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Engineering cotton yarns with artificial neural networking (ANN) PDF

269 Pages·2017·3.955 MB·English
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Engineering Cotton Yarns with Artificial Neural Networking (ANN) Engineering Cotton Yarns with Artificial Neural Networking (ANN) Dr. (Mrs.) Tasnim N. Shaikh and Mrs. Sweety A. Agrawal WOODHEAD PUBLISHING INDIA PVT LTD New Del hi, India Published by Woodhead Publishing India Pvt. Ltd. Woodhead Publishing India Pvt. Ltd., 303, Vardaan House, 7/28, Ansari Road, Daryaganj, New Delhi - 110002, India www.woodheadpublishingindia.com First published 2017, Woodhead Publishing India Pvt. Ltd. © Woodhead Publishing India Pvt. Ltd., 2017 This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing India Pvt. Ltd. The consent of Woodhead Publishing India Pvt. Ltd. does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing India Pvt. Ltd. for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. Woodhead Publishing India Pvt. Ltd. ISBN: 978-9-38505-920-9 Woodhead Publishing India Pvt. Ltd. WebPDF ISBN: 9781351186261 Typeset by Third EyeQ Technologies Pvt Ltd, New Delhi Contents Preface xiii 1 Classification of textile yarns 1 1.1 Introduction 2 1.2 Types of textile fibers 3 1.3 Types of textile yarn 5 1.4 Significance of cotton yarn in textile industry 9 1.5 Production pattern in brief for most significant cotton fiber in textile industry 10 1.6 Impact of natural fiber variations on yarn production process 13 References 14 2 Attributes of cotton mixing 17 2.1 Need for mix formulation 17 2.2 Interrelationship between fiber characteristics and yarn quality 18 2.3 Contribution of fiber parameters on ring spun yarn quality & cost 21 2.3.1 Length & length variations 21 2.3.2 Fineness 23 2.3.3 Maturity 25 2.3.4 Strength 26 2.3.5 Trash 27 2.3.6 Moisture 28 2.3.7 Colour 29 2.4 Importance of mix homogeneity 30 2.5 Impact of technological changes on homogeneity of mix 30 Reference 30 vi Contents 3 Testing techniques used in yarn engineering 33 3.1 Introduction 33 3.1.2 Attributes of cotton ring spun yarn engineering 34 3.2 Role of testing in cotton selection 35 3.3 Various fiber testing techniques 35 3.3.1 Fiber length 36 3.3.2 Fiber fineness 44 3.3.3 Fiber maturity 48 3.3.4 Fiber strength 51 3.3.5 Trash 55 3.3.6 Moisture 58 3.3.7 Colour 59 3.4 Stages of developments in testing techniques and its impact on mix formulation process 61 3.4.1 Classical testing techniques 62 3.4.2 Semi-automatic mode of testing 63 3.4.3 Automatic mode of testing 65 References 69 4 Statistical techniques used in yarn engineering 71 4.2 Analysis of test data 72 4.2.1 Measures of central tendency 73 4.2.2 Measurement of dispersion 74 4.3 Compatibility test methods 78 4.4 Statistical techniques for defining technological value of cotton 80 4.4.1 Drafting quality index (Q) 81 4.4.2 Fiber quality index (FQI) 82 4.4.3 Modified fiber quality index (MFQI) 84 4.4.4 Spinning consistency index (SCI) 85 4.4.5 Premium discount index (PDI) 86 4.4.6 Multi criteria decision-making (MCDM) 87 Contents vii 4.4.7 Geometric properties index (IG) 88 4.5 Statistical techniques for defining proportion of cotton constituents in mix 89 4.5.1 Judicious mixing 89 4.5.2 Linear programming 89 4.5.3 Fuzzy linear programming approach 92 References 93 5 Artificial neural networking (ANN) 95 5.1 Introduction 95 5.2 Historical background for the development of ANN 97 5.3 Basic concept of ANN (Artificial Neural Network) 98 5.4 Types of neural network 98 5.4.1 Single layer feed forward network 100 5.4.2 Multilayer feed forward network 100 5.4.3 Recurrent network 100 5.4.4 Learning of a network 101 5.5 Architecture of ANN 101 5.6 Designing the network 102 5.7 Operational mode of ANN 103 5.7.1 Training the network 103 5.7.2 Verification testing 104 5.8 Applications areas of ANN 104 5.9 ANN applications in the field of textile engineering 105 5.9.1 Fibers 106 5.9.2 Yarn 108 5.9.3 Fabric 109 5.9.4 Garment 114 5.9.5 Non-woven 115 5.10 Connotation of ANN offered solutions over the other methods 116 References 116 viii Contents 6 Changes in mix formulation approach with the technological developments 127 6.1 Introduction 127 6.2 Basic objectives of mix formulation 128 6.3 Constrains for accurate mixing 129 6.4 Different approaches of mix formulation 129 6.4.1 Classical visual judgment approach 130 6.4.2 Mix formulation with non–automatic instrumental technology 135 6.4.3 Mix formulation with automatic instrumental technology 143 References 149 7 Cotton fiber engineering 151 7.1 Introduction 151 7.2 Importance of cotton fiber engineering 152 7.3 Attributes of cotton fiber engineering 153 7.3.1 Cotton purchasing strategy 154 7.3.2 Cotton testing 154 7.3.3 Bale management 155 7.3.4 Cotton fiber selection 156 7.4 Bale inventory analysis system (BIAS) 159 7.5 Engineered fiber selection (EFS) 160 7.5.1 Determination of cotton specifications 160 7.5.2 Opening line configuration and availability 161 7.5.3 In-house inventory management 163 7.5.4 Mix profiles 165 7.5.5 Bale selection 165 7.5.6 Mix evaluation and performance verification 166 7.5.7 Benefits offered by EFS® 166 7.6 Linear programming 167 7.6.1 Assumptions of linear programming 168 Contents ix 7.6.2 Types of linear programming 168 7.6.3 Effect of inventory constraints 169 References 170 8 Yarn engineering by back propagation algorithm concept of ANN 173 8.1 Introduction 174 8.2 Reverse yarn engineering 175 8.2.1 Importance 175 8.2.2 Basic steps of networking 175 8.3 Procedure for cotton yarn engineering 176 8.3.1 Defining aim of yarn engineering 177 8.3.2 Database creation 178 8.3.3 Construction of desired artificial neural network 179 8.3.4 Modelling 183 8.3.5 Testing of neural network 190 8.3.6 Computing prediction error 191 References 192 9 Optimisation of yarn quality, cost and process parameters 195 9.1 Introduction 195 9.2 Components for optimisation 196 9.3 Technological value of cotton mix 197 9.3.1 Cotton fiber quality 197 9.3.2 Cotton cost 198 9.4 Optimisation of process parameters 200 9.5 Optimisation of yarn technological value 202 9.5.1 End product added value 204 9.5.2 Optimum fiber quality utilisation for target yarn by the use of EFS® system 206 9.5.3 Optimum fiber quantity utilisation efficiency for target yarn by the use of EFS® system 211 9.6 Summary 214

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