STUDIES ON UNIAXIAL TENSILE STRENGTH OF COTTON WOVEN FABRICS by SWAPNA MISHRA Department of Textile Technology Submitted In fulfillment of the requirements of the degree of Doctor of Philosophy to the INDIAN INSTITUTE OF TECHNOLOGY DELHI January, 2013
Indian Institute of Technology, New Delhi, 2013
Dedicated to My daughter Urvi
CERTIFICATE This is to certify that the thesis titled Studies on Uniaxial Tensile Strength of Cotton Woven Fabrics, being submitted by Ms. Swapna Mishra to the Indian Institute of Technology, Delhi, for the award of degree of Doctor of Philosophy is a record of bonafide research work carried out by her. Ms. Swapna Mishra has worked under our guidance and supervision and fulfilled the requirements for the 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. A. Majumdar Dr. B. S. Butola Associate Professor Assistant Professor Department of Textile Technology Department of Textile Technology I.I. T. Delhi I.I. T. Delhi New Delhi - 110 016 New Delhi 110 016 India India i
ACKNOWLEDGEMENTS First and foremost, I acknowledge the love and blessings of the invisible force the Almighty, for constant guidance and support throughout the Ph.D. work and through the intricacies of life and helping me to improve constantly as a human being. I would like to thank Prof. P.K. Banerjee for his supervision during the initial part of the research work. My sincere thanks and gratitude are due to my supervisors Dr. Abhijit Majumdar and Dr. B.S. Butola for their patience, support, guidance and encouragement during this study. I wish to thank the S.R.C. members Prof. V.K. Kotahri, Prof Alagirusamy and Prof. S.K. Gupta and to Prof. Manjeet Jassal, the Ph.D. Coordinator for their guidance and suggestions during the entire period of research work. The discussions and guidance provided by Prof. V.K. Kothari were extremely helpful and need a special mention. I am thankful to Prof. Blahoslav Neckář, University of Liberce for discussion on topic during his visits to I.I.T. Delhi. A special mention and thanks are due to Mr.Sachit Jain, Executive Director, Vardhman Textiles Limited, India for permitting the manufacturing of fabrics for the research work at C.P.D.C. Mahavir Spinning Mills Textile Division, Vardhman Textiles Limited, Baddi, H.P., India. The efforts of Mr. Sukhwant Singh Bains (Chief Manager-C.P.D.C.) and Mr. Rajeeb (Manager-Weaving, C.P.D.C.) are appreciated for ensuring that the fabrics were prepared according to specifications and within a short span of time. ii
I am thankful to Mr. Somesh Bhoumick (Vice President) at Shahi Exports Pvt. Ltd. (Unit- Sarla Fabrics Ltd. Ghaziabad, U.P.), India for facilitating processing of fabrics in relaxed state. Sincere thanks are due to my employer G.N.D.U. Amritsar for granting study leave to pursue Ph.D. at I.IT. Delhi and for granting leaves due from time to time for compilation work. My sincere thanks to all the faculty members, research scholars and staff members of Department of Textile Technology, IIT Delhi, with special appreciation to Mr. B. Biswal, Textile Testing Lab for being enthusiastically supportive during the tests conducted. Last, but not the least, I am thankful to my family and friends for their unconditional love and support. (Swapna Mishra) iii
ABSTRACT The present work aims at understanding the factors affecting the uniaxial tensile strength of 100% cotton woven fabrics. On a running loom, it is easier to adjust the interlacement pattern, weft count and pick density as compared to other parameters. Hence the focus of this work is mainly restricted to changes in these parameters. Four types of fabrics in plain, 2/2 twill, 3/1 twill and 4-end broken twill weave prepared on air-jet looms were studied. An attempt was made to correlate the change in tensile behavior of fabric with the change in weave, weft count and/or pick density. Percentage yarn strength utilization in the fabric (% SU) has been used for comparing fabric strength. The study has been carried on two sets of fabric. The first set had eight fabrics in which four fabrics were in each of the four weaves at 22 picks per cm (ppcm). Plain and broken twill fabrics at 18 and 26 ppcm formed the next four fabrics in the first set. The second set had 36 fabrics, nine (with three different weft counts and three pick densities) in each of the four weaves. The first set of fabrics was employed to experimentally study the effect of weave, direction of testing and pick density on tensile strength and strain developed in the specimen of different fabrics during tensile deformation. The tensile tests revealed that not only the interlacement pattern and pick density, but the direction of testing also affects the results obtained. Among all the weaves at a given pick density, plain fabric exhibited the maximum % SU when tested in the weft direction, but the minimum value when tested in the warp direction. The reverse was observed for broken twill fabric which showed the maximum % SU among all the weaves at a given pick density when tested in the weft direction but least iv
% SU when tested along the warp. This was true at all three pick densities. The twills lay somewhere in between the plain and broken twill in terms of % SU. The difference in tensile behavior of 3/1 twill and broken twill constructions in both warp and weft directions of testing, in spite of both having the same interlacement pattern, indicated that variation in the distribution of interlacement also influences the % SU. Lower % SU was observed in specimens tested in the warp direction compared to those tested in the weft, for a given weave and pick density. The initial crimp % is much higher in the warp direction, and it is possible that all the crimp may not have been removed during the process of tensile deformation. Therefore, warp yarns may still be in crimped condition at the time of failure. Hence, they would be inclined to the fabric plane and will not contribute fully towards load bearing. A higher initial crimp would also mean higher widthwise contraction due to crimp interchange. The load bearing yarns would be more inclined to the direction of loading and lead to lower % SU. The results of strain analysis in the specimens, which are explained later, support this. The calculation of % SU involved normalization against the number of load bearing yarns. Therefore, a significant change in % SU of samples tested in the weft direction was not expected due to increase in the pick density (number of load bearing yarns per unit length). On the contrary, it resulted in a marked increase in % SU of specimens tested in weft direction. Moreover, the rise in % SU due to increase in the number of load bearing yarns was found to be higher than that due to an equivalent increase in the number of transverse yarns. This proves that the number of load bearing yarns is critical in deciding % SU. A change in pick density was expected to increase the % SU of specimens tested in the warp direction as it increased the number of transverse yarns. This is because higher v
number of transverse yarns would result in more gripping or interlacement points, providing higher fabric assistance. The experimental results confirmed this. It was also observed that irrespective of the weave or pick density, all the specimens tested in the warp direction demonstrated catastrophic tear breaks near the moving jaw of the tensile testing instrument while the specimens tested in the weft direction showed multiple breaks. Visual examination of the specimens suggested the presence of shear near the jaws when specimens are tested in the warp direction. A possible cause could be non uniformity in the strain developed in the specimens. Strain studies were conducted on specimens exhibiting highest, near average and lowest % SU in each fabric type to verify this possibility. A grid was marked on the test specimen and strain along different segments of the grid was calculated. A difference in strain pattern between specimens was observed. Specimens tested in weft direction showed more even distribution, while those tested in the warp direction exhibited concentration of strain near one of the jaws, suggesting the presence of shear force leading to tear like breaks near the jaws. The study on the first set comprising of eight different fabrics concluded that the initial crimp% and number of yarns in the load bearing and transverse direction, combined with the interlacement pattern collectively affect the tensile behavior of the woven fabrics. A structure which had higher number of pin-joints and allowed complete decrimping culminated in higher % SU. Although the parameters influencing % SU were identified, the exact influence of each factor could not be quantified. The second set of fabrics was used to verify the significance of above identified parameters critically influencing % SU. They were also used to quantify the influence of each parameter on % SU. The data obtained from testing of 36 fabrics in the second set vi
has been used to develop models based on multiple linear regression and artificial neural network for predicting % SU of fabrics. Single step and stepwise linear multiple regression methods were used to relate nine fabric parameters (weave and yarn count, yarn density, yarn strength and yarn crimp in both the directions of testing) with % SU. The single step method was found to give lower errors of prediction and better choice of parameters influencing % SU. The number of yarns in the load bearing and transverse directions (NL and NT), float length (FL) of the weave and crimp % in the load bearing yarns (% CL) were found to be statistically significant parameters affecting % SU at 95% confidence level (p < 0.025). While NL and NT were found to be positively influencing the % SU, while increase in FL and % CL were detrimental to the same. The data set consisting of 72 sets of results of fabric testing were divided into training and testing data. While 65 sets were used for training of the ANN based on back-propagation algorithm, seven were used for testing the network. The linear density, strength, initial crimp % and number of yarns per unit length in load bearing and transverse directions along with the float length of the weave were used as input parameters. Many networks were explored and the network giving least error of prediction was chosen as the best. Five factors were found to be crucial in deciding % SU for the range of fabrics studied. These factors are the number of load bearing (NL) and transverse yarns (NT) per unit length, the initial crimps in the load bearing (%CL) and transverse yarns (%CT) and the float length (FL) of the weave, in that order. While the first two factors have a positive effect, the rest influence the % SU negatively. The rise in % SU with an increase in the number of load bearing yarns is much higher compared to that corresponding to an increase in number of vii
transverse yarns by an equal amount. This is attributed to the increase in binding force of the transverse yarn on the load bearing yarn when the number of load bearing yarns increase. Increasing the number of transverse yarns increases the number of binding points per load bearing yarn and also reduces the length gripped between two consecutive interlacements along the load bearing yarn. When the crimp % in the load bearing direction is increased the % SU decreases. An increase in the crimp % in the transverse yarns also brings down the % SU but to a lesser extent and then seems to stabilize. Higher float length leads to lower number of interlacing or binding points in the specimen and hence lower % SU. The two approaches (MLR and ANN) were also compared in terms of their prediction accuracy. The ANN model was been found to give better prediction accuracy and more reasonable choice of significant parameters compared to MLR model as the latter is not able to handle non-linearity between input parameters and % SU. It is concluded collectively, from the tests on both the set of fabrics that within the range of fabrics studied, one must aim at a high number of load bearing and transverse yarns, combined with low values of crimps in the two directions and a small float length to achieve maximum % SU. The finding may also be used to manipulate the five factors to design fabrics which meet end-use requirements or to make alterations in an existing design to improve the performance in terms of the % SU achieved. viii
CONTENTS Page no. Certificate Acknowledgements Abstract Contents List of Figures List of Tables i ii iv ix xiii xvii Chapter 1. Introduction 1.1 Motivation for the work 1 1.2 Objectives 5 1.3 Structure of the thesis 5 Chapter 2. Literature review 2.1 Introduction 9 2.2 General structure and tensile behavior of a woven fabric 10 2.3 Factors to quantify weave 12 2.4 Weave factors and weavability limits 17 2.5 Understanding structure-property relation of woven fabrics 19 2.5.1 Experimental studies 19 2.5.2 Models to predict tensile behavior of woven fabrics 26 2.5.2.1 Geometrical models 26 2.5.2.2 Mechanistic models 30 2.5.2.3 Statistical models 40 2.5.2.4 Continuum constitutive and FEM based models 44 ix
2.5.2.5 Multiple Linear Regression based models 52 2.5.2.6 Artificial Neural Network based models 55 2.6 Summary 60 2.7 Gaps identified in literature 61 Chapter 3. Effect of fabric parameters on tensile behavior of woven fabrics 3.1 Introduction 63 3.2 Part A: Band of parallel yarns 64 3.2.1 Materials 64 3.2.2 Methods 64 3.2.3 Results and discussions 65 3.3 Part B: Fabrics 66 3.3.1 Materials 67 3.3.2 Methods 69 3.3.2.1 Fabric Relaxation method 69 3.3.2.2 Pre-treatment of fabrics 69 3.3.2.3 Testing Methods 70 3.3.2.4 Calculation of theoretical and expected strength and % 70 SU of fabrics 3.3.3 Results and discussions 72 3.3.3.1 Effect of interlacement pattern and crimp % on % SU of fabric 72 3.3.3.2 Effect of weave factor 78 3.3.3.3 Effect of direction of testing 79 3.3.3.4 Effect of pick density 84 3.3.3.5 The nature of breaks in the specimens 87 x
3.4 Conclusions 89 Chapter 4. Study on strain distribution in woven fabrics 4.1 Introduction 91 4.2 Materials and methods 93 4.2.1 Materials 93 4.2.2 Methods 93 4.2.2.1 Specimen marking 94 4.2.2.2 Specimen coding 95 4.2.2.3 Steps for measurement of strain distribution 95 4.2.2.4 Expressions of strain 96 4.2.2.5 Pin-joint diagram and strain profile 98 4.3 Results and discussions 98 4.3.1 Strain distribution in plain and broken twill fabrics 98 4.3.2 Tensile results and strain distribution in 3/1 twill and broken 107 twill fabrics 4.3.2.1 Fabric construction and tensile properties 108 4.3.2.2 Visual examination of the specimens 110 4.3.2.3 Strain distribution in 3/1 twill and broken twill specimens 111 4.4 Conclusions 114 Chapter 5. Modeling of yarn strength utilization in cotton woven fabrics using multiple linear regression 5.1 Introduction 117 5.2 Materials and Methods 119 5.2.1 Materials 119 xi
5.2.2 Methods 121 5.3 Results and discussions 123 5.4 Conclusions 130 Chapter 6. Prediction of yarn strength utilization in woven fabrics using artificial neural network 6.1 Introduction 133 6.2 Basics of Artificial Neural Network 134 6.2.1 Back Propagation Algorithm 136 6.3 Materials and Methods 139 6.3.1 Materials 139 6.3.2 Methods 140 6.3.2.1 Fabric characterization 140 6.3.2.2 Processing of experimental data using artificial neural 140 network 6.4 Results and discussions 141 6.4.1 Performance of various neural network models 141 6.4.2 Trend analysis 143 6.4.3 Comparison between MLR and ANN approaches 147 6.5 Conclusions 148 Chapter 7. Conclusions 149 Chapter 8. Suggestions for further work 153 References 155 Resume 171 Publications xii