fabric engineering for ultraviolet radiation protection

FABRIC ENGINEERING FOR ULTRAVIOLET

RADIATION PROTECTION

PIYALI HATUA

DEPARTMENT OF TEXTILE TECHNOLOGY

INDIAN INSTITUTE OF TECHNOLOGY DELHI

SEPTEMBER 2015

FABRIC ENGINEERING FOR ULTRAVIOLET

RADIATION PROTECTION

by

PIYALI HATUA

Department of Textile Technology

Submitted

in fulfilment of the requirements of the degree of

Doctor of Philosophy

to the

INDIAN INSTITUTE OF TECHNOLOGY DELHI

SEPTEMBER 2015

Dedicated to my family

i

CERTIFICATE

This is to certify that the thesis titled ‘Fabric Engineering for Ultraviolet Radiation

Protection’, being submitted by Mrs. Piyali Hatua to the Indian Institute of Technology

Delhi, for the award of the degree of Doctor of Philosophy, is a record of bonafide research

work carried out by her. She has worked under our guidance and supervision and fulfilled the

requirements for submission of the thesis which has attained the standard required for a Ph.D.

degree of this Institute.

The results contained in this thesis have not been submitted, in part or in full, to any

other university or institute for the award of any degree or diploma.

Dr. Abhijit Majumdar Dr. Apurba Das

Associate Professor Professor

Department of Textile Technology Department of Textile Technology

Indian Institute of Technology Delhi Indian Institute of Technology Delhi

New Delhi 110016, India New Delhi 110016, India

New Delhi

Dated:

iii

ACKNOWLEDGEMENTS

It is a great pleasure for me to express my deep sense of gratitude to my supervisors

Dr. Abhijit Majumdar and Prof. Apurba Das for their constant interest, invaluable

supervision, continuous encouragement and cooperation throughout this research work. I am

really indebted to them for their invaluable guidance and support that they provided right

from the inception to the successful completion of this endeavour.

My sincere gratitude also goes to the members of my research committee Prof. V. K.

Kothari, Prof. M. Joshi and Prof. S. N. Maiti (Centre of Polymer Science and Engineering,

IIT Delhi), who have contributed significantly to the progress of my research work. I express

my gratitude to previous and present Head of the Department, Prof. Kushal Sen and Prof.

Ravi Chattopadhyay for providing all kinds of facilities. I also express my sincere gratitude to

all other faculty members of Department of Textile Technology, IIT Delhi for their

invaluable moral and technical support and assistance. I would like to specially thank Dr.

Dipayan Das and Dr. B. S. Butola of Department of Textile Technology, IIT Delhi for their

invaluable suggestions and support for my research work. In this occasion, I would also like

to acknowledge the contribution of all my teachers who inspired, motivated and helped me a

lot at various stages of my academic life. My sincere gratitude goes to Mr. S. Ghosh, Mr. S.

Roy, Mr. A. Chakraborty, Dr. A. Ghosh, Dr. P. K. Majumdar, Dr. S. C. Roy and Mrs. M.

Datta.

I express my sincere thanks to the staff members of all the laboratories and offices of

Department of Textile Technology, IIT Delhi for extending their helping hand whenever

needed. My sincere thank goes to Mr. M. Kundu, Mr. P. Singh, Mr. B. Biswal, Mr. M. Singh,

Mr. V. A. Passi, Mr. Jagdish, Mr. Suresh, Mr. R. K. Tejania, Mr. S. Sharma, and Mr. A. K.

Sehgal for their kind and enthusiastic cooperation.

iv

I am also thankful to Auro Spinning Mills, Vardhman Group, Baddi, Himachal

Pradesh, India for supplying all the yarn samples used in this research work.

I am thankful to the Council of Industrial and Scientific Research (CSIR) for

providing financial assistance through project.

I also express my heartfelt thanks to all my friends, PhD seniors, colleagues and lab-

mates, who supported and helped me a lot to make this project successful. Here specially I

would like to express a warm gratitude to my friends Rashmi and Ratyakshi for their loving

friendship, unconditional support and guidance, both in my professional as well as personal

life. I acknowledge their contribution from the bottom of my heart. I am thankful to my

labmates Dr. K. Shabaridharan, Mr. R. Baldua, Dr. B. Kumar, Mr. J. Krishnasami, Mr. S.

Chakraborty, Ms. S. Renuka and Mr. M. Ramamoorthy. I am also thankful to Dr. S. Maiti,

Dr. M. Bera, Dr. A. K. Pradhan, Mr. N. Srivastava, Mr. S. B. Pol, Mr. S. Thakur, Ms. U.

Chatterjee, Mr. A. Laha, Mr. S. Alam, Ms. S. Arora, Mr. R. Pal, Ms. S. Ghosh, Mr. A. K.

Mondal, for their cooperation and help whenever I needed.

Last but not the least, my hearty thanks goes to my whole family. I am grateful to my

parents Mr. Sukumar Hatua and Mrs. Mina Hatua for their immense love, support and

guidance in my life. I am really thankful to my sister Ms. Priyanka Hatua and my uncle’s

family Mr. Asit Hatua, Mrs. Mousumi Hatua, Ms. Papri Hatua and Ms. Pousali Hatua for

their unconditional love and support. I would also like to thank my parents-in-law Mr. Jiten

Mondal and Mrs. Arati Mondal for their love and support. Lastly, I am thankful to my

husband Mr. Tanmay Mondal for the sacrifice he made for my research. Living apart, he

continuously gave motivation, support and guidance throughout the whole tenure of my

work. I am really indebted to him for all those pains he bore during this time.

Piyali Hatua

v

ABSTRACT

Ultraviolet rays (UVR) are human carcinogen. Over-exposure to UVR from solar or artificial

origin has a potential risk to human health. Hence adequate level of skin protection from

UVR is essential. Use of clothing is the most simple and convenient way of UV protection.

UV protective properties of fabrics and clothing are evaluated by their ultraviolet protection

factor (UPF).

The UV protection characteristics of almost all known apparel fibres such as cotton,

silk, polyester etc. had been explored by various researchers. Recently, regenerated bamboo

viscose fibre has emerged as a promising material for the apparel use. From the results

reported in literatures, conflicting views regarding the better UV protection property of

bamboo viscose with respect to that of natural cotton have emerged. A systematic

investigation on the in vitro UV protection property of bamboo viscose fibre with respect to

that of natural cotton was carried out to clarify this conflict.

Fabric samples were produced from cotton and bamboo viscose yarns and their UPF

were tested. The UPF of both cotton and bamboo viscose fabrics was predicted by empirical

models using fabric cover % and areal density as input variables. A comparative analysis of

UPF was carried out between cotton and bamboo viscose fabrics using curve fitting

technique. The analysis showed that the apparently higher UPF of bamboo viscose fabrics

can be attributed to their higher areal density and cover resulting from higher shrinkage

though the fabrics were woven using same yarn count and thread density. The areal density

based UPF predictive model developed for cotton performed equally well for bamboo viscose

fabrics bolstering the fact that there is no distinguishable differences in the UV protection

property of these two fibres. The fabrics woven from cotton and bamboo viscose yarns and

vi

having similar cover % and areal density showed similar UPF and UV transmittance

behaviour.

Yarn parameters such as twist may have significant influence on the UV protection

property of fabric. Also hollow yarn structure can provide higher cover % due to its higher

degree of flattening, which in turn can offer better UV protection. Among the fabric

parameters, fabric cover is the most important parameter which determines the UPF.

However, for the same fabric cover, fabric thickness and individual pore size may play a

decisive role in influencing the UPF. The effects of yarn twist level and hollow yarn structure

on UPF of cotton woven fabrics were investigated in this research. The influences of fabric

thickness and individual pore size on UPF were explored keeping the fabric cover same.

Yarn samples were produced varying twist in three levels (3.6, 3.9 and 4.2). Fabric

samples were produced keeping all parameters constant except yarn twist. No discernible

effect of yarn twist on cotton woven fabric UPF was observed within the experimental range.

To produce hollow yarn structure, first core-spun yarn was manufactured in ringframe

using polyvinyl alcohol (PVA) multifilament as the core. Fabric samples were produced from

the core-spun yarn. PVA was washed out from the core of yarn during preparatory wet

processing treatment of fabric samples, leaving a hollow yarn structure in the fabric. Fabrics

with hollow yarn structure resulted higher UPF in comparison with the fabrics made from

normal yarn due to enhanced fabric cover arising from higher yarn flattening. The difference

was more prominent at higher cover %. Also the enhancement in UPF, due to the use of

hollow yarn structure, was amplified after UV absorber treatment of the samples.

Fabric thickness was varied keeping cover % constant by maintaining the ratio of

diameter and spacing of both warp and weft yarns constant. This study was carried out using

100% polyester and 100% cotton yarns. UPF of polyester fabrics decreased with the

decreasing fabric thickness when fabric cover was kept at same level. After UV absorber

vii

treatment, UPF of polyester fabrics first decreased and then increased with the decreasing

fabric thickness. On the other hand, no significant change in UPF of cotton fabrics was

observed with the decreasing fabric thickness at constant cover %. After UV absorber

treatment, cotton fabrics showed similar trend like polyester fabrics at lower cover (88%).

However, UPF increased continuously with decreasing thickness at higher levels of cover (93

and 96 %). The individual pore size in the fabric along with fabric thickness and inherent UV

protective property of the fibre materials played a crucial role in determining the UPF of

fabrics.

Attempt was made to predict the UPF of woven fabrics using different modeling

methodologies, namely nonlinear regression, artificial neural network (ANN) and adaptive

network based fuzzy inference system (ANFIS). Thirty-six fabrics were produced by varying

the proportion of polyester, weft count and pick density at three levels. Nonlinear regression,

ANN and ANFIS models were developed using 27 training datasets. Nine testing datasets

were used for evaluating the prediction performance of the models. The ANN model

demonstrated the best prediction performance, among the three developed models, followed

by ANFIS. Analysis of the developed regression, ANN and ANFIS models showed that UPF

improves with higher proportion of polyester, coarser yarn count and higher pick density.

ANN model was then used in combination with genetic algorithm (GA) for the fabric

engineering. The aim of fabric engineering is to choose the optimum combination of input

parameters i.e. fabric construction parameters so that the targeted fabric properties are

achieved. In this research, fabric engineering for ultraviolet protection and comfort properties

(air permeability and MVTR) was attempted using hybrid ANN-GA system. ANN was used

for developing predictive models as it yielded best prediction result for UPF. GA was used as

a tool for solving the optimization problem.

viii

Forty-two fabric samples were produced varying six parameters, namely polyester

fibre blend in warp and weft, warp and weft count, end and pick density. Separate ANN

models were developed for UPF, air permeability and MVTR using the test results pertaining

to 36 samples. Four feasible combinations of UPF, air permeability and MVTR were chosen

from the Pareto charts of UPF-air permeability and UPF-MVTR. The fitness function of GA

was so formed that the targeted UPF, air permeability and MVTR can be achieved. Penalty

function method was adopted to convert the constrained optimization problem into

unconstrained problem by combining the objective (UPF) and constraints (air permeability

and MVTR). The developed ANN-GA hybrid system was run to elicit the optimized set of

input parameters for achieving the targeted fabric properties. The hybrid ANN-GA system

was accurate in achieving target fabric properties but lacked precision in terms of input

parameter combinations. Further, to counter this problem, end and pick densities were

searched considering the availability of the yarns (polyester blend and count) in the

warehouse. The ANN-GA system demonstrated accuracy as well as precision in this case. To

validate the developed ANN-GA based fabric engineering system, four fabric samples were

woven using the optimized set of input parameters and their functional properties were

measured. The targeted and achieved properties of four validation samples were in

reasonably good agreement as the mean error for UPF, air permeability and MVTR were 8.3,

14.9 and 2.1 %, respectively.

ix

TABLE OF CONTENTS

Certificate i

Acknowledgements iii

Abstract v

Table of contents ix

List of figures xix

List of tables xxiii

Chapter 1 Introduction 1

1.1 Ultraviolet protection by fabrics 3

1.2 Fabric engineering for ultraviolet protection and comfort 4

1.3 Motivation for the work 5

1.4 Objectives 6

1.5 Organization of the thesis 7

Chapter 2 Literature review 9

2.1 Introduction 11

2.2 Ultraviolet radiation 11

2.3 Classification of ultraviolet radiation 12

2.4 Need for UV protection 13

2.4.1 Effects on skin 14

2.4.2 Effects on immune system 15

2.4.3 Effects on eye 16

2.5 Protection against solar UV radiation 16

x

2.5.1 Behavioural changes 16

2.5.2 Environment and legislation changes 16

2.5.3 Personal protection 17

2.6 Terms used for labeling 18

2.6.1 Ultraviolet protection factor (UPF) 18

2.6.2 Sun protection factor (SPF) 19

2.6.3 UV index (UVI) 20

2.7 Standards for UPF 22

2.7.1 Australian/New Zealand standard 22

2.7.2 United Kingdom standard 23

2.7.3 United States standards 24

2.7.4 European standards 25

2.8 Methods for determination of UPF 25

2.8.1 In vivo method 26

2.8.2 In vitro method 26

2.9 Effects of various textile parameters on UPF 27

2.9.1 Effects of fibre related factors 29

2.9.2 Effects of yarn related factors 31

2.9.3 Effects of fabric related factors 32

2.9.3.1 Effects of weave and fabric tightness 34

2.9.3.2 Effect of cover factor 36

2.9.3.3 Effect of volume porosity 39

2.9.3.4 Effects of areal density and thickness 40

2.9.3.5 Effect of colour 41

2.9.3.6 Effects of UV absorber and other additives 44

xi

2.9.3.7 Effect of laundering 46

2.9.3.8 Effect of stretch 47

2.9.3.9 Effect of moisture and swelling 48

2.9.3.10 Effect of layering 49

2.10 Some unconventional ways for improving UV protection 50

2.11 Ultraviolet protection and comfort 51

2.12 Modeling of UPF 51

2.13 Soft computing methods and its application in woven fabric

engineering

53

2.13.1 Soft computing methods 53

2.13.1.1 Artificial neural network 54

2.13.1.2 Fuzzy logic 57

2.13.1.3 Genetic algorithm 59

2.13.1.4 Hybrid systems 61

2.13.2 Soft computing applications in fabric property prediction 61

2.13.3 The need and complexity of fabric engineering 65

2.13.4 Applications of soft computing in fabric engineering 66

Chapter 3 Materials and methods 69

3.1 Introduction 71

3.2 Materials 71

3.3 Sample preparation 72

3.3.1 Yarn samples 72

3.3.2 Fabric samples 72

3.3.2.1 Sizing 72

xii

3.3.2.2 Warping 73

3.3.2.3 Drawing-in and denting 74

3.3.2.4 Weaving 74

3.3.3 Preparatory wet treatments of fabric samples 75

3.3.3.1 Desizing 75

3.3.3.2 Scouring 75

3.3.3.3 Bleaching 76

3.3.4 UV absorber treatment 76

3.4 Testing and characterization methods 77

3.4.1 Yarn diameter and fabric cross section 77

3.4.2 Yarn twist 77

3.4.3 End density and pick density 78

3.4.4 Fabric cover 78

3.4.5 Fabric areal density 78

3.4.6 Fabric thickness 79

3.4.7 Fabric shrinkage 79

3.4.8 Ultraviolet protection factor (UPF) 79

3.4.9 Air permeability 81

3.4.10 Moisture vapour transmission rate (MVTR) 81

Chapter 4 Comparative analysis of ultraviolet protection by fabrics

woven from cotton and bamboo viscose yarns

83

4.1 Introduction 85

4.2 Experimental 87

4.2.1 Materials 87

xiii

4.2.2 Fabric sample preparation 87

4.2.3 Testing 88

4.3 Results and discussion 89

4.3.1 Diameter of cotton and bamboo viscose yarns 89

4.3.2 UPF of cotton and bamboo viscose fabrics 89

4.3.3 Shrinkage, cover % and areal density of cotton and bamboo

viscose fabrics

90

4.3.4 Analysis of UPF of cotton and bamboo viscose fabrics using

curve fitting technique

95

4.3.5 UV transmittance of cotton and bamboo viscose fabrics

having similar cover % and areal density

100

4.4 Conclusions 101

Chapter 5 Studies on the effect of yarn twist, modified yarn

structure and fabric thickness on ultraviolet protection

by woven fabrics

103

5.1 Introduction 105

5.2 Effect of yarn twist on UPF of woven cotton fabrics 107

5.2.1 Yarn twist 107

5.2.2 Experimental 107

5.2.2.1 Yarn and fabric sample preparation 107

5.2.2.2 Testing 108

5.2.3 Results and discussion 108

5.2.3.1 Twist and diameter of yarn samples 108

5.2.3.2 Areal density, thickness and UPF of fabric samples 109

xiv

5.3 Effect of modified yarn structure (hollow) on UPF of woven

cotton fabrics

113

5.3.1 Advantages of hollow yarn over normal yarn 113


5.3.2.1 Materials 114

5.3.2.2 Yarn and fabric sample preparation 115

5.3.2.3 Testing 117

5.3.3 Results and Discussion 117

5.3.3.1 Yarn diameter and fabric cross-section 117

5.3.3.2 Cover % and areal density of fabric samples 119

5.3.3.3 UPF of fabric samples 120

5.4 Effect of fabric thickness and pore size, at constant cover %,

on UPF of woven fabrics

122

5.4.1 Different fabric thickness keeping constant cover % 122


5.4.2.1 Materials 124

5.4.2.2 Fabric sample preparation 124

5.4.2.3 Testing 126

5.4.3 Results and Discussion 126

5.4.3.1 Cover %, areal density and thickness of polyester fabrics 126

5.4.3.2 Pore characteristics of polyester fabrics 129

5.4.3.3 UPF of polyester fabrics 133

5.4.3.4 Cover %, areal density and thickness of cotton fabrics 136

5.4.3.5 Pore characteristics of cotton fabrics 139

5.4.3.6 UPF of cotton fabrics 139

xv

5.4.3.7 Overall effect of fabric parameters on UPF 142

5.4.4 Conclusions 144

Chapter 6 Comparative analysis of modeling methods for

predicting ultraviolet protection factor of woven fabrics

147


6.2 Experimental 151

6.2.1 Materials 151


6.3 Different modeling approaches and algorithms 152

6.3.1 Nonlinear regression model 152

6.3.2 Artificial neural network and Levenberg-Marquard

algorithm

153

6.3.3 Neuro-fuzzy systems 155

6.3.3.1 Fuzzy logic and fuzzy sets 155

6.3.3.2 Adaptive neuro-fuzzy inference system (ANFIS) 155

6.4 Development and evaluation of nonlinear regression, ANN

and ANFIS model

159

6.4.1 Model development 159

6.4.2 Data partitioning 160

6.2.3 Evaluation of prediction performance 161


6.5.1 UPF of fabrics 161

6.5.2 Nonlinear regression model for UPF 163

6.5.3 Analysis of contour plots of regression model 165

xvi

6.5.4 ANN model for UPF 167

6.5.5 Trend analysis by ANN model 167

6.5.6 ANFIS model for UPF 170

6.5.7 Linguistic rules of ANFIS model 170

6.5.8 Comparison between the prediction performances of

nonlinear regression, ANN and ANFIS models

174

6.6 Conclusions 176

Chapter 7 Fabric engineering for ultraviolet protection and comfort 177


7.2 Experimental 181

7.2.1 Materials 181


7.2.3 Testing 184


7.3.1 Development and selection of ANN models for UPF, air

permeability and MVTR

185

7.3.2 Developing Pareto charts for selection of UPF, air

permeability and MVTR of validation samples

191

7.3.2.1 Pareto charts for UPF and air permeability 191

7.3.2.2 Selection of UPF, air permeability and MVTR of validation

samples from Pareto charts

196

7.3.3 Optimization by ANN-GA hybrid approach 198

7.3.4 Optimization results of hybrid ANN-GA system 205

7.3.5 Validation of fabric engineering system 210

xvii

7.4 Conclusions 212

Chapter 8 Conclusions 213

Chapter 9 Suggestions for future work 217

References 221

List of publications 241

Bio-data 243

xix

LIST OF FIGURES

Figure no. Figure caption Page no.

2.1 Ultraviolet rays in the electromagnetic spectrum 12

2.2 Pictogram for UV protective clothing according to EN 13758-2 25

2.3 Reflection, transmission, absorption and scattering of UV rays 28

2.4 UV transmittance through fabrics made of various fibre materials 31

2.5 Effect of weft linear density (Ne) on UPF of plain woven fabric 32

2.6 Classification of woven fabric constructional parameters 34

2.7 Effect of weave and fabric tightness on UPF of grey cotton fabric 36

2.8 Effect of cover factor on UPF without weave influence 38

2.9 Effect of cover factor on UPF with weave influence 39

2.10 Effect of volume porosity on UPF of beige fabrics 40

2.11 Energy dissipation by absorption 45

2.12 Effect of repeated washing cycles on the CPF of white cotton fabric 46

2.13 Effect of extension on UV transmission 47

2.14 Effect of wetness on UV transmission 49

2.15 Effect of layering on UPF 50

2.16 Multilayered artificial neural network structure 55

2.17 Simple model of an artificial neuron 55

2.18 Different forms of membership function 59

2.19 Schematic representation of single point crossover and mutation 60

2.20 Possible hybrid combinations of three soft computing techniques 61

3.1 Roller attachment mounted on the front roller of ringframe 72

3.2 CCI Tech single end sizing machine 73

xx

3.3 CCI Tech single end warping machine 73

3.4 CCI Tech single rigid rapier loom 74

3.5 Temperature profile for UV absorber treatment of polyester fabric 77

3.6 Scheme for fabric cover measurement 78

3.7 UV transmittance analyzer 80

3.8 Optical components of UV transmittance analyzer 80

3.9 WVTR testing instrument 82

4.1 Cover % of cotton and bamboo viscose fabrics for 20 Ne yarns 92



4.4 Areal density of cotton and bamboo viscose fabrics for 20 Ne yarns 93



4.7 UPF of bamboo viscose fabrics vs cover % 97

4.8 UPF of bamboo viscose fabrics vs areal density 99

4.9 UV transmittance through fabrics having same cover % and areal density 101

5.1 Areal density of fabric samples 111

5.2 Thickness of fabric samples 111

5.3 UPF of fabric samples 112

5.4 Flattening of yarns (a) PVA core spun yarn (b) Normal yarn 114

5.5 Schematic diagram of core spinning process 116

5.6 Cross-section of fabric samples made of core and normal yarns 118

5.7 Cover % of normal and core yarn fabrics 119

5.8 Areal density of normal and core yarn fabrics 120

5.9 Images of normal and core yarn fabrics with pick density of 20 inch-1 121

xxi

5.10 Fabrics with different yarn diameter and spacing for same cover % 124

5.11 Cover % of polyester fabrics 128

5.12 Areal density of polyester fabrics 128

5.13 Thickness of polyester fabrics 129

5.14 Individual pore size in fabrics with same cover % 130

5.15 Different pore sizes in three polyester fabric sets 131

5.16 Pore area distribution of polyester fabric set 1 132



5.19 UPF of polyester fabrics before UV absorber treatment 135

5.20 UPF of polyester fabrics after UV absorber treatment 136

5.21 Cover % of cotton fabrics 137

5.22 Areal density of cotton fabrics 138

5.23 Thickness of cotton fabrics 138

5.24 UPF of cotton fabrics before UV absorber treatment 141

5.25 UPF of cotton fabrics after UV absorber treatment 142

6.1 ANFIS architecture 156

6.2 Contours showing the effect of proportion of polyester and weft count on

UPF

166

6.3 Contours showing the effect of proportion of polyester and pick density

on UPF

167

6.4 Effect of proportion of polyester and weft count on UPF 169

6.5 Effect of proportion of polyester and pick density on UPF 169

6.6 Effect of pick density and weft count on UPF 170

6.7 Fuzzy linguistic rules 172

xxii

6.8 Effect of proportion of polyester and weft count on UPF 173

6.9 Effect of proportion of polyester and pick density on UPF 173

7.1 Trend analyses of ANNUPF model 190

7.2 Trend analyses of ANNAP model 190

7.3 Trend analyses of ANNMVTR Model 191

7.4 UPF and air permeability for 30 Ne yarns and 50 % polyester blend 193





7.9 Selection of UPF and air permeability from Pareto chart 197

7.10 Selection of UPF and MVTR from Pareto chart 197

7.11 Binary coding of a chromosome 199

7.12 Flow chart of hybrid ANN-GA optimization system 203

7.13 Number of generation vs r value 204

7.14 Scatter plot of warp and weft fractional covers 209

xxiii

LIST OF TABLES

Table no. Table caption Page no.

2.1 Classification of ultraviolet radiation and their abbreviation 13

2.2 Various protecting factors and their uses 18

2.3 UV index and the recommended exposure time 22

2.4 Ultraviolet protection classification 23

2.5 Summary of factors affecting UPF 28

3.1 Details of yarn samples 71

3.2 Desizing parameters 75

3.3 Scouring parameters 75

3.4 Bleaching parameters 76

3.5 Recipe for UV-Sun® CEL liquor 76

3.6 Recipe for Dorafast AKS 300 % liquor 77

4.1 Spinning process parameters 87

4.2 Summary of fabric parameters 88

4.3 Diameter of cotton and bamboo viscose yarns 89

4.4 Fabric parameters and UPF for cotton and bamboo viscose fabrics 91

4.5 UPF of bamboo viscose fabrics predicted from cotton’s cover % model 96

4.6 UPF of bamboo viscose fabrics predicted from cotton’s areal density

model

99

4.7 UPF prediction accuracy of various models 100

5.1 Spinning parameters 108

5.2 Weaving parameters 108

5.3 Twist and diameter of yarn samples 109

xxiv

5.4 Areal density, thickness and UPF 110

5.5 Two-way ANOVA table 112

5.6 Spinning parameters 116


5.8 Yarn diameter (major axis) in fabric after yarn flattening 117

5.9 UPF of fabric samples before and after UV absorber treatment 121

5.10 Ratio of yarn diameters 125

5.11 Weft yarn count and pick density of cotton fabrics 125

5.12 Weft yarn count and pick density of polyester fabrics 126

5.13 Cover %, areal density, thickness and pore area of polyester fabrics 127

5.14 UPF of polyester fabrics 135

5.15 Cover %, areal density thickness and pore area of cotton fabrics 137

5.16 UPF of cotton fabrics 140

5.17 One-way ANOVA for three cotton fabric sets before UV absorber

treatment

141

5.18 Effects of fabric parameters on UPF for constant cover % 144


6.2 Actual and coded parameters for regression model 160

6.3 ANFIS parameters 160

6.4 UPF of fabric samples for training and testing dataset 162

6.5 Initial ANOVA for nonlinear regression model of UPF 164

6.6 Final ANOVA for nonlinear regression model of UPF 164

6.7 Summary of UPF prediction accuracy of regression, ANN and ANFIS

models

165

6.8 Detailed prediction results in testing datasets 175

xxv

7.1 Parameters and levels used in Box and Behnken design plan 182

7.2 Detailed sample design plan 183

7.3 Input and output fabric parameters for ANN models 187

7.4 Prediction accuracy of developed ANN models 189

7.5 Specification of validation samples 196

7.6 Operating parameters for ANN-GA hybrid approach 204

7.6 Results for validation sample V1 in different runs 208

7.8 Optimized input and output parameters for all validation samples 209

7.9 Optimized and achieved values of input and output parameters 211

fabric engineering for ultraviolet radiation protection

Documents