Abhijit Majumdar

Predicting the breaking elongation of ring spun cotton yarns using mathematical, statistical, and artificial neural network models

This paper presents a comparative study of three modeling methodologies for predicting the breaking elongation of ring spun cotton yarns. Constituent cotton fiber properties and yarn count are used as inputs to these models. The predictive powers of the three different models—mathematical, statistical, and artificial neural network—are estimated and com pared. The relative importance of various cotton fiber properties measured by a high volume instrument is also investigated using the artificial neural network model.

Improving the Impact Resistance of Textile Structures by using Shear Thickening Fluids: A Review

This paper reviews the applications of shear thickening fluids (STF) to enhance the impact resistance behavior of textile materials. The mechanism of shear thickening has been presented in the form of ‘order disorder theory’ and ‘hydrodynamic clustering’. The applications of different shear thickening fluids on fabrics made from high performance fibers and their contribution in improving the impact performance have also been presented in this article.

Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties

Abstract This paper presents the application of a hybrid neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. The proposed system possesses the advantages of both artificial neural networks and fuzzy logic, and is thus more intelligent. HVI fibre test results are used to train the neuro-fuzzy inference system and its prediction performance is compared with those of artificial neural network and regression models. The prediction accuracy of the proposed neuro-fuzzy system is superior to that of a conventional multiple regression model and comparable with an artificial neural network model.

Determination of quality value of cotton fibre using hybrid AHP-TOPSIS method of multi-criteria decision-making

Abstract This paper presents a new multi-criteria decision-making approach to determine the quality value of cotton fibre. Major cotton fibre properties were considered and their relative importance or weights were determined by a typical pair-wise comparison method. Cotton fibres were ranked according to their relative closeness with respect to the best and worst possible alternatives. This ranking was compared with the ranking of yarn tenacity in two different yarn counts. It was found that there is significant agreement between the two rankings.

Application of Analytic Hierarchy Process for the Selection of Cotton Fibers

In many engineering applications, the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become very intricate when the selection criteria are expressed in terms of different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective way in dealing with such kind of complicated problems. Cotton fiber is selected or graded, in the spinning industries, based on several quality criteria. However, the existing selection or grading method based on Fiber quality Index (FqI) is rather crude and ambiguous. This paper presents a novel approach of cotton fiber selection using the AHP methodology of Multi Criteria Decision Making.

Development and performance optimization of knitted antibacterial materials using polyester-silver nanocomposite fibres.

The development and performance optimization of knitted antibacterial materials made from polyester-silver nanocomposite fibres have been attempted in this research. Inherently antibacterial polyester-silver nanocomposite fibres were blended with normal polyester fibres in different weight proportions to prepare yarns. Three parameters, namely blend percentage (wt.%) of nanocomposite fibres, yarn count and knitting machine gauge were varied for producing a large number of knitted samples. The knitted materials were tested for antibacterial activity against Gram-positive bacteria Staphylococcus aureus. Statistical analysis revealed that all the three parameters were significant and the blend percentage of nanocomposite fibre was the most dominant factor influencing the antibacterial activity of knitted materials. The antibacterial activity of the developed materials was found to be extremely durable as there was only about 1% loss even after 25 washes. Linear programming approach was used to optimize the parameters, namely antibacterial activity, air permeability and areal density of knitted materials considering cost minimization as the objective. The properties of validation samples were found to be very close to the targeted values.

Modelling of thermal conductivity of knitted fabrics made of cotton–bamboo yarns using artificial neural network

Regenerated cellulosic fibre made from bamboo is gaining popularity for apparel use due to its improved functional properties. This paper presents the modelling of thermal conductivity of knitted fabrics made from blended yarns of cotton and bamboo fibres using an artificial neural network (ANN). Five parameters, namely knitted fabric structure type, yarn linear density, bamboo fibre proportion (%), fabric thickness and fabric areal density, were used as inputs to the ANN model. The developed model was able to predict the thermal conductivity of fabrics with very good accuracy. The trend analysis of the developed model revealed the influence of various input parameters on the thermal conductivity of knitted fabrics. These findings can be judiciously used for the selection of optimum material and structural parameters of knitted cellulosic fabrics for a particular end‐use.

Effect of weave, structural parameters and ultraviolet absorbers on in vitro protection factor of bleached cotton woven fabrics

The weave, fabric cover, areal density and ultraviolet (UV) absorbers are some of the factors which influence the ultraviolet protection factor (UPF) of cotton fabrics. It will be of interest to know whether fabric cover or fabric areal density is a better predictor of cotton fabric UPF. It will also be of interest to know whether the UV absorbers are equally effective for all kinds of cotton fabric.

An investigation on yarn engineering using artificial neural networks

Abstract Engineering of spun yarns having specific tensile, evenness and hairiness characteristics is a long-cherished dream of spinning technologists. Selection of suitable raw materials at minimum cost and optimisation of process parameters are the two major tasks to be achieved to manufacture engineered yarn. Advent of high-speed fibre-testing machines and development of powerful modelling tools such as artificial neural network (ANN) have provided a great impetus in the yarn engineering research. This article demonstrates the feasibility of yarn engineering by developing a yarn-to-fibre ‘reverse’ model, using ANN. This approach is entirely different from the prevailing forward models, which predict the properties of final yarn using the fibre properties as inputs. The cost minimisation of cotton fibre mix was ensured by using the classical linear programming approach in combination with ANN. The engineered yarns demonstrated good agreement with the target yarn properties.

Soft computing in fibrous materials engineering

Soft computing is a cluster of modelling and optimisation techniques which mimics the behaviour of biological systems. Artificial neural network (ANN), fuzzy logic (FL) and genetic algorithms (GA) are three main constituents of soft computing. In recent years, soft computing systems have been successfully used in every discipline of science, technology and engineering. Fibrous materials possess a unique combination of characteristics as they are strong, flexible and light-weight. Therefore, fibrous materials are gaining increased attention with time from the materials scientists and engineers. When fibrous materials are used for technical applications, the requirement in terms of functional properties becomes more important than the aesthetics. In certain cases, it becomes imperative to get an idea about the properties of the fibrous materials before their manufacturing. As the fibrous materials have inherent variability, estimation of their properties by mathematical models often yields a very high prediction error. Soft computing systems present the potential solutions for the modelling and optimisation of fibrous materials. This monograph presents a compendium of researches on the application of soft computing techniques in fibrous materials modelling, optimisation and engineering.