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Soft computing in textile engineering

Edited by A Majumdar, Indian Institute of Technology, India

Woodhead Publishing Series in Textiles No. 111

 - covers the entire process of textile production, from fibre manufacture to garment engineering including artificial neural networks, fuzzy logic and genetic algorithms
 - examines soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture
 - specifically reviews soft computing in relation to textile properties and applications featuring garment modelling and sewing machines
 - discusses the role of soft computing in textile quality evaluation considering fibre grading, tearing and image processing

Soft computing refers to a collection of computational techniques which study, model and analyse complex phenomena. As many textile engineering problems are inherently complex in nature, soft computing techniques have often provided optimum solutions to these cases. Although soft computing has several facets, it mainly revolves around three techniques; artificial neural networks, fuzzy logic and genetic algorithms. The book is divided into five parts, covering the entire process of textile production, from fibre manufacture to garment engineering. These include soft computing techniques in yarn manufacture and modelling, fabric and garment manufacture, textile properties and applications and textile quality evaluation.

Published in association with The Textile Institute

£155.00 / US$265.00 / €185.00

An e-book version of this title is available to purchase as part of a Collection or Pick n Mix or by Chapter. Visit Woodhead Publishing Online

About the editor

Abhijit Majumdar is an Assistant Professor at the Indian Institute of Technology, Delhi. His research focuses on yarn and fabric production, operations and production management, and soft computing applications. He has written extensively on artificial neural networks and fuzzy logic.

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Contents

PART 1 INTRODUCTION TO SOFT COMPUTING
PART 2 SOFT COMPUTING IN YARN MANUFACTURING
PART 3 SOFT COMPUTING IN FABRIC MANUFACTURING
PART 4 SOFT COMPUTING FOR TEXTILE PROPERTIES AND APPLICATIONS
PART 5 SOFT COMPUTING IN TEXTILE QUALITY EVALUATION

PART 1 INTRODUCTION TO SOFT COMPUTING

Introduction to soft computing techniques: artificial neural networks, fuzzy logic and genetic algorithm
A K Deb, Indian Institute of Technology, India
 - Introduction: traditional computing and soft computing
 - Evolutionary algorithms
 - Fuzzy sets and fuzzy logic
 - Neural networks
 - Other approaches
 - Hybrid techniques
 - Conclusion
 - References

Artificial neural networks in materials modeling
M Murugananth, Tata Steel, India
 - Introduction
 - Evolution of neural networks
 - Neural network models
 - Importance of uncertainty
 - Application of neural networks in materials science
 - Future trends
 - Acknowledgements
 - References

Fundamentals of soft models in textiles
J Militký, Technical University of Liberec, Czech Republic
 - Introduction
 - Empirical model building
 - Linear regression models
 - Neural networks
 - Selected applications of neural networks
 - Conclusion
 - References

PART 2 SOFT COMPUTING IN YARN MANUFACTURING

Artificial neural network in yarn property modeling
R Chattopadhyay, Indian Institute of Technology, India
 - Introduction
 - Review of the literature
 - Comparison of different models
 - Artificial neural network
 - Design methodology
 - Artificial neural network model for yarn
 - Modeling tensile properties
 - Conclusion
 - References

Performance evaluation and enhancement of artificial neural network in prediction modeling
A Guha, Indian Institute of Technology, India
 - Introduction
 - Skeletonization
 - Sensitivity analysis
 - Use of principal component analysis for analysis of neural network’s failure
 - Improving the performance of neural network
 - Sources of further information and future trends
 - References

Yarn engineering using artificial neural network
A Basu, The South India Textile Research Association, India
 - Introduction
 - Yarn property engineering using artificial neural network
 - Ring spun yarn engineering
 - Air-jet yarn engineering
 - Advantages and limitations
 - Conclusions
 - Sources of further information and advice
 - References

Adaptive neuro-fuzzy systems in yarn modeling
A Majumdar, Indian Institute of Technology, India
 - Introduction
 - Artificial neural network and fuzzy logic
 - Neuro-fuzzy system and adaptive neural network based fuzzy inference system (ANFIS)
 - Applications of ANFIS in yarn property modeling
 - Limitations of ANFIS
 - Conclusions
 - References

PART 3 SOFT COMPUTING IN FABRIC MANUFACTURING

Woven fabric engineering by mathematical modeling and soft computing methods
B K Behera, Indian Institute of Technology, India
 - Introduction
 - Fundamentals of woven construction
 - Elements of woven structure
 - Fundamentals of design engineering
 - Traditional designing
 - Traditional designing with structural mechanics approach
 - Designing of textile products
 - Design engineering by theoretical modeling
 - Modeling methodologies
 - Deterministic models
 - Nondeterministic models
 - Authentication and testing of models
 - Reverse engineering
 - Future trends in non-conventional methods of design engineering
 - Conclusion
 - References

Soft computing applications in knitting technology
M Blaga, Gheorghe Asachi Technical University, Romania
 - Introduction
 - Scope of soft computing applications in knitting
 - Applications in knitted fabrics
 - Applications in knitting machines
 - Future trends
 - Acknowledgements
 - References

Modelling nonwovens using artificial neural networks
A Patanaik and R D Anandjiwala, CSIR Materials Science and Manufacturing, and Nelson Mandela Metropolitan University, South Africa
 - Introduction
 - Artificial neural network modelling in needle-punched nonwovens
 - Artificial neural network modelling in melt blown nonwovens
 - Artificial neural network modelling in spun bonded nonwovens
 - Artificial neural network modelling in thermal and chemical bonded nonwovens
 - Future trends
 - Sources of further information and advice
 - References

PART 4 SOFT COMPUTING FOR TEXTILE PROPERTIES AND APPLICATIONS

Garment modelling by fuzzy logic
B Witkowska, Textile Research Institute and I Frydrych, Technical University of Lodz, Poland
 - Introduction
 - Basic principles of garment modeling
 - Modelling of garment pattern alteration with fuzzy logic
 - Advantages and limitations
 - Future trends
 - References

Soft computing applications for sewing machines
R Korycki and R Krasowska, Technical University of Lodz, Poland
 - Introduction
 - Dynamical analysis of different stitches
 - Sources of information
 - Thread need by needle and bobbin hook
 - Modeling and analysis of stitch tightening process
 - Conclusions and future trends
 - References

Artificial neural network modeling for prediction of thermal transmission properties of woven fabrics
V K Kothari Indian Institute of Technology and D Bhattacharjee, Terminal Ballistics Research Laboratory, India
 - Introduction
 - Artificial neural network systems
 - Thermal insulation in textiles
 - Future trends
 - Conclusions
 - References

PART 5 SOFT COMPUTING IN TEXTILE QUALITY EVALUATION

Fuzzy decision making and its applications in cotton fibre grading
B Sarkar, Jadaypur University, India
 - Introduction
 - Multiple criteria decision making (MCDM) process
 - Fuzzy multi criteria decision making
 - Fuzzy TOPSIS
 - Conclusions
 - References

Silk cocoon grading by fuzzy expert systems
A Biswas and A Ghosh, Government College of Engineering and Textile Technology, India
 - Introduction
 - Concept of fuzzy logic
 - Experimental
 - Development of a fuzzy expert system for cocoon grading
 - Conclusions
 - References - Introduction
 - Concept of fuzzy logic
 - Experimental
 - Development of a fuzzy expert system for cocoon grading
 - Conclusions
 - References

Artificial neural network applications in textile composites
S Mukhopadhyay, Indian Institute of Technology, India
 - Introduction
 - Quasi-static mechanical properties
 - Viscoelastic behavior
 - Fatigue behavior
 - Conclusion
 - References

Modelling the fabric tearing process
B Witkowska, Textile Research Institute and I Frydrych, Technical University of Lodz, Poland
 - Introduction
 - Existing models of fabric tearing process
 - Modelling the tear force for the wing shape specimen using the traditional method of force distribution and algorithm
 - Assumptions for modelling
 - Measurement methodology
 - Experimental verification of theoretical tear strength model
 - Modelling the tear force for the wing-shaped specimen using artificial neural networks
 - Conclusions *Acknowledgements
 - References

Textile quality evaluation by image processing and soft computing techniques
A A Merati, Amirkabir University of Technology and D Semnani, Isfahan University of Technology, Iran
 - Introduction
 - Principles of image processing technique
 - Fibre classification and grading
 - Yarn quality evaluation
 - Fabric quality evaluation
 - Garment defects classification and evaluation
 - Future trends
 - References