Anindya Ghosh

Nanotechnology in fibrous materials–a new perspective

This issue reviews various areas where nanotechnology has come up predominately in fibrous materials, namely in electrospun polymeric nanofibers and polymer layered silicate nanocomposites. It includes synthesis, characterization, various methods of collecting nanofibers, factors affecting electrospinning, methods of increasing the productivity of the electrospinning process, and different electrospinning designs. It also covers synthesis and characterization of polymer nanocomposites. Various properties of nanocomposites are discussed. The rheological behavior and morphology of nanocomposites are covered. Different modeling and simulation methods applicable to electrospun nanofibers and polymer layered silicate nanocomposites are discussed. Some of the potential application areas of electrospun nanofibers, polymer layered silicate nanocomposites, and various products available in the market based on nanotechnology are also discussed. Some of the lacking areas and future prospects in nanofibrous structures (nanofibers and nanocomposites) are emphasized in this issue.

Stress–strain characteristics of different spun yarns as a function of strain rate and gauge length

Abstract The influence of strain rates and gauge lengths on the characteristics of the stress–strain curves of ring, rotor, air-jet, and open-end friction spun yarns is investigated. A modified form of Vangheluwes model is used in describing the stress–strain characteristics of spun yarns. The proposed model can fairly well replicate these characteristics.

Pattern classification of fabric defects using support vector machines

Purpose – The purpose of this paper is to address a solution to the problem of defect recognition from images using the support vector machines (SVM).Design/methodology/approach – A SVM‐based multi‐class pattern recognition system has been developed for inspecting commonly occurring fabric defects such as neps, broken ends, broken picks and oil stain. A one‐leave‐out cross validation technique is applied to assess the accuracy of the SVM classifier in classifying fabric defects.Findings – The investigation indicates that the fabric defects can be classified with a reasonably high degree of accuracy by the proposed method.Originality/value – The paper outlines the theory and application of SVM classifier with reference to pattern classification problem in textiles. The SVM classifier outperforms the other techniques of machine learning systems such as artificial neural network in terms of efficiency of calculation. Therefore, SVM classifier has great potential for automatic inspection of fabric defects in ...

Yarn engineering using hybrid artificial neural network-genetic algorithm model

This work aims to manufacture cotton yarns with requisite quality by choice of suitable raw materials for a given spinning system. To fulfill this aim, a hybrid model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been developed which captures both the high prediction power of ANN and global solution searching ability of GA. In an attempt to achieve a yarn having predefined tenacity and evenness, a constrained optimization problem is formulated with the ANN input-output relation between fibre and yarn properties. GA has been used to solve the optimization problem by searching the best combination of fibre properties that can translate into reality a yarn with the desired quality. The model is capable in identifying the set of fibre properties that gives requisite yarn quality with reasonable degree of accuracy.

Multi-objective optimization of air permeability and thermal conductivity of knitted fabrics with desired ultraviolet protection

A simultaneous optimal solution of two objectives, namely air permeability and thermal conductivity has been derived for both single jersey and 1 × 1 rib knitted fabrics with desired ultraviolet (UV) protection. As these two objectives are conflicting with each other, a set of optimal solutions are possible which are non-dominating in nature. These set of optimal solutions are known as Pareto optimal solutions. In this work, the Pareto optimal solutions were derived with an elitist multi-objective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm (NSGA II). These Pareto optimal solutions helped to obtain the effective knitting and yarn parameters to engineer knitted fabrics with optimal comfort and desired level of UV protection.

A technique of cotton bale laydown using clustering algorithm

A new technique of cotton bale management using clustering algorithm has been proposed. The method is based on the grouping cotton bales of similar kind into respective categories using k-mean square clustering algorithm. A set of 500 cotton bales were clustered into 5 categories by minimizing the total within-group squared Euclidean distance around the 5 centroids. In order to cluster bales of different categories, 8 fibre properties, viz., strength, elongation, upper half mean length, length uniformity, short fibre content, micronaire, reflectance and yellowness of each bale have been considered. Once the bales are clustered into different categories, it is possible to prepare consistent bale mix for consecutive laydowns on the basis of frequency relative picking method.

Tensile and twist failure of mulberry and tasar silk

Abstract This article investigates the tensile and twist failure of mulberry and tasar silk filaments. The mulberry and tasar filaments were subjected to uniaxial loading on Instron tensile tester at different rates of extension and gauge lengths. Furthermore, the number of turns to rupture the silk filaments was tested using a twist tester. The results showed that the mulberry filament has a higher tensile and twist strength than the tasar filament. The SEM photomicrographs of the region of fracture divulged that the tensile and twist failure of mulberry and tasar filaments takes place in catastrophic and non-catastrophic modes, respectively.

Engineering of Knitted Cotton Fabrics for Optimum Comfort in a Hot Climate

Cotton knitted fabrics are popular for summer-wear and outer-wear due to their comfort. The typical porous structure of knitted fabrics, however, increases the risk of exposure of human skin to UV rays, resulting in skin cancer. Therefore a trade-off is required between the comfort and UV ray resistance of the fabric. In this study, an attempt was made to engineer single jersey and 1×1 rib knitted fabrics with optimum comfort and desired UV resistance. It was found that 1×1 rib knitted fabrics could provide better comfort and UV protection with respect to single jersey fabrics manufactured on the same gauge knitting machine.

Raw Jute Grading By Multi-Criteria Decision Making Method

Six quality parameters of jute fibers, that is, strength, defect, root content, color, fineness, and bulk density are regarded as the criteria for grading of raw jute. The relative weights for different criteria are estimated using Analytic Hierarchy Process (AHP). The criteria such as strength, root content, and fineness are measured experimentally, whereas defect, color, and bulk density are determined subjectively. The 10 Tossa jute lots are ranked based on the TOPSIS method of Multi-Criteria Decision Making (MCDM). The ranking of jute attained by this method shows a significant agreement with that of traditional method as practiced in India.

Forecasting of Cotton Yarn Properties Using Intelligent Machines

An intelligence machine is a computer program that can learn from experience, i.e. modifies its processing on the basis of newly acquired information and thereafter makes decisions in a rightfully sensible manner when presented with inputs. Examples of such machine learning systems are artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic, evolutionary computation, etc. The prediction of cotton yarn properties from constituent fibre properties is quite significant from a technological point of view. Regardless of the relentless efforts made by researchers, the exact relationship between fibre and yarn properties has not yet been decisively recognized. The intelligence machine, which is a potent data-modeling tool in capturing complex input-output relationships, seems to be the right approach to decipher the fibre-to-yarn relationship. In this work, various cotton yarns properties, such as strength, elongation, evenness and hairiness, have been predicted from fibre properties by us...