NOPR Collection:

Role of fibre, yarn and fabric parameters on bending and shear behaviour of plain woven fabrics

Fri, 01 Mar 2019 00:00:00 GMT

Title: Role of fibre, yarn and fabric parameters on bending and shear behaviour of plain woven fabrics Authors: Alam, Md Samsu; Majumdar, Abhijit; Ghosh, Anindya Abstract: Influence of fibre blend, yarn count and fabric sett (thread density) on bending and shear rigidities of plain woven fabric has been studied. Fifteen plain woven square fabrics have been woven using 20, 30 and 40 Ne yarns of three different blends (100% cotton, 100% polyester and 50:50 polyester-cotton). The fabric samples are produced at three levels according to the Box and Behnken design of experiment methodology. Fabric bending and shear rigidities are measured by using Kawabata Evaluation System (KES) at low stress region. An increasing trend of fabric bending and shear rigidities are observed with lower proportion of polyester, coarser yarn count and higher fabric sett. Yarn count is found to be the most important parameter influencing fabric bending and shear rigidities followed by fabric sett and blend proportion of polyester. A strong degree of association is found between bending and shear rigidities of fabric. Page(s): 9-15

Application of RSM to optimise single locking cotton feeder for enhancing ginning efficiency of double roller gin

Fri, 01 Mar 2019 00:00:00 GMT

Title: Application of RSM to optimise single locking cotton feeder for enhancing ginning efficiency of double roller gin Authors: Arude, V G; Deshmukh, S P; Patil, P G; Shukla, S K Abstract: Spike cylinder single locking cotton feeder has been developed and optimized to enhance the ginning efficiency of double roller (DR) gin. The feeder is developed with an aim to unlock the cotton bolls and maintain constant feeding rate of individual locules at the ginning point of DR gin. Spike cylinder speed and cotton moisture content are optimized by using response surface methodology. Ginning efficiency of DR gin is improved with the use of developed feeder. Quadratic models for prediction of ginning output and specific energy and linear model for prediction of reduction in bulk density are generated by using response surface methodology following central composite design that show excellent agreement with the experimental values. Multiple response analysis shows the optimum level of moisture content (7.49%) and spike cylinder speed (317 rpm) with desirability of 0.904695 by maximizing the output and minimizing the specific energy. The ginning output, cleaning efficiency and reduction in bulk density are increased by 23.25%, 16% and 30.5% respectively, whereas the specific energy is decreased by 12% without any adverse effect on fibre quality. Colour grade of the cotton improves from middling to strict middling. Thus, the developed feeder would be highly useful for cotton ginneries. Page(s): 16-23

Development of sportswear with enhanced moisture management properties using cotton and regenerated cellulosic fibres

Fri, 01 Mar 2019 00:00:00 GMT

Title: Development of sportswear with enhanced moisture management properties using cotton and regenerated cellulosic fibres Authors: Bait, Smita Honade; Shrivastava, Neeraj; Behera, Jagadananda; Ramakrishnan, Vijay; Dayal, Amit; Jadhav, Ganesh Abstract: The effect of fibre composition on moisture management properties and peak heat flux (q_max) values of one commercial sport garment and six knitted fabrics (sportswear), composed of 100% polyester, 100% cotton, 100% modal, and blend of polyester with cotton and modal, have been investigated. The moisture management properties are assessed by using the moisture management tester, and the feeling of coldness or warmth is assessed by measuring q_max value on KES-F7 Thermo labo II. Blending polyester fibre with cotton and modal has improved moisture management properties of the fabrics in comparison to 100% polyester fabric. q_max study also indicates that polyester/cotton and polyester/modal blend fabrics are cooler as compared to 100 % polyester fabric. Page(s): 24-30

Prediction of rotor-spun yarn quality using hybrid artificial neural network-fuzzy expert system model

Fri, 01 Mar 2019 00:00:00 GMT

Title: Prediction of rotor-spun yarn quality using hybrid artificial neural network-fuzzy expert system model Authors: Ghanmi, Hanen; Ghith, Adel; Benameur, Tarek Abstract: This study aims at developing a new approach to predict and determine the quality of rotor-spun yarn in terms of fibre characteristics as well as critical yarn properties. Hybrid modeling by combining two or more techniques has been demonstrated to give better performance than that of several single approaches over many research areas. Hence, in this study a hybrid model by combining two soft computing approaches, namely artificial neural network (ANN) and fuzzy expert system, has been developed. The ANN is used to predict three yarn characteristics, namely tenacity, breaking elongation and CVm. Then these three outputs are used to predict the new quality index by means of the fuzzy expert system. The accuracy of predicted model has been estimated using statistical performance criteria, such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and mean relative per cent error (MRPE). The results show the ability of model to predict the rotor-spun yarn quality and according to the analytical findings, the hybrid model gives accurate result. Page(s): 31-38