Cotton Yarn Strength Research Articles

Decision Rule Prediction for Assessment of Rotor Spun Cotton Yarn Strength Using Rough Set

ABSTRACT Being an agro-based natural product, cotton always poses vagueness in the parameters. Hence, formulation of decision rules for prediction of cotton yarn quality from the imprecise cotton fiber properties is an intricate problem. Yarn tenacity is considered an imperative quality parameter in the cotton textile industry. Modelling of yarn tenacity has remained as the cynosure of research for textile engineers. In recent years, rough set theory has evolved as one of the most important techniques used for handling imprecise data. One of the cardinal uses of rough set theory is its application to rule generation. Our approach focuses on the elimination of the redundant data set in order to generate effective decision rules which retain the accuracy of the original data set. In this work rough set theory is employed to generate decision rules to predict yarn tenacity from six input parameters. The validation results prove that the generated 45 decision rules accurately predicted 14 out of 16 unknown test data. Thus, in the present competitive market, this model is potent for getting recognition from the textile industry.

New techniques for improving the quality of cotton yarn using natural dyes from teak leaves (Tectona grandis), ketapang leaves (Terminalia catappa), and tender skin (Lannea coromandelica)

Sade village woven fabric is one of Lombok’s superior woven fabrics. The process of producing this woven fabric is traditional by using yarn spun from cotton. The purpose of this study is to obtain the optimal strength of cotton yarn using natural dyes. Mordan stage and fixation using alum solution. The coloring stage uses teak leaves, ketapang leaves, and banten skin. All three ingredients are dissolved in water with concentrations 1:10, 1:8, and 1:5. In the mordan stage, 8 grams of alum is dissolved in 1 liter of water. While at the fixation stage, 50 grams of alum is dissolved in 1 liter of water. Teak leaves produce a dark brown color, ketapang leaves produce turmeric yellow color, and banten skin produces a brick red color. After going through the coloring process, the yarn is tested using Tensilon RTG. The result show that the yarn strength increase during the coloring process. In addition, differences in the concentration of the solution also affect the strength of the yarn produced. Solution with a concentration ratio of 1: 8 produces optimal tensile strength of 0.3450 cN/dtex on teak leaves, 0.3369 cN/dtex on ketapang leaves, and 0.2450 cN/dtex on banten skin.

Experimental determination of cotton yarn strength at different speeds of movement obtained by various technological methods

Our results on the strength parameters of cotton yarns manufactured by carded and combed systems by ring and rotor spinning (CD-carded ring-spun, OE-carded rotor-spun, CM-combed ring-spun) are presented. Experiments to test yarns for strength were conducted on a “Statimat C” tensile installation at speeds of the lower end of the yarn from 100 mm/min to 1000 mm/min. From the diagrams of yarn stretching to breaking, the changes in breaking load, specific breaking load, critical strain values at which the yarn breaks, time of stretching before breaking, depending on the strain rate for the considered types of yarns, were determined. The analysis of the experimental results obtained shows the quantitative and qualitative characteristics of the change in strength parameters of three varieties of yarns.

Microstructure, morphology and strength of cotton yarns sized by collagen solution

For sizing cotton yarn before weaving, a new composition based on a biodegradable polymer—collagen, has been proposed. The detected changes in the FT-IR spectra of the yarn coated with a collagen solution indicate the obvious formation of new intermolecular hydrogen bonds between the primary and secondary amino groups of the collagen and the cellulose hydroxyl groups. The formation of such bonds contributed to the strengthening of the collagen film on the surface of cotton threads. SEM studies have shown a significant improvement in the microstructure of threads and the morphology of fibers compared with threads not sized and sized with starch. When sizing with a collagen solution, the diameter of the threads decreases by 14%, when sizing with a starch solution—by 18%. In cases of collagen sizing, along with bonding, compaction, a longitudinal orientation of the fibers is observed. The diameter of the cellulose fibers sized with collagen is in the range of 12–15 microns. Unlike starch, which fills the interfiber spaces and even penetrates into individual tubular fibers, collagen is evenly located only on the surface of the fibers. Improving the microstructure and morphology of cellulose fibers and threads when sizing with a collagen-based composition leads to a reduction in the consumption of sizing agent, an increase in the linear density of threads by 15–20%, an increase in breaking load by 25–35%, and a decrease in elongation at break by 15–20%.

Amphipathic Starch with Phosphate and Octenylsuccinate Substituents for Strong Adhesion to Cotton in Warp Sizing

The objectives of this work were to survey the effect of amphipathic modification of starch on the adhesion to cotton fibers for improving the adhesion of starch to cotton in warp sizing. The amphipathic starch (AS) with oleophilic octenylsuccinate and hydrophilic phosphate substituents was prepared by the phosphorylation with sodium tripolyphosphate (STP) followed by the octenylsuccinylation with 2-octenylsuccinic anhydride (OSA). Two series of AS samples with differential total degrees of substitution (DSt) and substituent ratios of phosphates to octenylsuccinates were evaluated by fourier transform infrared (FTIR) analysis, degree of substitution, adhesion to cotton and surface tension. The adhesion of the starch to fibers was investigated using a legal method (FZ/T 15001-2008). The FTIR spectra revealed that octenylsuccinates and phosphates have been attached to the backbones of the starch. The amphipathic modification of starch with STP and OSA was an effective method to enhance the adhesion of corn starch to cotton. The strong adhesion of the AS to cotton was attributed to the reduced surface tension arisen from oleophilic octenylsuccinate and hydrophilic phosphate substituents and increased steric hindrance of the substituents introduced onto starch. The investigation showed that the improvement in the adhesion of the starch after amphipathic modification could be buttressed by the tensile strength of cotton yarns sized with AS. It was found that the AS was desizable and showed satisfactory desizing efficiency in oxidant desizing. Based on the adhesion, reaction efficiencies, and desizability, the AS with a total DSt of 0.033 and a substituent ratio of DSp0.014/DSo0.032 showed potential for use in warp sizing.

Conductive and durable CNT-cotton ring spun yarns

A facile and original method was developed to fabricate flexible conductive yarns using cotton roving and carbon nanotubes (CNTs). The CNTs were assembled to cotton roving and then wrapped around by fibers through twisting during ring spinning. The obtained CNT treated cotton yarns (CNT-CYs) showed great electrical conductivity and durability properties. The CNT-CYs were analyzed using scanning electron microscopy and Raman scattering spectroscopy. The electrical conductivity, mechanical property and flexibility of CNT-CYs were investigated. The results show that electrical resistance of roving, twist and linear density of yarn affect the electrical conductivity of CNT-CYs. Combination with CNTs increased the breaking strength of cotton yarns. The electrical resistance of CNT-CYs was relatively stable during stretching and human motions. Moreover, no obvious changes in electrical resistance were found after CNT-CYs were bent 100 times. The CNT-CYs possessed good durability to repeated washing and abrasion.

Regularities in Variation of Support Functions of Physically Nonlinear Elastic-Visco-Plastic Law of Strain of Cotton Yarn

Support functions of the parameters variation of proposed physically nonlinear elastic-viscoplastic law of strain of cotton yarn are determined on the basis of the results of processing the experimental data on yarn tension to breakage. These functions allow us to determine the average values of nonlinear law parameters, which allow its use in practice in solving the applied problems of yarn mechanics and determining and evaluating the strength of cotton yarn.

Cassava starch‐graft‐polymethacrylamide copolymers as flocculants and textile sizing agents

ABSTRACTCassava starch‐graft‐polymethacrylamide (PMAM) copolymers were synthesized by a free‐radical‐initiated polymerization reaction, and the products were tested for their efficiency as flocculants and textile sizing agents. The highest percentages of grafting and monomer conversion were 79.9 and 78.0%, respectively. The grafted starches were characterized by Fourier transform infrared spectroscopy, X‐ray diffraction analysis, scanning electron microscopy, differential scanning calorimetry, and thermogravimetric analysis. The average molecular weight of PMAM chains in the grafted starches ranged from 15.9 to 30.8 × 105 g/mol. The grafted starches exhibited a higher peak viscosity and paste stability in comparison to the native starch (NS). Dynamic mechanical analysis showed that grafting provided fairly shear‐stable hydrogels, and the highest storage modulus obtained was 17,900 Pa compared to 1879 Pa for NS. The flocculation studies demonstrated the superiority of starch‐g‐PMAM over cassava starch and PMAM as an efficient flocculant. The tensile strength of cotton yarns sized with the starch‐grafted copolymer was significantly higher (104 MPa) compared to that sized with NS (34 MPa). © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 39810.

Multi Objective Optimization of Yarn Quality and Fibre Quality Using Evolutionary Algorithm

The quality and cost of resulting yarn play a significant role to determine its end application. The challenging task of any spinner lies in producing a good quality yarn with added cost benefit. The present work does a multi-objective optimization on two objectives, viz. maximization of cotton yarn strength and minimization of raw material quality. The first objective function has been formulated based on the artificial neural network input–output relation between cotton fibre properties and yarn strength. The second objective function is formulated with the well known regression equation of spinning consistency index. It is obvious that these two objectives are conflicting in nature i.e. not a single combination of cotton fibre parameters does exist which produce maximum yarn strength and minimum cotton fibre quality simultaneously. Therefore, it has several optimal solutions from which a trade-off is needed depending upon the requirement of user. In this work, the optimal solutions are obtained with an elitist multi-objective evolutionary algorithm based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). These optimum solutions may lead to the efficient exploitation of raw materials to produce better quality yarns at low costs.

Yarn Strength Modelling Using Genetic Fuzzy Expert System

This paper deals with the modelling of cotton yarn strength using genetic fuzzy expert system. Primarily a fuzzy expert system has been developed to model the cotton yarn strength from the constituent fibre parameters such as fibre strength, upper half mean length, fibre fineness and short fibre content. A binary coded genetic algorithm has been used to improve the prediction performance of the fuzzy expert system. The experimental validation confirms that the genetic fuzzy expert system has significantly better prediction accuracy and consistency than that of the fuzzy expert system.

Yarn Strength Modelling Using Fuzzy Expert System

Yarn strength modelling and prediction has remained as the cynosure of research for the textile engineers although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of prediction accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy models are trained using enormous amount of noise free input-output data, which are difficult to collect from the spinning industries. In contrast, fuzzy logic based models could be developed by using the experience of the spinner only and it gives good understanding about the roles played by various inputs on the outputs. This paper deals with the modelling of ring spun cotton yarn strength using a simple fuzzy expert system. The prediction accuracy of the model was found to be very encouraging.

Use of input selection techniques to improve the performance of an artificial neural network during the prediction of yarn quality properties

AbstractThe performance of an artificial neural network (ANN) is affected by the number and types of inputs. The aim of this article is to study the performance of ANN algorithms, used for the prediction of cotton yarn strength, elongation, and evenness, as the input units are subtracted (skeletonized) and added to the input layer. Nineteen factors, consisting of fiber properties, processing parameters, and yarn quality properties, were used as the main source of inputs. The initial sets of inputs, which were selected on the basis of their relationship with the output factors, were 13, 13, and 12 for yarn strength, elongation, and evenness, respectively. The final sets of inputs were 14 factors for the three yarn quality properties being predicted, and the new ANN algorithms showed performance improvement of 40, 37, and 47% for strength, elongation, and evenness, respectively, when compared to the algorithms with 19 factors. Yarn twist, fiber length, and fiber length uniformity were common among the five most influential factors affecting yarn strength, elongation, and evenness, accounting for 40, 37, and 37% for the prediction of yarn strength, elongation, and evenness, respectively. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008

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

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.

Effect of Seed Coat Fragments on Cotton Yarn Strength: Dependence on Fiber Quality

Contaminants in cotton fibers cause yam regularity defects that alter their structure. The effect of these alterations on yarn strength is discussed with particular focus on seed coat fragments (SCF), one of the primary cotton contaminants from the fiber to the finished product. This paper presents the results of an experimental study showing that, in certain cases, the presence of SCF has a significant effect on yarn strength. This effect is closely correlated with fiber quality, and a discussion of this interaction is presented on the basis of different statistical methods.

The Effect of Test Length on Strength of Cotton Yarns

In the paper, the tensile properties of cotton yarns are studied. A lot of data are gotten and analyzed. It indicates that the breaking strength of the yarns follows Weibull distribution. Then the effect of sample length on strength is analyzed using Weibull's statistics. The Weibull parameters are calculated through regression method. The breaking strength of the yarn at different sample length is predicted. Theoretical prediction conforms the experimental results very well.

Applied Statistics: A Handbook of BMDP Analyses

BMDP programs used (or suggested) for the examples.- Some points on the BMDP instructions.- Examples.- A Admissions to intensive care unit.- B Intervals between adjacent births.- C Statistical aspects of literary style.- D Temperature distribution in a chemical reactor.- E A 'before and after' study of blood pressure.- F Comparison of industrial processes in the presence of trend.- G Cost of construction of nuclear power plants.- H Effect of process and purity index on fault occurrence.- I Growth of bones from chick embryos.- J Factorial experiment on cycles to failure of worsted yarn.- K Factorial experiment on diets for chickens.- L Binary preference data for detergent use.- M Fertilizer experiment on growth of cauliflowers.- N Subjective preference data on soap pads.- O Atomic weight of iodine.- P Multifactor experiment on a nutritive medium.- Q Strength of cotton yarn.- R Biochemical experiment on the blood of mice.- S Voltage regulator performance.- T Intervals between the failure of air-conditioning equipment in aircraft.- U Survival times of leukemia patients.- V A retrospective study with binary data.- W Housing and associated factors.- X Educational plans of Wisconsin schoolboys.- Further Sets of Data.- Set 3 Survival times of rats.- Set 14 Grouping of perfumes.- References.

Factors Affecting the Cellulolytic Activity of Rumen Contents

The cellulolytic activity of rumen contents was assayed by measuring losses in weight and tensile strength of cotton yarn incubated in rumen contents in the presence of dietary additives (barley, tallow) and at different pH values. The addition of barley depressed cellulolysis and the titer of filter paper-degrading bacteria only if the pH was allowed to fall. Lowering the pH from 7.0 to 6.0 by addition of HCl almost completely inhibited attack of cotton and greatly reduced the titer of filter paper-degrading bacteria. The layering of tallow on cotton inhibited attack of cotton but did not decrease the titer of filter paper-degrading bacteria. The results are discussed with special reference to the importance of the study of cellulosic substrates, which require a known enzyme or mixture of enzymes for attack.

A Generalized Equation for Predicting the Lea Strength of Cotton Yarns

A Generalized Equation for Predicting the Lea Strength of Cotton Yarns

A Generalized Equation for Predicting the Lea Strength of Cotton Yarns—Reply

A Generalized Equation for Predicting the Lea Strength of Cotton Yarns—Reply

Influência da adubação foliar com NPK, na cultura algodoeira (G. hirsutum L. var. IAC-12), sobre a qualidade da fibra e da semente

Aplicações de fertilizantes por via foliar e ao solo foram comparados na cultura algodoeira, utilizando-se a variedade IAC-12, com a finalidade de se estudar uma influência sobre alguns caracteres físicos da fibra e da semente. Tanto na adubação ao solo, como na foliar, foram utilizados como fonte de nitrogênio, a uréia (46%N), como fonte de fósforo, o superfosfato concentrado (45% P2O2) e como fonte de potássio, o cloreto de potássio (60% K2O). Os resultados obtidos mostram que entre as características físicas da análise da fibra, encontrou-se um efeito positivo no comprimento da fibra, resistência da fibra, resistência do fio e maior peso de semente (índice de semente) quando o cloreto de potássio foi aplicado por via foliar. Quanto à uniformidade da fibra e ao índice de finura, não houve influência da adubação foliar com nenhum dos três elementos. Nestes casos, a adubação ao solo, proporcionou melhores resultados. Deve-se ressaltar que com a dose mais alta de fósforo (60 kg de P2O5/ha), o valor do índice de finura foi maior. O adubo nitrogenado na dose de 40 kg de N/ha, influenciou na resistência da fibra (Pressley).