Search results

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

Cotton fibre lots are graded and selected for yarn spinning based on their quality value which is a function of certain fibre properties. Cotton grading and selection has created a domain of emerging interest among the researchers. Several researchers have addressed the said issue using a few exponents of multi-criteria decision-making (MCDM) technique. The purpose of this study is to demonstrate a cotton selection problem using a recently developed measurement of alternatives and ranking according to compromise solution (MARCOS) method which can handle almost any decision problem involving a finite number of alternatives and multiple conflicting decision criteria.

The MARCOS method of the MCDM technique was deployed in this study to rank 17 cotton fibre lots based on their quality values. Six apposite fibre properties, namely, fibre bundle strength, elongation, fineness, upper half mean length, uniformity index and short fibre content are considered as the six decision criteria assigning weights previously determined by an earlier researcher using analytic hierarchy process.

Among the 17 alternatives, C9 secured rank 1 (the best lot) with the highest utility function (0.704) and C7 occupied rank 17 (the worst lot) with the lowest utility function (0.596). Ranking given by MARCOS method showed high degree of congruence with the earlier approaches, as evidenced by high rank correlation coefficients (Rs > 0.814). During sensitivity analyses, no occurrence of rank reversal is observed. The correlations between the quality value-based ranking and the yarn tenacity-based rankings are better than many of the traditional methods. The results can be improved further by adopting other efficient method of weighting the criteria.

The properties of raw cotton have significant impact on the quality of final yarn. Compared to the traditional methods, MCDM is reported as the most viable solution in which fibre parameters are given their due importance while formulating a single index known as quality value. The present study demonstrates the application of a recently developed exponent of MCDM in the name of MARCOS for the first time to address a cotton fibre selection problem for textile spinning mills. The same approach can also be extended to solve other decision problems of the textile industry, in general.

Novelty of the present study lies in the fact that the MARCOS is a very recently developed MCDM method, and this is a maiden application of the MARCOS method in the domain of textile, in general, and cotton industry, in particular. The approach is very simple, highly effective and quite flexible in terms of number of alternatives and decision criteria, although highly robust and stable.

Bacterial exopolysaccharides (eps) have fascinating chemical compositions, properties and structures which could be used in the modification of natural fibres. Bacterial eps have therefore been used to modify plant cellulose fibre surface and impart desired properties. The purpose of this paper is therefore to investigate the influence of gin trash cultured bacteria eps on the physical and structural properties of cotton fibres.

Gin trash soil sample was collected from a ginnery in Kenya, and physiochemical and microbial characterization was done. The soil sample was then fermented for 24 h before being used to treat raw cotton fibres at varied conditions of temperature, pH and treatment time periods. Physical and structural properties of the treated fibres were then determined using USTER HVI-1000 M700, Fourier transform infrared, scanning electron microscope (SEM) and X-ray diffraction (XRD) and compared with those of the raw fibres.

The bacteria broth treated fibres were found to have increased in strength, spinning consistency index, elongation and fineness by 25.44, 24.30, 11.70 and 3.60%, respectively. The variations were attributed to interactions of bacterial eps with cotton cellulose through hydrogen bonding. SEM and XRD analysis revealed an increase in fibre surface roughness and crystallinity, respectively.

Bacterial eps have been used to modify plant cellulose fibre surface and impart desired properties. Eps producing bacteria have been isolated from different habitats such as saline water, soil samples, food wastes and petroleum-contaminated soil. To the best of the authors’ knowledge, bacterial eps cultured from gin trash soil sample for modification of cotton fibres have however not been previously done, hence the originality of the current study.

In spinning industries, selection of the most appropriate fibre for yarn manufacturing plays an important role for achieving an optimal mix of several yarn characteristics, like maximum tenacity, elasticity and spinning ability; and minimum unevenness and hairiness. Identification of the best suited cotton fibre from a set of available alternatives in presence of different conflicting physical properties is often treated as a multi-criteria decision-making (MCDM) problem. The paper aims to discuss this issue.

In this paper, the preference ranking organisation method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) methods are integrated to solve a cotton fibre selection problem. The PROMETHEE II method ranks the alternative cotton fibres based on their net outranking flows, whereas GAIA acts as a visual aid to strongly support the derived selection decision. The weight stability intervals for all the considered fibre properties (criteria) over which the position of the top-ranked cotton fibre remains unchanged are also determined.

The clusters of cotton fibres formed in the developed GAIA plane act as a yard stick for their appropriate grading to aid the blending process. The ranking of 17 cotton fibres as achieved applying the combined PROMETHEE-GAIA approach highly corroborates with the observations of the past researchers which proves its immense potentiality and applicability in solving fibre selection problems.

Two MCDM methods in the form of PROMETHEE II and GAIA are integrated to provide a holistic approach for cotton fibre grading and selection while taking into consideration all the available cotton fibre properties.

The purpose of this study was to produce yarns from three different spinning techniques, i.e.Murata Vortex Spinning (MVS) ring spinning and rotor spinning. Those yarns were then used to produce fabrics. Then, the effect of silicone softener on tactile comfort of fabric was investigated.

Three different yarns, i.e. Ring, Rotor and MVS yarns, were used to make fabrics using CCI sample loom which were then subjected to post treatments like desizing, scouring and bleaching. After the completion of the dyeing process, silicone-based softener was used to improve the hand feel of fabrics. The structures of three yarns were evaluated using Scanning electron microscopy. The fabrics were evaluated against compression, bending and surface properties using Kawabata evaluation system.

The fabric made of MVS yarn depicted more geometrical roughness, coefficient of friction and bending rigidity but less compressibility as compared to fabrics made with other yarns. It was observed that softener concentration has a direct relationship with thickness and bending rigidity of the fabric, and inverse relationship with coefficient of friction and geometrical roughness of the fabric.

MVS yarn has some superior properties over rotor and ring spun yarn like high production rates, high resistance to pilling, clear appearance and stability against deformation but has disadvantage that it has less compressibility. Therefore, softener is applied on the fabric, to address this issue, so that it could also be used for apparels application.

Jacquard woven fabrics are widely used in various sections of upholstery industry, where mattress cover is one of them. Strength of jacquard woven mattress fabric depends on several factors. The objective of this study is to model the multi‐linear relationship between fibre, yarn and fabric parameters on the strength of fabric using artificial neural network (ANN) and Taguchi design of experiment (TDOE) methodologies.

TDOE was applied to determine the optimum design values and the contribution of each parameter. Robustness (performance) of models is measured by root mean squared error (RMSE). These tools will enable the user to predict the fabric strength from number of given inputs. It also provides the knowledge related to the contribution of fibre, yarn and fabric parameters on fabric strength. Fabrics tested in this study made from different fibre types and max/min level for several fabric and yarn‐related parameters. The models generated with TDOE and ANN methodologies were compared with the actual experimental data.

It was found that ANN model gives better approximation with the minimum RMSE.

The data taken from factory are related with jacquard woven fabric.

This study has many practical implications that brings up a general approach for textile industry. During manufacturing, waste or scrap ratio can be reduced and production planning become more efficient.

Firstly, before starting manufacturing in factory, we can easily predict the strength of woven fabric using the defined factors. This makes the model usable at the planning stage of the fabric. Secondly, the contribution of factors affecting fabric strength was determined. The ANN model generated in this study helps the engineers of planning department at the company easy to plan the manufacturing of fabric with a good estimation of fabric strength before the production order.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Spun silk is one of the top grade textile materials, and its products have high added value and meet the needs of the market. However, the technology level and process design of silk spinning are still much lower than cotton spinning; especially singeing is applied on spun silk yarn, and generates waste materials. The purpose of this paper is to introduce a kind of pneumatic compact spinning, four-line compact spinning (FLCS), into silk spinning and study the corresponding spun yarn qualities.

First, taking the silk spinning frame FK501 as an example, the process of modification of FLCS is presented. Then, three kinds of spun silk yarns, 80 Nm (12.5tex), 100 Nm (10tex) and 120 Nm (8.3tex), are spun on the common silk spinning frame FK501 and the spinning frame modified with FLCS. The evenness, breaking strength and hairiness of spun yarns are tested and comparatively analyzed. After the ply yarn production, three singeing procedures should be applied on the ring ply yarns, while only two singeing procedures should be applied on the compact ply yarns, which is beneficial for material saving.

The results show that compared with ring spun silk yarns, the comprehensive quality of compact spun silk yarns is improved, especially the harmful long hairiness (=3 mm) of yarn. Compared with the single spun silk yarn, the comprehensive qualities of the ply yarn are improved; especially, the breaking strength of the ply yarns is two times larger than the single yarn. After singeing, the hairiness of the ply yarn is decreased greatly, and the evenness is also improved, while the strength is decreased. Compared with ring spun silk yarn, the singeing times of compact spun silk yarn can be decreased, and the gas consumption in each singeing is also decreased, which is beneficial for material saving.

In this paper, a kind of pneumatic compact spinning, FLCS, is introduced into the silk spinning. It is shown that compared with ring spun silk yarns, the comprehensive quality of compact spun silk yarns is improved, especially the harmful long hairiness (=3 mm) of yarn. After the ply yarn production, three singeing procedures should be applied on the ring ply yarns, while only two singeing procedures should be applied on the compact ply yarns, which is beneficial for material saving.

The purpose of this paper is to focus on the use of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry.

The relative importance of multiple evaluation criteria and the extension of the TOPSIS are prioritized using AHP. The TOPSIS method is applied to compensate for the imprecise ranking of the AHP in the selection of a maintenance policy mix.

An efficient ranking of alternatives can be achieved for maintenance strategy selection through the combination of AHP and TOPSIS.

The paper highlights a new insight into multi‐criteria decision‐making techniques to select an optimum maintenance policy for a process industry with the use of a case study.