Engineering cotton yarns with artificial neural networking (ANN) /
Tasnim N. Shaikh, Sweety A. Agrawal
- 1st edition
- New Delhi : Woodhead, c2017
- xiii, 253p. : ill. ; 24cm
1. Classification of textile Yarns 1.1 Introduction 1.2 Types of textile fiber 1.3 Types of textile yarns etc...
2. Attributes of cotton mixing 2.1 Need for mix formulation 2.2 Interrelationship between fiber characteristics and yarn quality 2.3 Contribution of fiber parameters on ring spun yarn quality & cost etc...
3. Testing techniques used in yarn engineering 3.1 Introduction 3.2 Role of testing in cotton selection 3.3 Various fiber testing techniques etc...
4. Statistical techniques used in yarn engineering 4.2 Analysis of test data 4.2.1 Measures of central tendencies 4.2.2 Measurement of dispersion etc...
5. Artificial neural networking (ANN) 5.1 Introduction 5.2 Historical background for development of ANN 5.3 Basic concept of ANN (Artificial Neural Network) etc...
6. Changes in mix formulation approach with the technological developments 6.1 Introduction 6.2 Basic objectives of mix formulation 6.3 Constraints for accurate mixing etc...
7. Cotton fiber engineering 7.1 Introduction 7.2 Importance of cotton fiber engineering 7.3 Attributes of cotton fiber engineering etc...
8. Yarn engineering by back propagation algorithm concept of ANN 8.1 Introduction 8.2 Reverse yarn engineering 8.2.1 Importance 8.2.2 Basic steps of networking etc...
9. Optimization of yarn quality, cost and process parameters 9.1 Introduction 9.2 Components for optimization 9.3 Technological value of cotton mix etc...
10. Case study 10.1 Introduction 10.2 Case study 1 10.2.1 Basic attributes used for ANN reverse yarn etc... Appendices