Soft Computing in Textile Engineering Edited by Abhijit Majumdar

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Soft Computing in Textile Engineering
Edited by Abhijit Majumdar
Soft Computing in Textile Engineering

Contents
Contributor contact details xi
Woodhead Publishing series in Textiles xv

Part I Introduction to soft computing
1 Introduction to soft computing techniques: artificial
neural networks, fuzzy logic and genetic algorithms 3
A. K. Deb, Indian Institute of Technology, Kharagpore, India
1.1 Introduction: traditional computing and soft computing 3
1.2 Evolutionary algorithms 4
1.3 Fuzzy sets and fuzzy logic 10
1.4 Neural networks 13
1.5 Other approaches 17
1.6 Hybrid techniques 21
1.7 Conclusion 21
1.8 References 22
2 Artificial neural networks in materials modelling 25
M. Murugananth, Tata Steel, India
2.1 Introduction 25
2.2 Evolution of neural networks 26
2.3 Neural network models 28
2.4 Importance of uncertainty 31
2.5 Application of neural networks in materials science 32
2.6 Future trends 40
2.7 Acknowledgements 41
2.8 References and bibliography 42
3 Fundamentals of soft models in textiles 45
J. Militký, Technical University of Liberec, Czech Republic
3.1 Introduction 45
3.2 Empirical model building 46
3.3 Linear regression models 62
3.4 Neural networks 77
3.5 Selected applications of neural networks 87
3.6 Conclusion 96
3.7 References 98

Part II Soft computing in yarn manufacturing
4 Artificial neural networks in yarn property modeling 105
R. Chattopad hyay, Indian Institute of Technology, Delhi, India
4.1 Introduction 105
4.2 Review of the literature 106
4.3 Comparison of different models 106
4.4 Artificial neural networks 106
4.5 Design methodology 113
4.6 Artificial neural network model for yarn 113
4.7 Modeling tensile properties 117
4.8 Conclusion 123
4.9 References 123
5 Performance evaluation and enhancement of artificial neural networks in prediction modelling 126
A. Guha, Indian Institute of Technology, Bombay, India
5.1 Introduction 126
5.2 Skeletonization 127
5.3 Sensitivity analysis 131
5.4 Use of principal component analysis for analysing failure of a neural network 135
5.5 Improving the performance of a neural network 140
5.6 Sources of further information and future trends 143
5.7 References 144
6 Yarn engineering using an artificial neural network 147
A. Basu, The South India Textile Research Association, India
6.1 Introduction 147
6.2 Yarn property engineering using an artificial neural network (ANN) 150
6.3 Ring spun yarn engineering 150
6.4 Air-jet yarn engineering 155
6.5 Advantages and limitations 157
6.6 Conclusions 157
6.7 Sources of further information and advice 157
6.8 References 158
7 Adaptive neuro-fuzzy systems in yarn modelling 159
A. Maj umda r, Indian Institute of Technology, Delhi, India
7.1 Introduction 159
7.2 Artificial neural network and fuzzy logic 160
7.3 Neuro-fuzzy system and adaptive neural network based fuzzy inference system (ANFIS) 165
7.4 Applications of adaptive neural network based fuzzy
inference system (ANFIS) in yarn property modelling 167
7.5 Limitations of adaptive neural network based fuzzy inference system (ANFIS) 176
7.6 Conclusions 176
7.7 References 176

Part III Soft computing in fabric manufacturing
8 Woven fabric engineering by mathematical modeling
and soft computing methods 181
B. K. Behera, Indian Institute of Technology, Delhi, India
8.1 Introduction 181
8.2 Fundamentals of woven construction 182
8.3 Elements of woven structure 183
8.4 Fundamentals of design engineering 185
8.5 Traditional designing 186
8.6 Traditional designing with structural mechanics approach 187
8.7 Designing of textile products 188
8.8 Design engineering by theoretical modeling 189
8.9 Modeling methodologies 191
8.10 Deterministic models 192
8.11 Non-deterministic models 200
8.12 Authentication and testing of models 208
8.13 Reverse engineering 209
8.14 Future trends in non-conventional methods of design engineering 210
8.15 Conclusion 212
8.16 References 213
9 Soft computing applications in knitting technology 217
M. Blaga, Gheorghe Asachi Technical University of Iasi, Romania
9.1 Introduction 217
9.2 Scope of soft computing applications in knitting 221
9.3 Applications in knitted fabrics 222
9.4 Applications in knitting machines 231
9.5 Future trends 241
9.6 Acknowledgements 243
9.7 References and bibliography 244
10 Modelling nonwovens using artificial neural networks 246
A. Patanaik and R. D. Anandj iwala, CSIR Materials
Science and Manufacturing, and Nelson Mandela
Metropolitan University, South Africa
10.1 Introduction 246
10.2 Artificial neural network modelling in needle-punched nonwovens 247
10.3 Artificial neural network modelling in melt blown nonwovens 256
10.4 Artificial neural network modelling in spun bonded nonwovens 260
10.5 Artificial neural network modelling in thermally and
chemically bonded nonwovens 262
10.6 Future trends 265
10.7 Sources of further information and advice 266
10.8 Acknowledgements 266
10.9 References and bibliography 266

Part IV Soft computing in garment and composite manufacturing
11 Garment modelling by fuzzy logic 271
R. Ng, Hong Kong Polytechnic University, Hong Kong
11.1 Introduction 271
11.2 Basic principles of garment modelling 274
11.3 Modelling of garment pattern alteration with fuzzy logic 281
11.4 Advantages and limitations 286
11.5 Future trends 289
11.6 References 289
12 Soft computing applications for sewing machines 294
R. Koryck i and R. Krasowska , Technical University of
Łódź, Poland
12.1 Introduction 294
12.2 Dynamic analysis of different stitches 295
12.3 Sources of information 296
12.4 Thread need by needle and bobbin hook 297
12.5 Modelling and analysis of stitch tightening process 308
12.6 Conclusions and future trends 326
12.7 References 327
13 Artificial neural network applications in textile composites 329
S. Mukhopad hyay, Indian Institute of Technology, Delhi, India
13.1 Introduction 329
13.2 Quasi-static mechanical properties 331
13.3 Viscoelastic behaviour 336
13.4 Fatigue behaviour 338
13.5 Conclusion 347
13.6 References 347

Part V Soft computing in textile quality evaluation
14 Fuzzy decision making and its applications in cotton fibre grading 353
B. Sarka r, Jadavpur University, India
14.1 Introduction 353
14.2 Multiple criteria decision making (MCDM) process 357
14.3 Fuzzy multiple criteria decision making (FMCDM) 366
14.4 Conclusions 380
14.5 References and bibliography 380
15 Silk cocoon grading by fuzzy expert systems 384
A. Biswas and A. Ghosh, Government College of
Engineering and Textile Technology, India
15.1 Introduction 384
15.2 Concept of fuzzy logic 385
15.3 Experimental 389
15.4 Development of a fuzzy expert system for cocoon grading 390
15.5 Conclusions 400
15.6 References 402
16 Artificial neural network modelling for prediction of thermal transmission properties of woven fabrics 403
V. K. Kothari, Indian Institute of Technology, Delhi, India
and D. Bhattac harjee, Terminal Ballistics Research Laboratory, India
16.1 Introduction 403
16.2 Artificial neural network systems 404
16.3 Thermal insulation in textiles 410
16.4 Future trends 413
16.5 Conclusions 419
16.6 References 421
17 Modelling the fabric tearing process 424
B. Witkowska , Textile Research Institute, Poland and
I. Frydrych, Technical University of Łódź, Poland
17.1 Introduction 424
17.2 Existing models of the fabric tearing process 434
17.3 Modelling the tear force for the wing-shaped specimen using the traditional method of force distribution and algorithm 438
17.4 Assumptions for modelling 441
17.5 Measurement methodology 448
17.6 Experimental verification of the theoretical tear strength model 459
17.7 Modelling the tear force for the wing-shaped specimen using artificial neural networks 471
17.8 Conclusions 485
17.9 Acknowledgements 487
17.10 References and bibliography 487
18 Textile quality evaluation by image processing and soft computing techniques 490
A. A. Merati, Amirkabir University of Technology, Iran
and D. Semnani, Isfahan University of Technology, Iran
18.1 Introduction 490
18.2 Principles of image processing technique 491
18.3 Fibre classification and grading 495
18.4 Yarn quality evaluation 501
18.5 Fabric quality evaluation 509
18.6 Garment defect classification and evaluation 516
18.7 Future trends 519
18.8 References and bibliography 520
Index 524

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