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FABRIC ENGINEERING FOR ULTRAVIOLET
RADIATION PROTECTION
PIYALI HATUA
DEPARTMENT OF TEXTILE TECHNOLOGY
INDIAN INSTITUTE OF TECHNOLOGY DELHI
SEPTEMBER 2015
© Indian Institute of Technology Delhi (IITD), New Delhi, 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 Experimental 114
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 Experimental 124
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
6.1 Introduction 149
6.2 Experimental 151
6.2.1 Materials 151
6.2.2 Fabric sample preparation 152
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 Results and discussion 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
7.1 Introduction 179
7.2 Experimental 181
7.2.1 Materials 181
7.2.2 Fabric sample preparation 181
7.2.3 Testing 184
7.3 Results and discussion 185
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
4.2 Cover % of cotton and bamboo viscose fabrics for 25 Ne yarns 92
4.3 Cover % of cotton and bamboo viscose fabrics for 30 Ne yarns 93
4.4 Areal density of cotton and bamboo viscose fabrics for 20 Ne yarns 93
4.5 Areal density of cotton and bamboo viscose fabrics for 25 Ne yarns 94
4.6 Areal density of cotton and bamboo viscose fabrics for 30 Ne yarns 94
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
5.17 Pore area distribution of polyester fabric set 2 132
5.18 Pore area distribution of polyester fabric set 3 133
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
7.5 UPF and air permeability for 20 Ne yarns and 50 % polyester blend 194
7.6 UPF and air permeability for 40 Ne yarns and 50 % polyester blend 194
7.7 UPF and air permeability for 30 Ne yarns and 0 % polyester blend 195
7.8 UPF and air permeability for 30 Ne yarns and 100 % polyester blend 195
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
5.7 Weaving 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
6.1 Weaving parameters 152
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
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