Ideal Function is used to mathematically specify the ideal form of the signal-

response relationship as embodied by the design concept for making the

higher-level system work perfectly.

3.

Quadratic Loss Function (also known as Quality Loss Function) is used to

quantify the loss incurred by the user due to deviation from target

performance.

4. Signal-to-Noise Ratio is used for predicting the field quality through

laboratory experiments.

5.

Orthogonal arrays (OAs) are used for gathering dependable information about

control factors (design parameters) with a small number of experiments.

P-Diagram

Figure 4.2 Parameter Diagram of a Product/Process/System (Mahajan, 2007)

Product/

Process/

System

Noise

factors

X

Signal

factorsM

Response

Y

Control

factors

Z



40

P-Diagram is a must for every development project. It is a way of succinctly

defining the development scope. The P-Diagram takes the inputs from

system/customers and relates those inputs to desired outputs of a design that the

engineer is creating, also considering non-controllable outside influences. The P-

Diagram is a useful tool in brainstorming and documenting – signal factors, response

variable or ideal function, control factors, noise factors, error states (or the failure

modes). P-Diagram is essentially a schematic diagram that encompasses signal factor,

control factor, noise factor and response variables. Figure 4.2 shows the parameter

diagram of a product/process/system.

Quality measurement

It is common to use the fraction of products outside the specified limits as the

measure of quality. Though it is a good measure of the loss due to scrap, it miserably

fails as a predictor of customer satisfaction. The quality loss function serves that

purpose very well.

The Taguchi loss function or quality loss function maintains that there is an

increasing loss both for producers and for society at large, which is a function of the

deviation or variability from the ideal or target value of any design parameter. The

greater the deviation from target, the greater is the loss. The concept of loss being

dependent on variation is well established in design theory, and at a systems level isrelated to the benefits and costs associated with dependability.

The quality loss, L, suffered by an average customer due to a product with y as

value of the characteristic is given by the following equation:

L = k * (y – m) ( 4.1 )

where k = ( A0 / Δ02 )

m – target value of critical product characteristic

Δ0 – allowed deviation from the target

A0 – loss due to defective product

Figure 4.3 presents the quality loss function.



41

Figure 4.3 Quality Loss Function

Signal-to-noise ratios (SNR)

The product/process/system design phase involves deciding the best

values/levels for the control factors. The SNR is an ideal metric for that purpose. The

equation for average quality loss, L, says that the customer’s average quality loss

depends on the deviation of the mean from the target and also on the variance. An

important class of design optimization problem requires minimization of the variance

while keeping the mean on target.

Between the mean and standard deviation, it is typically easy to adjust themean on target, but reducing the variance is difficult. Therefore, the designer should

minimize the variance first and then adjust the mean on target. Among the available

control factors most of them should be used to reduce variance. Only one or two

control factors are adequate for adjusting the mean on target.

The design optimization problem can be solved in two steps:

1.

Maximize the SNR defined as

SNR = 10 log ( )( 4.2 )

This is the step of variance reduction.



42

2. Adjust the mean on target using a control factor that has no effect on h. Such a

factor is called a scaling factor. This is the step of adjusting the mean on

target.

There are three standard types of SNR depending on the desired performance

response (Phadke, 1989):

1. Smaller the better (for making the system response as small as possible):

SNR s = -10 log ( ∑ ) ( 4.3 )

2.

Nominal the best (for reducing variability around a target):

SNR N = 10 log (

)

( 4.4 )

3.

Larger the better (for making the system response as large as possible):

SNR L = -10 log ( ∑ ) ( 4.5 )

Orthogonal arrays

The Taguchi method utilizes OAs from design of experiments theory to study

a large number of variables with a small number of experiments. OAs significantly

reduces the number of experimental configurations to be studied. Furthermore, the

conclusions drawn from small scale experiments are valid over the entire

experimental region spanned by the control factors and their settings (Phadke, 1989).

OAs are not unique to Taguchi. They were discovered considerably earlier

(Bendell, 1988). However, Taguchi has simplified their use by providing tabulated

sets of standard OAs and corresponding linear graphs to fit specific projects (Taguchi

and Konishi, 1987). A L9 (34) orthogonal array is shown in Figure 4.4.

In this array, the columns are mutually orthogonal. That is, for any pair of

columns, all combinations of factor levels occur; and they occurs equal number of

times. Here there are four parameters A, B, C and D each at three levels. This is called

an "L9" design, with the 9 indicating the nine rows, configurations or prototypes to be

tested. Specific test characteristics for each experimental evaluation are identified in

the associated row of the table. Thus, L9 means that nine experiments are to be carried



43

out to study four variables at three levels. The number of columns of an array

represents the maximum number of parameters that can be studied using that array.

Note that this design reduces 81 (34) configurations to 9 experimental evaluations.

A B C D

1 1 1 1

1 2 2 2

1 3 3 3

2 1 2 3

2 2 3 1

2 3 1 2

3 1 3 2

3 2 1 3

3 3 2 1

Figure 4.4 L9 (34) Orthogonal Array

4.4 OVERVIEW OF TAGUCHI METHOD

Figure 4.5 provides a brief overview of the process followed by Taguchi's

approach to parameter design (Phadke, 1989; Wille, 1990).

1. Determine the quality characteristic to be optimized

Determining the quality characteristic to be optimized is the first step in the

Taguchi method. The quality characteristic is a parameter whose variation has a

critical effect on product quality. It is the output or the response variable to be

observed. Examples are weight, cost, corrosion, target thickness, strength of a

structure, and electromagnetic radiation.



44

2.

Identify the noise factors and test conditions

The next step is to identify the noise factors that can have a negative impact on

system performance and quality. Noise factors are those parameters which are either

uncontrollable or are too expensive to control. Noise factors include variations in

environmental operating conditions, deterioration of components with usage, and

variation in response between products of same design with the same input.

Figure 4.5 Flowchart of Taguchi Method (Phadke, 1989)

3.

Identify the control parameters and their alternative levels

The third step is to identify the control parameters thought to have significant

effects on the quality characteristic. Control (test) parameters are those design factors

that can be set and maintained. The levels (test values) for each test parameter must bechosen at this point. The number of levels, with associated test values, for each test

parameter defines the experimental region (Unal and Dean, 1991).

4. Design the matrix experiment and define the data analysis procedure

Predict the Performance at these Levels

Analyze the Data and Determine Optimum Levels for Control Factors

Conduct the Matrix Experiment

Design the Matrix Experiment and Define the Data Analysis Procedure

Identify the Control Factors and their Alternative Levels

Identify the Noise Factors and Test Conditions

Determine the Quality Characteristic to be Optimized



45

The next step is to design the matrix experiment and define the data analysis

procedure. First, the appropriate OA for the noise and control parameters to fit a

specific study are selected. Taguchi provides many standard OA and corresponding

linear graphs for this purpose (Taguchi and Konishi,1987).

Taguchi proposes OA based simulation to evaluate the mean and the variance

of a product's response resulting from variations in noise factors (Bryne and Taguchi,

1986; Phadke, 1989; Taguchi, 1986). With this approach, OAs are used to sample the

domain of noise factors. The diversity of noise factors are studied by crossing the OA

of control factors by an OA of noise factors (Bendell, 1988).

5.

Conduct the matrix experiment

The next step is to conduct the matrix experiment and record the results. The

Taguchi method can be used in any situation where there is a controllable process

(Meisl, 1990; Phadke, 1989; Wille, 1990). The controllable process can be an actual

hardware experiment, systems of mathematical equations, or computer models that

can adequately model the response of many products and processes.

6. Analyze the data and determine the optimum levels

After the experiments have been conducted, the optimal test parameter

configuration within the experiment design must be determined. To analyse theresults, the Taguchi method uses SNR.

7.

Predict the performance at these levels

Using the Taguchi method for parameter design, the predicted optimum

setting need not correspond to one of the rows of the matrix experiment. This is often

the case when highly fractioned designs are used (Bryne and Taguchi, 1987; Phadke,

1989). Therefore, as the final step, an experimental confirmation is run using the

predicted optimum levels for the control parameters being studied.

4.5 ANALYSIS OF VARIANCE (ANOVA)

An important technique for analyzing the effect of categorical factors on a

response is to perform an analysis of variance (ANOVA). An ANOVA decomposes the

variability in the response variable amongst the different factors. Depending upon the



46

type of analysis, it may be important to determine: (a) which factors have a significant

effect on the response, and/or (b) how much of the variability in the response variable is

attributable to each factor.

Taguchi experimental design could not judge the effect of independent variables

on the entire process therefore; the percentage contribution using ANOVA can be used to

compensate for this effect. The parameters used in ANOVA are explained below:

1. Source: The source includes the independent variable control factor A, B, C,…

and error factor e and the sum of all observations T .

2.

Sum of squares (SS): SS A , SS B , SS C ,… denote the sum of squares of A,B,C,…

and SSe denotes the sum of squares of error; SS T is the total variation. Thus

the equation can be written as

SS T = ∑ ( 4.6 )

where, CF (correction factor) =∑

m = Total number of experiments

n j = SNR of individual experiments

SS e = SS T -(SS A+SS B+…)

3.

Degree of freedom (DF): DF denotes the number of independent variables. In

the ANOVA table, the DF for each factor is the number of its levels minus

one. The error of the DF is the total DF minus the sum of the DF of each

factor.

Calculation of DF (rule)

The overall mean always uses one DF.

For each factor A, B,…; if the number of levels are n A , n B ,…; for each

factor the DF = number of levels – 1; for example the DF for factor A

is n A-1 and B is n B-1.

For any two factor interaction, for example A and B the DF is (n A-

1)(n B-1).



47

4. Variance: It is explained as the sum of squares divided by the degree of

freedom, it can be expressed mathematically as

V i = ( 4.7 )

where i = A, B, C,..., e, T.

5. F-ratio: This value is defined as the variance of each factor divided by the

error variance.

( 4.8 )

where i = A, B, C,..., e, T.

The F-value can determine the effect of each variable. The bigger the F-value

the more significant is the effect of the variable.

6. Expected sum of squares (SS’): It is explained as the sum of squares of all

factors minus the error variance and then multiplied by the degree of freedom

of each factor.

∗ ( 4.9 )

where I = A, B, C,..., e, T.

7. P-value (Percentage of contribution to the total variation): P i denotes the

percentage of the total variance of each individual variable.

′ ∗ 100% ( 4.10 )

where I = A, B, C,..., e, T.

In case of ANOVA analysis, if the percentage error contribution to the total

variance, P e is lower than 15% it implies that no significant variable is missing in the

experimental design. On the other hand, if P e exceeds 15%, certain important variables

are overlooked and the experimental data are no longer considered representative and the

error is replaced by “pool to error”.



48

ANOVA helps in formally testing the significance of all main factors and their

interactions by comparing the mean square against an estimate of the experimental error

at specific confidence level. In the analysis, F-ratio is a ratio of mean square error to

residual, and is traditionally used to determine significance of a factor. However, F-ratio

does not indicate the extent of deviation in the result, therefore, p-value is called as level

of significance (Montgomary, 1986). If p-value for a factor is less than 0.05, then the

factor is considered as statistically significant at 95% confidence level.

4.6 MINITAB

Minitab is a comprehensive and flexible statistical analysis solution. Minitab is

often used in conjunction with the implementation of six sigma and other statistics-based

process improvement methods. It offers a complete set of statistical tools. Minitab is

distributed by Minitab Inc, a privately owned company headquartered in State College,

Pennsylvania, with subsidiaries in Coventry, England (Minitab Ltd.), Paris, France

(Minitab SARL) and Sydney, Australia (Minitab Pty.).

4.6.1 Taguchi Experimental Analysis in Minitab

1. Before using Minitab, all pre-experimental planning need to be completed.

For example, all control factors for the inner array and noise factors for the

outer array need to be chosen.

2.

Taguchi design (orthogonal array) can be generated by using either Create

Taguchi Design or Define Custom Taguchi Design option.

3.

After the creation of the design, units (coded or uncoded) in which Minitab

expresses the factors in the worksheet can be changed using Display

Design option. Use Modify Design to rename the factors, change the

factor levels, add a signal factor to a static design, ignore an existing signal

factor (treat the design as static), and add new levels to an existing signal

factor.

4.

Perform the experiment and gather the response data. Then enter the

collected response data in Minitab worksheet.

5.

Use Analyze Taguchi Design to analyze the collected experimental data.

6.

Use Predict Taguchi Results to predict signal to noise ratios and response

characteristics for selected new factor settings.



49

4.6.2 Two-way ANOVA in Minitab

1.

Use Stat > ANOVA > General Linear Model > Fit General Linear

Model to perform a two-way ANOVA in Minitab.

2.

In Responses, enter the response variable name.

3. In Factors, enter the name of the factors.

4. Select Model.

5.

Select the factors in Models and covariates. Choose 2 to the right

of Interactions through order and click Add.

6.

Click OK in each dialog box.



50

CHAPTER 5

EXPERIMENTAL WORK

5.1 SELECTION OF PROCESS PARAMETERS AND THEIR LEVELS

Selection of input parameters is the beginning task of any experimental work. The

proper selection of input parameters requires knowledge of the industry, process

mechanics, past experience and the manufacturer’s data. Wrongly selected value of

process parameters may lead to poor results. In order to identify the process parameters

that affect the stress induced in polymer sheets during extrusion, an Ishikawa cause-effect

diagram is constructed.

5.1.1 Responsible Parameters for Stresses Induced in Extruded Polymer sheet

The first step in phase I is to determine the various factors/parameters and their

level that influence the performance characteristics. As a result of literature and industrial

survey following parameters are found responsible for stress induced in polymer sheet

during extrusion.

Draw ratio

Screw speed

Melt temperature

Adapter temperature

Die temperature

Pull-off speed

Middle roll temperature

Bottom roll temperature

Following noise factors are also identified

Improper screw and/or die design

Variation in material composition

Operator-induced variations

Changes in the plant environment



51

In this experimental work, five factors are considered. These are draw ratio, melt

temperature, die temperature, middle roll temperature and bottom roll temperature. For

the optimisation of sheet extrusion, two levels of factors are used. Table 5-1 presents the

control variables and their levels.

Table 5-1. Control Variables and Their Levels

Control variables Low High

Draw ratio 1:1 2:1

Melt temperature 177oC 199oC

Die temperature 170oC 200oC

Middle roll temperature 70oC 100oC

Bottom roll temperature 15oC 35oC

The die gap and draw ratio are related to each other. Sheets of thickness 0.36 mm

were made for all the experiments. The die gap should be set at 0.36 mm to extrude a

sheet of thickness 0.36 mm with a draw ratio of 1:1. For a draw ratio 2:1 the die gap

should be set at 0.72 mm. The effect of different draw ratios on inducing stresses in the

extruded sheet was examined as the drawing of the material between the die lip and rollnip can either result in increased stresses due to the stretching or decreased stresses as the

extruded sheet is allowed to relax before it is cooled.

When extruded sheet from the die comes in contact with a chill roll of extremely

low temperature, stresses induced into the material becomes fixed But if the temperature

of the nip is set just low enough to allow the material not to stick to the center roll and if

the material is given enough time to cool at a uniform rate and to relax, then the stresses

won’t get set into the sheet.

Thermal consistency of the extruded material must also be considered as the

temperature at which polymer is processed must be selected in such a way that the

stresses induced during plasticating should be relieved as much as possible before

entering the die (Chan and Lee, 1989).



52

5.2 POLYMER MATERIAL

Sheet grade PVC pellets (S-502 TW9, from Vinashowa) were used for the

experimentation. Some of the specifications of the material are presented in Table 5-2.

Table 5-2. Specifications of Sheet Grade PVC Pellet used for Sheet Extrusion

Composition

A PVC resin K-value 80 62.40±1%

B DEHP 32.44±1%

C Epoxidised soya bean oil 4.99±0.1%

D Calcium stabilizer 0.07±0.01%

E Zinc stabilizer 0.07±0.01%

F Silicon oil 0.09±0.1%

Chemical requirements

1 Colouration None

2 Opalescence Slight

3 UV absorption (Extinction in the range 230-360nm) 0.2 max

4 Extractable DEHP (mg/100 mL) <15.0

5 Oxidisable matter (Na2S2O3 - 0.01mol/L) <1.5

6 Ammonia (mg/L) <0.8

7 Chloride ions (mg/mL) <4

8(a) Acidity (NaOH – 0.01mol/L) <0.4 mL

8(b) Alkalinity (HCl – 0.01 mol/L) <0.8 mL

9 Residue on evaporation (mg/100 mL) <5

10 Ash (mg/mL) <1

11 Determination of heavy metals (as per ISO 3826) 2 mg/L

Biological requirements

1 Cell culture cytotoxicity as per ISO 10993-5/USP Should comply

2 Haemolysis test as per ISO 3826 Should comply

3 Systematic injection as per USP/ISO 10993-11(acute Should comply

4 Intracutaneous injection as per USP/ISO 10993 (irritation) Should comply

5 Sensitisation test as per ISO 10993-10 Should comply



53

Physical requirements

1 Appearance Clear

2 Colour Almost colourless

3 Hardness (shore A, 15 seconds at 230C) 76 – 80

4 Tensile strength at 230C (kg/cm2) ≥153.06

5 Elongation at break (%) ≥300

6 Specific gravity 1.22 – 1.23

7 Pellet size Length: 2-5 mm

Diameter: 3-4 mm

5.3

EXTRUDER

A IKEGAI ES 65-22 single screw extruder fitted with a conventional three-zone

screw was used for the experimentation purpose. The specifications of the extruder are

presented in Table 5-3.

Table 5-3. Specifications of the Extruder Machine

Type Single screw

Resin extruded Sheet grade PVC

Extrusion capability 50-60 kg/h

Screw

Diameter 65 mm

L/D 22

Revolution 20-60 rpm

Main electric motor

15 Kw

Change gear ratio 3:1

Type Three-phase shunt commutator motor

Control panel Attached to the side of the frame.

Temperature control panel One sided, self-supporting

Power Supply AC, 3ϕ, 200V, 50 Hz

Direction to use Left handed

Hopper 60L IKEGAI standard

Cylinder heating system Space heater



54

5.4 SHEET DIE

The specifications of sheet die used for experimentation purpose are presented in

Table 5-4.

Table 5-4. Specifications of sheet die

Company Extrusion Dies Incorporated (EDI)

Type of die Ultraflex 40 die

Die lip length 530 mm

Lip opening Maximum 0.040” (1.02 mm)

Minimum 0.001” (0.00 mm)

Weight 347.7 lbs (157.7 kg)

Thread system Metric

5.5 EXPERIMENTAL DESIGN

The total degrees of freedom are computed to select an appropriate orthogonal

array for experiments. In this study the interaction between the polymer sheet extrusion

parameters is neglected. Therefore, there are five degrees of freedom owing to the five

sets of extrusion parameters in polymer sheet extrusion. The degree of freedom for the

orthogonal array should be greater than or equal to those for the process parameters. In

this study an L8 orthogonal array which can handle two-levels of process parameters is

used. This array has seven degrees of freedom. Each extrusion parameter is assigned to a

column and eight extrusion parameter combinations are available. Minitab (version 17)

software is used to design the experiments. Table 5-5 presents the coded L8 orthogonal

array generated by Minitab (version 17).

5.6 EXPERIMENTAL PROCEDURE

Based on the earlier researches and interview with the workers in the company,

the following variables were chosen for this experiment: draw ratio between die and roll

nip, melt temperature of the material before the die, die temperature, temperature settings

of the middle roll and the temperature settings of the bottom roll. The list of experiments

is presented in Table 5-6.



55

Table 5-5. L8 Orthogonal Array

C1 C2 C3 C4 C5

Draw ratio Melt temperature

(oC)

Die temperature

(oC)

Middle roll

temperature (oC)

Bottom roll

temperature (oC)

1 1 1 1 1 1

2 1 1 1 2 2

3 1 2 2 1 1

4 1 2 2 2 2

5 2 1 2 1 2

6 2 1 2 2 1

7 2 2 1 1 2

8 2 2 1 2 1

Table 5-6. List of Experiments

C1 C2 C3 C4 C5

Draw ratio Melt temperature

(oC)

Die temperature

(oC)

Middle roll

temperature (oC)

Bottom roll

temperature (oC)

1 1:1 177 170 70 15

2 1:1 177 170 100 35

3 1:1 199 200 70 15

4 1:1 199 200 100 35

5 2:1 177 200 70 35

6 2:1 177 200 100 15

7 2:1 199 170 70 35

8 2:1 199 170 100 15

The experiments were not necessarily carried out in the same sequence as given in

the table. It is done in a manner that allowed the most efficient use of time. For example,

all the experiments with the same die gap setting were carried out as first set. Then the

experiments having the cooler die temperature settings were carried out as the die could

be heated up faster than cooled down. While in the case of rollers experiments with hotter



56

roll temperature settings were performed as first set followed by lower temperature

setting as the rollers can be cooled more rapidly than heat them up.

Eight FPVC sheets of thickness 0.36 mm (0.35-0.38 mm) were extruded for this

study. A digital thickness gauge was used to make sure that the plastic sheet was

processed within the acceptable thickness tolerance. Four samples of size 100 mm x 100

mm were cut from each of the extruded FPVC sheets and were labelled ‘left outside’, ‘left

middle’, ‘right outside’ and ‘right middle’ with the machine direction marked on each

sample. Each sample is placed in an oven for 35 minutes at 1200C. The samples are then

allowed to cool to room temperature. The samples are then measured for the dimensional

changes. The length of all four sides and the longest diagonal were taken to calculate the

percentage of shrinkage.

The area of samples that were shrunken into irregular forms was found by using

the following method. The shortest side and the shortest length were used to form a right

triangle. The area of right triangle was found out using the following equation.

Ar = 0.5 × w × h ( 5.1 )

The remaining area of the irregular form was calculated using the equation used

for finding out the area of an acute triangle. The longest side, the longest length and the

diagonal were used to solve this.

Aa = (0.5 × b) ( 5.2 )

The sum of ( 1 ) and ( 2 ) gives a close approximation of the area of irregular

shrunken form.

At = Ar + Aa ( 5.3 )

The percentage shrinkage (%S ) is calculated by dividing the total area of shrunken

irregular form ( At ) by the area of pre-shrunken sample ( Ai) and then subtracting it from

unity.

% 1 100 ( 5.4 )



57

The experimental result for shrinkage is presented in Table 5-7. The observations

made about the state of samples after the shrinkage test were noted down and these

observations were found to have a direct correlation with the obtained calculations.

Table 5-7 . Experimental Result for Shrinkage

Run Shrinkage (%) Average

shrinkage (%)Left outside Left middle Right middle Right outside

1 7.135 7.091 7.096 7.142 7.116

2 4.312 4.274 4.292 4.308 4.297

3 4.918 4.873 4.885 4.904 4.895

4 3.237 3.232 3.229 3.239 3.234

5 8.483 7.781 7.984 8.291 8.135

6 6.915 6.746 6.732 6.861 6.814

7 6.351 6.179 6.278 6.319 6.282

8 5.449 5.395 5.403 5.458 5.426



59

T a b l e 6 - 1 .

S i g n a l - t o - N o i s e R a t i o a n d M e a n s

f o r S h r i n k a g e

M e a n s

7 . 1

1 6 0 0

4 . 2

9 6 5 0

4 . 8

9 5 0 0

3 . 2

3 4 2 5

8 . 1

3 4 7 5

6 . 8

1 3 5 0

6 . 2

8 1 7 5

5 . 4

2 6 2 5

S N

R

- 1 7 . 0 4 4

7 6 3 0

- 1 2 . 6 6 2

3 4 9 2

- 1 3 . 7 9 5

1 0 8 0

- 1 0 . 1 9 5

4 7 8 2

- 1 8 . 2 1 1

6 9 8 0

- 1 6 . 6 6 7

9 6 0 8

- 1 5 . 9 6 2

0 7 3 9

- 1 4 . 6 9 0

1 0 8 2

S h r i n k a g e

R i g h t

o u t s i d e

7 . 1

4 2

4 . 3

0 8

4 . 9

0 4

3 . 2

3 9

8 . 2

9 1

6 . 8

6 1

6 . 3

1 9

5 . 4

5 8

R i g h t

m i d d l e

7 . 0

9 6

4 . 2

9 2

4 . 8

8 5

3 . 2

2 9

7 . 9

8 4

6 . 7

3 2

6 . 2

7 8

5 . 4

0 3

L e f t

m i d d l e

7 . 0 9 1

4 . 2 7 4

4 . 8 7 3

3 . 2 3 2

7 . 7 8 1

6 . 7 4 6

6 . 1 7 9

5 . 3 9 5

L e f t

o u t s i d e

7 . 1

3 5

4 . 3

1 2

4 . 9

1 8

3 . 2

3 7

8 . 4

8 3

6 . 9

1 5

6 . 3

5 1

5 . 4

4 9

B o t t o m r o l l

t e m p e r a t u r e

( o C )

1 5

3 5

1 5

3 5

3 5

1 5

3 5

1 5

M i d d l e r o l l


( o C ) 7 0 1 0 0 7 0

1 0 0 7 0

1 0 0 7 0

1 0 0

D i e


( o C )

1 7 0

1 7 0

2 0 0

2 0 0

2 0 0

2 0 0

1 7 0

1 7 0

M e l t

t

e m p e r a t u r e

( o C )

1 7 7

1 7 7

1 9 9

1 9 9

1 7 7

1 7 7

1 9 9

1 9 9

D r a w

r a t i o

1 : 1

1 : 1

1 : 1

1 : 1

2 : 1

2 : 1

2 : 1

2 : 1

R u n

1 2 3 4 5 6 7 8



60

6.1.1 Calculation of Mean of Signal-to-Noise Ratios

The average SNR for different levels of draw ratio, melt temperature, die

temperature, middle roll temperature and bottom roll temperature is presented in Table 6-

2. Main effects plot for SNRs obtained from Minitab is shown in Figure 6.1.

Table 6-2. Response Table for Signal-to-Noise Ratios

Level Draw ratio Melt

temperature

Die

temperature

Middle roll

temperature

Bottom roll

temperature

1 -13.42 -16.15 -15.09 -16.25 -15.55

2 -16.38 -13.66 -14.72 -13.55 -14.26

Delta 2.96 2.49 0.37 2.70 1.29

Rank 1 3 5 2 4

Figure 6.1 Main Effects Plot for Signal-to-Noise Ratios



61

6.1.2 Calculation of Mean of Means

The average of means for different levels of draw ratio, melt temperature, die

temperature, middle roll temperature and bottom roll temperature is presented in Table 6-

3. Main effects plot for means obtained from Minitab is shown in Figure 6.1.

Table 6-3. Response Table for Means

Level Draw ratio Melt

temperature

Die

temperature

Middle roll

temperature

Bottom roll

temperature

1 4.885 6.590 5.780 6.607 6.063

2 6.664 4.959 5.769 4.943 5.487

Delta 1.779 1.631 0.011 1.664 0.576

Rank 1 3 5 2 4

Figure 6.2 Main Effects Plot for Means



62

From the response tables and graphs for SNRs and means it is revealed that draw

ratio is the most significant process parameter followed by middle roll temperature, melt

temperature, bottom roll temperature and the least significant parameter is die temperature.

The optimum value of process parameters is indicated below.

Draw Ratio – 1:1

Melt temperature – 199 oC

Die temperature – 200 oC

Middle roll temperature – 100 oC

Bottom roll temperature – 35 oC

6.2 ANALYSIS OF VARIANCE (ANOVA)

ANOVA is used for testing the significance of the main factors considered by

comparing the mean square against an estimate of the experimental error at specific

confidence level. The ANOVA table for shrinkage is shown in Table 6-4. Table 6-5

presents the model summary.

Table 6-4. Analysis of Variance (ANOVA) for Shrinkage

DF Adj SS Adj MS F-Value P-Value %

contribution

S o u r c e

Draw ratio 1 6.3270 6.32701 32.45 0.029 34.689

Melt temperature 1 5.3195 5.31951 27.29 0.035 29.165

Die temperature 1 0.0002 0.00023 0.00 0.976 0.001

Middle roll temperature 1 5.5395 5.53946 28.41 0.033 30.371

Bottom roll temperature 1 0.6633 0.66326 3.40 0.206 3.637

Error 2 0.3899 0.19495 2.137

Total 7 18.2394

Table 6-5. Model Summary

S R-sq R-sq(adj) R-sq(pred)

0.441535 97.86% 92.52% 65.80%



63

From the ANOVA table it is clear that draw ratio is the most significant parameter

that affects the stress induced during polymer sheet extrusion. Melt temperature and

middle roll temperature have almost equal effect on shrinkage as the percentage

contribution of both these factors is almost same. After that the main contribution is that

of bottom roll temperature. The least influential parameter is the die temperature. The

influence of die temperature can be said to be negligible (0.001%).

6.3

CONFIRMATION EXPERIMENT

Confirmation experiment is the last step in the first iteration of the design of

experiment process. The goal of the confirmation experiment is to validate the conclusion

drawn during the analysis phase. The confirmation experiment is performed by

conducting a test with optimum combination of the factors and their levels determined

statistically (Badole and Pawade, 2009). Table 6-6 presents the optimum values of

extrusion parameters for the conformation experiment. Table 6-7 shows the result of the

confirmation experiment done using optimal extrusion parameters.

Table 6-6. Optimum Values of Extrusion Parameters

Input Process Parameters Optimum Value

Draw ratio 1:1

Melt temperature 199 oC

Die temperature 200 oC

Middle roll temperature 100 oC

Bottom roll temperature 35 oC

Table 6-7 . Result of Conformation Experiment

Shrinkage (%) Average shrinkage (%)

Left outside Left middle Right middle Right outside

3.218 3.187 3.212 3.218 3.209

The average shrinkage obtained from the conformation test is in good agreement

with the minimum shrinkage (experimental run 4) obtained while conducting the

experiment as per L8 orthogonal array; this confirms that the results have excellent

reproducibility.



64

CHAPTER 7

CONCLUSION

7.1

CONCLUSION

Based on the experiments performed it is very evident that proper operating

conditions have a dramatic effect on the stresses induced into the FPVC sheet during

polymer extrusion. This study has presented the application of Taguchi technique to

determine the optimal process parameters for polymer sheet extrusion. The following

conclusions are drawn from the study:

1.

The parameters taken in the experiments are optimized to minimize the stressesinduced in the FPVC sheet during extrusion. The optimum setting of parameters

for polymer sheet extrusion is as following:

Draw ratio = 1:1; Melt temperature = 199 oC; Die temperature = 200 oC; Middle

roll temperature = 100 oC; Bottom roll temperature = 35 oC.

2. Lower draw ratio is found to cause much less shrinkage as compared to higher

draw ratio. It can be inferred from the results that induced stresses get fixed when

sheet gets in contact with a chill roll of extremely low temperature. The

temperature of middle roll should be set in such a way that uniform cooling of the

plastic should be made possible. The plastic must be processed at a temperature

that will allow any stresses that have been induced inside the barrel to be relieved

as much as possible before entering the die.

3. From ANOVA analysis, contribution of each factor to internal stresses induced in

polymer sheet during extrusion is obtained. Draw ratio was found to be the major

factor affecting shrinkage and its contribution is 55%. This reveals that the draw

ratio is the most significant parameter which affects the internal stresses. Melt

temperature and middle roll temperature also have significant influence on the

stresses induced in polymer sheet. The effect of die temperature on internal

stresses is found to be negligible.



65

4. The shrinkage (3.209% ) observed in the confirmation test is in agreement with

the value of shrinkage (3.234%) obtained with same levels of parameters while

conducting the series of experiments as per L8 orthogonal array. This indicates the

robustness of the experimental design and confirms that the results have excellent

reproducibility.

7.2 FUTURE SCOPE

Internal stresses induced in polymer sheet during extrusion causes great problems

for the next in-house processes such as thermoforming. The finished product may appear

acceptable but when it get exposed to any thermal history again any internal stresses that

was processed into the product becomes evident which may lead to customer

dissatisfaction. No specific methodology had been mentioned in the literature to

reduce/minimize the internal stresses induced during polymer sheet extrusion. This study

provides adequate information regarding the input process parameters to minimize the

internal stresses in polymer sheet extrusion. The presence of internal stresses reduces the

polymer stability which may lead to polymer degradation (Shur et al., 1978; Grassie and

Scott, 1985; Arisawa and Porter, 1970). Polymer degradation affects the productivity to a

great extent. Therefore it is great necessary to make sure that all precautions are taken to

alleviate the unwanted stresses. Residual stresses are induced in all kind of plastic

manufacturing process including injection molding, machining, calendaring and vacuum

forming. So this study could well be replicated for other processes also in such a way that

suits them.



66

REFERENCES

Agassant, J.F., Avenas, P. and Sergent, J. (1996) ‘La mise en forme des matiéres

plastiques’, Lavoisier , 3rd edition, Paris.

Al Rozuq, R. and Al Robaidi, A. (2013) ‘Application of neural network (ANN) to

predict XLPE cable in extrusion processes’, Journal of Materials Sciences and

Applications.

Arisawa, K. and Porter, R.S. (1970) ‘The degradation of polystyrene during

extrusion’, Journal of Applied Polymer Science, Vol. 14, No. 4, pp. 879–896.

Badole, C.M. and Pawade, R.S. (2009) ‘Behaviour of CVD PVD coated carbide tools in

high speed turning of Inconel 718’, In National Conference on World-class

Manufacturing , Amrutvahini College of Engineering, Sangamner, University of

Pune.

Bata, G.L. (1984) ‘The Archimedean screw as an extruder; historical note’, Polymer

Engineering and Science, Vol. 24, No. 9, pp. 624-625.

Bendell, T. (1988) ‘Taguchi methods’, Proceedings of 1988 European Conference,

Elsevier Applied Science, Amsterdam.

Bertsekas, D.P. (1999) ‘Nonlinear programming’, Athena Scientific, 2nd edition,

Cambridge MA

Bharti, P.K. and Khan, M.I. (2010) ‘Recent methods for optimization of plastic injection

moulding process- A retrospective and literature review’, International Journal of

Engineering, Science and Technology, Vol. 2, No. 9, pp. 4540-4554.

Bouvier, J-M. and Campanella, O.H. (2014) ‘Extrusion processing technology: food and

non-food biomaterials’, Wiley- Blackwell , New York.

Box, G.E.P and Draper, N.R. (1987) ‘Empirical model-building and response surfaces’,

Wiley, New York.

Byrne, D.M., Taguchi, G. (1987) "The Taguchi approach to parameter design", Quality

Progress, Vol.20, No.12, pp.19-26.



67

Chan, T.W.D. and Lee, L.J. (1989) ‘Orientation and residual stress distribution in plastic

sheet extrusion’, ANTEC '89.

Cirak, B. and Kozan, R. (2009) ‘Prediction of the coating thickness of wire coating

extrusion processes using artificial neural network (ANN)’, Modern Applied

Science, Vol. 3, No. 7, pp. 52-66.

Giunta, A.A., Balabanov, V., Haim, D., Grossman, B., Mason, W.H., Watson, L.T. and

Haftka, R.T. (1996) ‘Wing design for a high-speed civil transport using a design

of experiments methodology’, AIAA paper 96-4001-CP, Proceedings of 6 th

AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization,

Bellevue WA, Part 1, pp. 168-183.

Grassie, N. and Scott, G. (1985) ‘Polymer degradation and stabilisation’, Cambridge

University Press, London.

Huang, H.-X. and Liao, C.-M. (2002) ‘Prediction of parison swell in plastics

extrusion blow molding using a neural network method’, Polymer Testing , Vol.

21, pp. 745-749.

Lebaal, N., Puissant, S. and Schmidt, F. (2010) ‘Application of a response surface

method to the optimal design of the wall temperature profiles in extrusion die’,

International Journal of Material Forming , Vol. 3, No. 1, pp. 47-58.

Liu, X., Li, K., McAfee, M., Nguyen, B.K. and McNally, G.M. (2012) ‘Dynamic gray-

box modeling for on-line monitoring of polymer extrusion viscosity’, Polymer

Engineering and Science, Vol. 52, No. 6, pp. 1332-1341.

Mahajan, M. (2007) ‘Statistical Quality Control’, Dhantar Rai & Co. (P) Ltd.

Mamalis, A.G., Vortselas, A.K. and Kouzilos, G. (2012) ‘Tube-extrusion of polymeric

materials: optimization of processing parameters’, Journal of Applied Polymer

Science, Vol. 126, No. 1, pp. 186-193.

Mathivanan, D., Nouby, M. and Vidhya, R. (2010) ‘Minimization of sink mark defects in

injection molding process- Taguchi approach’, International Journal of

Engineering, Science and Technology, Vol. 2, No. 2, pp. 13-12.



69

Rauwendaal, C. (1986) ‘Polymer extrusion’, Hanser Publishers, Munich.

Sheet Producers Division Technical Committee (SPD) (2001) ‘A guideline for a test

method for orientation of plastic sheeting by means of a hot air oven’, The Society

of the Plastics Industry, Inc., Washington, D.C.

Shur, Y.J., Ranby, B., Chung, K-H. and Kim, S-D. (1978) ‘Kinetic studies on shear

degradation of polystyrene during extrusion’, Polymer Engineering & Science,

Vol. 18, No. 10, pp. 812–816.

Spina, R. (2006) ‘Optimization of injection moulded parts by using ANN-PSO approach’,

Journal of Achievements in Materials and Manufacturing Engineering , Vol. 15,

No. 1-2, pp.146-152.

Staats, E.F. ‘An empirical relationship for analysing engineering materials made from

flexible plastic heat shrinkage films’, a thesis presented to the graduate faculty of

the University of Akron, December, 1972.

Stevens, M.J. and Covas, J.A. (1995) ‘Extruder principles and operation’, Chapman &

Hall , 2nd edition, London.

Steward, E.L. (1999) ‘Control of melt temperature on single screw extruders’,

SPE/ANTEC 1999 Proceedings.

Swan, D.A. and Savage, G.J. (1998) ‘Continuous Taguchi- a model based approach to

Taguchi’s quality by design with arbitrary distribution’, Quality and Reliability

Engineering International, Vol. 14, No. 1, pp. 29–41.

Taguchi, G. (1986) ‘Introduction to quality engineering: Design quality into product and

process’, Asian productivity Organization, Tokyo.

Taguchi, G. and Konishi, S. (1987) ‘Orthogonal Arrays and Linear Graphs’, American

Supplier Institute Inc., Dearborn, MI.

The Society of Plastic Engineers (2004) ‘Fundamental skills and polymer sciences’,

Ronjon publishing .

Toropov, V.V., van Keulen, F., Markine, V.L. and de Boer, H. (1996) ‘Refinements in the

multi-point approximation method to reduce the effects of noisy responses’, 6th



70

AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and

Optimization, Bellevue WA, Part 2, pp. 941-951.

Unal, R. and Dean, E.B. (1991) ‘Taguchi approach to design optimization for quality and

cost: an overview’, In Annual Conference of the International Society of

Parametric Analysts.

Van Campen, D.H., Nagtegaal, R. and Schoofs, A.J.G. (1990) ‘Approximation methods

in structural optimization using experimental designs for multiple responses’, In:

Eschenauer, H., Koski, J., Osyczka, A. (Ed.), Multicriteria Design Optimization,

Springer-Verlag , Berlin, Heidelberg, New York, pp. 205-228.

Venter, G., Haftka, R.T. and Starnes, J.H. (1996) ‘Construction of response surfaces for

design optimization applications’, AIAA paper 96-4040-CP, Proceedings of 6 th

AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization,

Bellevue WA, Part 2, pp. 548-564.

Wellstead, P.E, Health, W.P. and Kjaer, A.P. (1998) ‘Identification and control of web

processes; polymer film extrusion’, Control Engineering Practice, Vol. 6, No. 3,

pp. 321-331.

White, J.L. (1990) ‘Twin Screw extrusion; technology and principles’, Hanser , Munich.

Willie, R. (1990) ‘Landing gear weight optimization using Taguchi analysis’, In 49th

Annual International Conference of Society of Allied weight Engineers Inc,

Chandler, AR.

Yu, J-C., Chen, X-X., Hung, T-R. and Thibault, F. (2004) ‘Optimization of extrusion

blow molding processes using soft-computing and Taguchi's method’ , Journal of

Intelligent Manufacturing , Vol. 15, pp. 625-634.

Zadeh, L. (1996) ‘Fuzzy logic equals computing with words’, IEEE Transactions on

Fuzzy Systems, Vol. 4, No. 2, pp. 103–111.

Zadeh, L. (1997) ‘Toward a theory of fuzzy information granulation and its centrality in

human reasoning and fuzzy logic’, Fuzzy Sets and Systems, Vol. 90, No. 2, pp.

111–127.

Zadeh, L. (1965) ‘Fuzzy sets’, Information and Control , Vol. 8, No. 3, pp. 338–353.



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http://www.ijetae.com/files/Volume3Issue4/IJETAE_0413_40.pdf

1% match (Internet from 17-Oct-2010)

http://www.phadkeassociates.com/fundamentals /Phadke_Fundamentals_Introduction_To_Robust_Design_Taguchi_Method.pdf

1% match (Internet from 02-Jun-2014)

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.8388&rep=rep1&type=pdf

1% match (Internet from 04-Jan-2011)

http://www.chemicalfabricsandfilm.com/pdfs_researchSection/techSupport/shrinkage.pdf


A. G. Mamalis. "Tube extrusion of polymeric materials: Optimization of the processingparameters", Journal of Applied Polymer Science, 2012

1% match (Internet from 26-Sep-2012)

http://cms3.minitab.co.kr/board/minitab_data/7.%20DesignofExperimentsAllTopics.pdf


Azadegan, A.. "Fuzzy logic in manufacturing: A review of literature and a specializedapplication", International Journal of Production Economics, 201108

1% match (Internet from 13-Aug-2012)

http://www.adskerala.com/Kerala_directory/Medical_Supplies__Equipments /TERUMO_PENPOL_Limited_2330.htm

nitin Originality Report https://turnitin.com/newreport_printview.asp?eq=0&eb=0&esm=0&oi...

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10

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12

13

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15

16

17

18

19

20

21

1% match (student papers from 22-Dec-2009)

Submitted to BITS, Pilani-Dubai on 2009-12-22


T.W. Womer. "Optimizing Sheet Extrusion Conditions to Minimize Internal Stresses inThermoformed Sheet", Journal of Plastic Film and Sheeting, 01/01/1992

1% match (Internet from 24-Sep-2009)

http://www.shsu.edu/~mth_fbb/stat533/projectf2005/aruna_s.pdf


Patel, K.M.. "Mechanics of machining of face-milling operation performed using aself-propelled round insert milling cutter", Journal of Materials Processing Tech., 20060110

1% match ()

http://www2.sas.com/proceedings/sugi30/209-30.pdf

1% match (student papers from 11-Jan-2014)

Submitted to Indian Institute of Technology, Madras on 2014-01-11


Yu, Jyh-Cheng Chen, Xiang-Xian Hung, Tsu. "Optimization of extrusion blow molding

processes using soft computing and Taguchi's method.(Author a", Journal of Intelligent Manufacturing,Oct 2004 Issue


Submitted to Rochester Institute of Technology on 2013-12-04


Nadhir Lebaal. "Application of a response surface method to the optimal design of the walltemperature profiles in extrusion die", International Journal of Material Forming, 06/28/2009

1% match (Internet from 08-Apr-2010)

http://iesu5.ieem.ust.hk/dfaculty/ajay/courses/ieem317/lecs/robust/robustdesign1.pdf


Yue Mu. "An optimization strategy for die design in the low-density polyethylene annularextrusion process based on FES/BPNN/NSGA-II", International Journal of Advanced ManufacturingTechnology, 02/20/2010

1% match (Internet from 01-Jan-2011)

http://www.thefreelibrary.com /Application+of+Taguchi+and+response+surface+methodology+for+biodiesel...-a0216182261


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22

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26

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28

29

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31

32

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34

1% match (student papers from 07-Mar-2013)

Submitted to University of South Africa on 2013-03-07

1% match ()

http://www.penpolindia.com/whoweare.html


Submitted to Institute of Graduate Studies, UiTM on 2012-12-18

1% match (Internet from 04-Aug-2006)

http://thequalityportal.com/p_diagram.htm

1% match (Internet from 07-May-2009)

http://www1.ximb.ac.in/users/fac/Pkbala/pkbala.nsf/23e5e39594c064ee852564ae004fa010 /4846b870e9353a7d65257352005f9129/$FILE/Taguchi%20Methods__.doc


S. L. Mok. "Review of research in the determination of process parameters for plastic injectionmolding", Advances in Polymer Technology, 1999

< 1% match (Internet from 12-Dec-2009)

http://www.managementparadise.com/forums/archive/index.php/t-16317.html

< 1% match (Internet from 29-Apr-2014)

http://polymerprojecttopics.blogspot.co.uk/2010/08/pvc-blood-bag.html

< 1% match (student papers from 28-Jan-2013)

Submitted to Jawaharlal Nehru Technological University on 2013-01-28

< 1% match (student papers from 07-Nov-2013)

Submitted to University of Adelaide on 2013-11-07

< 1% match (Internet from 05-Mar-2007)

http://www.wire.tu-bs.de/forschung/projekte/platon/documents/gridreview.pdf

< 1% match (Internet from 07-May-2014)

http://www.docstoc.com/docs/122600643 /ScienceThermoplasticsandThermoplasticCompositesTechnicalInformationforPlastics

< 1% match (student papers from 16-Oct-2013)

Submitted to University of South Australia on 2013-10-16


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47

< 1% match (Internet from 07-Feb-2012)

http://www.ami.ac.uk/courses/topics/0217_npit/index.html


http://pubs.sciepub.com/ajvd/1/2/3/

< 1% match (student papers from 18-Feb-2011)

Submitted to Higher Education Commission Pakistan on 2011-02-18


http://terumopenpol.com/

< 1% match (publications)

Tosun, N.. "A study on kerf and material removal rate in wire electrical discharge machiningbased on Taguchi method", Journal of Materials Processing Tech., 20041030

< 1% match (Internet from 04-Aug-2003)

http://www.research.ge.com/cooltechnologies/pdf/2000crd120.pdf

< 1% match (Internet from 01-Mar-2012)

http://www.netl.doe.gov/technologies/oil-gas/EP_Technologies/ReservoirChar/Modeling /Modeling.html


Y. S. Tarng. "Application of the Taguchi Method to the Optimization of the Submerged ArcWelding Process", Materials and Manufacturing Processes, 5/1/1998


Hamdy Hassan. "3D study on the effect of process parameters on the cooling of polymer byinjection molding", Journal of Applied Polymer Science, 2009


http://www.eng.warwick.ac.uk/~esraf/ES380/es380%202005.pdf


http://en.wikipedia.org/wiki/Plastics_extrusion

< 1% match ()

http://202.138.126.249/cmshtml/extrusion%20principles.pdf?page_id=1112



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48

49

50

51

52

53

54

55

56

57

58

59

Submitted to Caledonian College of Engineering on 2013-01-17


http://ijltet.org/wp-content/uploads/2013/07/67.pdf


Y. S. Tarng. "The Use of Fuzzy Logic in the Taguchi Method for the Optimisation of theSubmerged Arc Welding Process", The International Journal of Advanced Manufacturing Technology,07/05/2000

< 1% match (student papers from 22-Mar-2013)

Submitted to Atilim University on 2013-03-22


http://www.coursehero.com/file/187520 /McGrawHillDesignForSixSigmaARoadmapForProductDevelopment/


Chanda, . "Fabrication Processes", Plastics Fabrication and Recycling, 2008.

< 1% match (Internet from 18-Sep-2012)

http://tijst.net/issues/2010/no3/2010_V15_No3_1.PDF


Guerrero-Cusumano, J.L.. "Multivariate exponential families and the Taguchi loss function",Information Sciences, 199904


Lebaal, Nadhir Schmidt, Fabrice Puissant. "Design of optimal extrusion die for a range ofdifferent materials.(Technical report)", Polymer Engineering and Science, March 2009 Issue


http://www.ijser.org/ResearchPaperPublishing_December2012_Page2.aspx

< 1% match (Internet from 13-Jul-2010)

http://www.arabrise.org/articles/A040203K.pdf


http://en.wikipedia.org/wiki/Nonlinear_programming


http://www.cs.iusb.edu/thesis/NBradley_thesis.pdf


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< 1% match (Internet from 07-Jan-2010)

http://business.kosmix.com/topic/Minitab


http://www.eng.auburn.edu/~maghsood/INSY7330 /Strength%20and%20Limitations%20of%20Taguchi's%20Contributions.pdf


Submitted to Republic Polytechnic on 2013-01-15

< 1% match (Internet from 21-Nov-2009)

http://www.plasticsheet.org/Guide/HOTAIR.pdf


http://injectionmoldinginfo.org/manufacturing-costs-injection-molding/


Cybulski, . "Plastic Extrusion", Plastic Conversion Processes A Concise and Applied Guide,2009.


"4 Processing of plastics", Progress in Polymer Science, 1981


http://sauditime.com/kg/tools/taguchi.htm


Hernandez, Pablo Ayala. "Neural-Fuzzy Approach to Optimize Process Parameters forInjection Molding Machine", 2012 Eighth International Conference on Intelligent Environments, 2012.

< 1% match (Internet from 31-Mar-2009)http://www.asq-harrisburg.org/news.htm

< 1% match (student papers from 29-Jun-2011)

Submitted to VIT University on 2011-06-29


http://www.brad.ac.uk/staff/vtoropov/burgeon/thesis_luis/chapter3.pdf


Yang, K.. "Application of Taguchi method in optimization of cervical ring cage", Journal of


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Biomechanics, 2007


http://blog.continuous-extrusion-machine.com/2012/12/


H.-X. Huang. "Neural Modeling of Parison Extrusion in Extrusion Blow Molding", Journal ofReinforced Plastics and Composites, 07/01/2005

< 1% match (student papers from 16-Feb-2014)

Submitted to Athlone Institute of Technology on 2014-02-16


http://injectionmoldinginfo.org/production-cost-injection-molding/

< 1% match (student papers from 02-Mar-2014)

Submitted to University of Witwatersrand on 2014-03-02


Submitted to Georgia State University on 2014-01-16

< 1% match ()

http://nova.postech.ac.kr/~dkim/course/ece521/fuzzy_tb.pdf


Abdelati Elalem. "Reduction of Automobile Carbon Dioxide Emissions", International Journalof Material Forming, 04/2010


http://www.google.co.uk/search?q=extrusion+process&hl=en&rlz=1R2SNYS_enGB


Sanchez, Elie, Takanori Shibata, and Lotfi A. Zadeh. "FRONT MATTER", Advances in FuzzySystems - Applications and Theory, 1997.


http://www2.nkfust.edu.tw/~jcyu/WebsiteData/Website/CED_Lab /doc/CSCWD01_FUNTGA.pdf

< 1% match (student papers from 03-Mar-2014)Submitted to University of Witwatersrand on 2014-03-03


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S. Mirzamohammadi. "Study of corrosion resistance and nanostructure for tertiaryAl

2O

3 /Y

2O

3 /CNT pulsed electrodeposited Ni based nanocomposite", Transactions of the Institute of

Metal Finishing, 03/01/2010

< 1% match (student papers from 09-Jul-2012)

Submitted to Universiti Teknologi Petronas on 2012-07-09

< 1% match (Internet from 22-Jul-2010)

http://www.slideshare.net/siddharth4mba/doe-design-of-experiments-979738


Fei, Ng Chin, Nik Mizamzul Mehat, and Shahrul Kamaruddin. "Practical Applications ofTaguchi Method for Optimization of Processing Parameters for Plastic Injection Moulding: A

Retrospective Review", ISRN Industrial Engineering, 2013.


Sehijpal Singh. "Parametric optimization of magnetic-field-assisted abrasive flow machiningby the Taguchi method", Quality and Reliability Engineering International, 07/2002

< 1% match (student papers from 01-Oct-2012)

Submitted to Middle East Technical University on 2012-10-01


Fisher, Thomas W., III. "Fix thickness variations in extruded sheet.(Troubleshooter:EXTRUSION)", Plastics Technology, August 2005 Issue


http://wikipedia.7val.de/wiki/Plastics_extrusion


Hsien, Kuang-Hung, and Shyh-Chour Huang. "Application of Taguchi method to robust multi—criteria optimum design for ultra-thin centrifugal fan", 2011 IEEE International Conference on SystemsMan and Cybernetics, 2011.

< 1% match (student papers from 17-May-2013)

Submitted to University Tun Hussein Onn Malaysia on 2013-05-17


http://www.minitab.com.tw/Download/MSS17GettingStarted.pdf



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Method", Materials and Manufacturing Processes, 09/2010


Wei-Jaw Deng. "An Effective Approach for Process Parameter Optimization in InjectionMolding of Plastic Housing Components", Polymer-Plastics Technology and Engineering, 09/2008

< 1% match (publications)Yang. "Reliability Improvement Through Robust Design", Life Cycle Reliability Engineering,

01/01/2007


Elgomati, H. A.; Majlis, B. Y.; Ahmad, I.; Salehuddin, F.; Hamid, F. A.; Zaharim, A. and Apte,P. R.. "Application of Taguchi Method in the Optimization of Process Variation for 32nm CMOSTechnology", Journal of Applied Sciences Research, 2011.

< 1% match (Internet from 22-Oct-2009)http://www.phadkeassociates.com/index_files

/Introduction%20to%20Robust%20Design%20-%20Phadke.pdf

< 1% match (Internet from 04-Jun-2012)

http://medlibrary.org/medwiki/Plastics_extrusion


http://ajse.kfupm.edu.sa/articles/342B_P.17.pdf

< 1% match (Internet from 02-Jun-2014)

http://www.technicaljournalsonline.com/ijaers/VOL%20II /IJAERS%20VOL%20II%20ISSUE%20IV%20JULY%20SEPTEMBER%202013/325.pdf


http://www.cqm.rs/ijqr/journal/v6-n2/7.pdf


http://era.teipir.gr/era4/abstr/b58.doc

< 1% match (Internet from 29-Oct-2010)

http://www.scipub.org/fulltext/ajeas/ajeas31207-213.pdf


http://www.ias.ac.in/sadhana/Pdf2005Dec/PE1312.pdf


http://ijltet.org/wp-content/uploads/2013/01/10.pdf


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Raguraman, C. M., A. Ragupathy, and L. Sivakumar. "Experimental Determination of HeatTransfer Coefficient in Stirred Vessel for Coal-Water Slurry Based on the Taguchi Method", Journal ofEngineering, 2013.


Cao, F.. "Neural network modelling and parameters optimization of increased explosiveelectrical discharge grinding (IEEDG) process for large area polycrystalline diamond", Journal ofMaterials Processing Tech., 20040610


Sun, Z.. "Numerical optimization of gating system parameters for a magnesium alloy castingwith multiple performance characteristics", Journal of Materials Processing Tech., 20080401


Lan, S.. "A parameter study on the micro hot-embossing process of glassy polymer forpattern replication", Microelectronic Engineering, 200912


A. M. Jinturkar. "Determination of water quality index by fuzzy logic approach: a case ofground water in an Indian town", Water Science & Technology, 04/2010


Ming-Shyan Huang. "Effect of rapid mold surface inducting heating on the replication abilityof microinjection molding light-guided plates with V-grooved microfeatures", Journal of AppliedPolymer Science, 12/05/2010


El-Haik. "Robust Parameter Design for Medical Devices", Medical Device Design for SixSigma, 04/04/2008



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