Yarn Unevenness Research Articles

Study on the relationship between blending uniformity and yarn performance of blended yarn

The performance of blended yarns is affected by the distribution of component fibers within blended yarn and the blending uniformity. However, there is a lack of comprehensive and quantitative investigations on the relationship between them. In this paper, various specifications for two-component blended yarns were prepared, and the main factors affecting blending uniformity were analyzed. Then the yarn blending irregularity and yarn performance, including tenacity, tenacity coefficient of variation, extension and yarn unevenness were tested. Finally, Pearson correlation analysis and linear regression were performed between blending irregularity and each yarn performance to characterize the influence of blending uniformity on yarn performance quantitatively. The results show that the blending irregularity is effectively improved by uniform feeding of slivers, and increasing the passage of sliver blending. The blending irregularity has no significant influence on the relationship between twist factor and yarn performance. The blending irregularity has the most positive and highest effect on tenacity coefficient of variation, followed by tenacity and unevenness in third place, and the Pearson correlation coefficient ( P) were all above 0.5, and the linear regression coefficient was above 10−3, but the breaking extension was weakest and negatively correlated with blending irregularity. Except for breaking extension, the effect of blending irregularities on yarn performance becomes more obvious when there are large differences in fiber linear density and fiber length. This paper reveals the relationship between blending uniformity and yarn performance, to provide a basis for theoretical research on the properties of blended yarns.

3-D Measurement of Yarn Unevenness Based on A Low-Cost Multicamera Collaborative System and Signal Analysis

3-D Measurement of Yarn Unevenness Based on A Low-Cost Multicamera Collaborative System and Signal Analysis

VISUALIZING SPUN YARN DEFORMATION: INSIGHTS FROM OPTICAL INSTRUMENTATION

This article presents a comprehensive analysis of key quality indicators for spun yarns, focusing on yarns with a linear density of T=20 (Ne=30) tex produced on both simple and compact ring spinning machines. Through the utilization of optical instrumentation, various parameters including relative breaking strength (Rkm), strength, elongation at break (E %), and hairiness (H %) were meticulously examined to evaluate yarn quality. The study delves into the assessment of yarn unevenness (CV %) as a crucial quality metric, aiming to provide insights into the deformation characteristics of spun yarns. By employing advanced optical techniques, such as high-resolution imaging and precise measurements, the deformation behaviour of yarns under different spinning conditions is elucidated. The findings shed light on the influence of spinning machine type on yarn quality parameters, revealing nuanced differences in strength, elongation, and hairiness between simple and compact spinning processes. Additionally, the analysis highlights the correlation between yarn deformation and overall yarn quality, emphasizing the significance of understanding deformation mechanisms in optimizing textile manufacturing processes. Through a rigorous examination of these quality indicators, this research contributes valuable insights into the intricate dynamics of spun yarn deformation and its implications for textile production. The utilization of optical instrumentation offers a novel approach to visualize and quantify yarn deformation, providing a deeper understanding of the factors influencing yarn quality and performance in industrial settings.

IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE

The image processing philosophy is mainly determined by the complexity of the image and provides the necessary information to be derived from the image. In the textile industry, the image processing technique focuses on the determination of the geometric properties of the fibers such as cross-sectional shape, diameter, length, fineness, and curl while the studies on the yarn characteristics mostly focus on the determination of yarn hairiness, yarn unevenness and yarn defects (thick place, thin place and neps). In this study, previous studies about image processing approaches that are applied for fiber characteristics were investigated. A case study was conducted to automatically determine the visible foreign matter in the waste cotton bundle that can be used for recycled cotton yarn production. It was revealed that the image processing methods can be successfully applied for foreign fiber and matter detection in cotton bundle. As a result, it is emphasized that the waste cotton properties can be specified with a sensitive and accurate approach via image processing technique, objective and numerical determination can be obtained instead of visual evaluation based on experience.

Investigation of the use of hemp fibers in recycling spinning

In recycle yarn spinning, the original fibers are added to the blend as a carrier because of the recycled fibers obtained by opening from fabric scraps are very short and coarse. In the textile recycling industry, mostly petroleum-based synthetic fibers are used as carrier fibers. In an environmentally important activity such as recycling spinning, it is necessary to reduce the use of synthetics used as carrier fibers. When considered from this point of view, hemp fibers come to the fore with their important performance and environmental features. Based on this fact, in this study, in addition to the standard production conditions in the recycling yarn production facility, hemp fibers in different proportions were added to the blends to reduce the ratio of polyester as carrier fiber and the effects of this situation on yarn quality were examined. According to the results, it was observed that adding hemp fiber to the recycle yarn blend increased yarn tenacity, yarn unevenness and yarn hairiness while decreased yarn elongation properties.

Investigation of the dyeability and various performance properties of fabrics produced from flax and hemp fibres and their blends with cotton in comparison with cotton

AbstractIn this study, it is aimed to provide alternative fibres to cotton with enhanced comfort level, environmentally friendly and colour‐enriched in the cellulosic knitted fabric field. For this purpose, in addition to 100% cotton, 100% linen, 100% hemp yarns, knitted fabrics were produced from 70% cotton/30% linen and 70% cotton/30% hemp yarns. First of all, the properties of yarns such as tenacity, elongation at break, yarn unevenness, thin places, thick places and neps were examined comparatively. Then, pique fabrics were produced from these yarns and dyed with a reactive dye to a selected colour. Afterwards, physical (weight, wale/course density), mechanical (bursting strength, pilling, abrasion resistance) and comfort (air permeability and water vapour permeability) properties of all fabric samples, both in raw form and after dyeing and finishing processes, were compared. Furthermore, dyeing properties (colour, dye‐uptake, dyeing levelness, fastness) of fabric samples were also investigated. The dye uptake (%) values of the yarns decrease in the order of cotton > cotton/hemp > cotton/linen > hemp > linen. However, fastness values of dyed fabrics were nearly identical. Physical and mechanical properties of fabrics were very similar, while the air permeability of the fabrics decrease in the order of hemp > linen > cotton/hemp > cotton/linen > cotton. As a result of the study, it has been possible to produce knitted fabrics with superior performance characteristics (dyeability, comfort, etc.) from yarns produced via blending natural cellulosic fibres (flax and hemp) with certain proportions of cotton fibres, which contribute to sustainable production.

A sustainable approach to manufacture mélange yarn from waste jute fiber and pre-consumer cotton fabric waste using I-optimal mixture design

Today, the exploitation of recycled fibers emanating from textile waste has substantially increased owing to the growing environmental consciousness, legal sustainability mandates, and escalating raw material expenses. Herein, for the very first time, an innovative and sustainable approach was employed to utilize the dyed waste jute fibers accumulated beneath the jute weaving machines to create mélange yarns. Recycled cotton was used as well to reduce the amount of virgin cotton in mélange yarn. First-time, a precise prediction of mixing proportions in mélange yarn was yielded by using an I-optimal mixture design, a part of response surface methodology. The design recommended performing 16 experimental runs with 3 mixing components: dyed waste jute fiber (5–30%), recycled cotton (5–15%) and virgin cotton (55–80%), resulting in the production of 16 distinct yarn samples. Surface response models such as special quartic, special cubic, linear, and quadratic were developed for predicting the yarn unevenness, imperfection, hairiness, strength, and elongation. All the models were found to be statistically significant with p-values below 0.0500. Using desirability functions based on the response models, the optimized mixture components in the yarn were derived to be 15.8% dyed waste jute, 7.4% recycled cotton, and 76.8% virgin cotton fibers. This formulation was used to produce an 18 Ne mélange yarn whose quality parameters closely matched the predicted values, substantiating the effectiveness of the response model in navigating and predicting the attributes of mélange yarn.

A novel model for predicting tenacity and unevenness of ring-spun yarn: a special case in textile engineering

This study aimed to model the unevenness and tenacity of ring-spun yarn in a special case in textile engineering using response surface methodology. Yarn number and front roll speed were input variables, while yarn tenacity and unevenness were response/output variables. This study showed that the response surface methodology (RSM) could predict the yarn’s tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.99 and 0.98 and with the error sum of square (SS residual) values 0.00187 and 0.003215, respectively. We also found that an artificial neural network (ANN) could predict the yarn's tenacity and unevenness with the yarn coefficient of determination (R2) values of 0.51 and 0.63 and with the error sum of square (SS residual) values 1.48 and 0.856, respectively. It was concluded that the response surface methodology (RSM) and artificial neural network (ANN) could predict the yarn's tenacity and unevenness. Response surface methodology (RSM) predicts yarn characteristics better than ANN with MIMO (multiple inputs, multiple outputs) modeling. The novelty of this study is that we used RSM and ANN for the first time to obtain the tenacity and unevenness of ring-spun yarn accurately. A simpler approach was employed in this study for predicting tenacity and unevenness using RSM and ANN; however, future research holds the potential for incorporating advanced mathematical models to enhance the prediction. This research suggests that RSM and ANN can be applied to predicting the tenacity and unevenness of ring-spun yarn. The scientific application of this research is that the investigation will benefit practitioners in the textile industry to optimize yarn parameters by ring spinning machines.

Yarn Unevenness Prediction using Generalized Regression Neural Network

<p>This study aimed to propose a method to predict yarn unevenness grounded on the generalized regression neural network and traditional neural network model to further improve the prediction accuracy. The yarn unevenness model was constructed. Under this model, a three-layer neural network, a four-layer neural network, a five-layer neural network, and a generalized regression neural network were designed. Finally, Python was used for training and simulation. The training parameters and the three network models data were made consistent to ensure the comparability of the results. The results showed that using the yarn unevenness model, the average relative error of the four-layer neural network to cut down 0.87% compared with that of the three-layer neural network. Compared with the five-layer neural network, the four-layer neural network performance was not much different, but the running speed was increased by 46.05%. Compared with the four-layer neural network, the average relative error of the generalized regression neural network was reduced by 0.57%, the mean square error was reduced by 0.98%, he root mean square error was reduced by 4.76%, and the running speed was increased by 74.70%.</p> <p> </p>

EFFECT OF SLUB PARAMETERS ON YARN UNEVENNESS, HAIRINESS AND STRENGHT PROPERTIES OF SLUB YARNS

In this study yarn unevenness, diameter variation, hairiness and strength properties of slub yarns with various slub structures were investigated. The yarns (Ne20) in different slub thickness (1.2, 1.4, 1.6 and 1.8), slub length (1 – 3, 5 – 7 and 10 – 12 cm.), slub frequency (0.5, 3 and 6 pcs/m) and slub ramp length (1, 2 and 4 cm.) were produced from PET fibers. The multipopulation slub yarn which have two-slub length was also spun. The measurements of the yarns were made on the Fancy Yarn Profile of the Uster Tester 5 and the Uster Tensojet devices. The values of the test result were analyzed and evaluated statically. The slub parameters were found significant effect on the unevenness and strength properties of the yarns. As the slub thickness, slub length and frequency increased, the yarn unevenness, hairiness increased and the yarn strength decreased. Ramp length was found statistically significant for yarn breaking strength and elongation.

Influence of fiber length and comber noil extraction in long staple worsted spinning on eri silk yarn quality

The eri cocoons are open mouthed and as their silk filaments are discontinuous, these can only be used for spun yarn production. In this study, the effect of fiber length and comber noil extractions on yarn quality were evaluated. The significant impact of fiber length as well as first and second comber noil extraction was observed on yarn unevenness, imperfections, tenacity, breaking elongation and hairiness. With the aim of minimizing unevenness, imperfections and hairiness along with maximizing tenacity and breaking elongation of yarn; the fiber length and comber noil were optimized using Minitab 19 software. It was revealed that 150 mm fiber length can be set in the staple cutter during spinning preparatory for all yarn counts; 19% and 7% comber noil extractions are required for first and second combing, respectively, to produce 40 and 60 Nm yarns, whereas for 80 Nm yarn about 1% more noil extraction is needed in both combing processes. Principal component analysis depicted that both two comber noil contribute about 57% and fiber length of 43% on yarn quality. Regression equations from the response surface design of the experiment were derived for different yarn quality characteristics which exhibit a difference between actual and predicted values of below 10%.

THE EFFECT OF SLIVER DELIVERY SPEED ON THE UNEVENNES OF CARDED AND COMBED COTTON YARNS

The purpose of this study is to determine the effect of five different sliver delivery speeds on the yarn unevenness CVm (%). For this purpose, 100% cotton raw material was used, and card sliver samples were produced at five different card sliver delivery speeds (60 m/min, 120 m/min, 180 m/min, 240 m/min and 300 m/min). A total of 600 sample cops were produced in the ring spinning machine, each of which is 10 pieces of knitting and weaving yarn types in the carded and combed machine line, with the yarn counts Ne 20/1, Ne 30/1 and Ne 40/1 from these reed strips produced. The quality characteristics of the raw material used and the produced carded sliver samples were measured using the Uster HVI 1000 and Uster AFIS test equipment. The quality characteristics of the ring yarn cops samples produced on the ring spinning machine were measured with the help of Uster Tester 4 and Uster Tensojet Test devices. The results were analyzed by inferential and descriptive analysis methods. ANOVA analysis method was used for statistical analysis. According to the research findings, in both carded and combed Ne 40/1 yarn samples, an increase in yarn unevenness was observed with the increase in card sliver exit speed, and this increase created a statistically significant difference. The results revealed that the yarn unevenness is directly proportional to the card sliver exit speed and the increase of the card sliver exit speed causes the production of card sliver samples with higher unevenness values (Cvm (%).

Investigation of the Performance of Cotton/Polyester Blend in Different Yarn Structures

Abstract The demand for polyester fiber is increasing gradually day by day. Because of its good strength, low manufacturing cost, and ease of modification, polyester fiber has distinct characteristics, whereas cotton is well known for its comfort. Blending these fibers improves the performance of yarns. In this study, cotton/polyester was blended in different ratios to evaluate yarn performance. Three groups of yarn: rigid, core, and dual-core-spun, have been produced to examine the yarn’s performance. From the study, it has been found that increasing the polyester blend ratio increases the yarn strength and elongation but decreases the yarn unevenness and imperfections. Among the group, having more core components decreases yarn strength, unevenness, and imperfection but increases elongation. From the statistical analysis, except strength, all other properties have good interaction on yarn type and blending ratio. Pearson correlation also indicated that elongation and hairiness have a good correlation with yarn type where, except for hairiness, all other properties have shown a strong positive correlation on blending ratio.

Hybrid Neuro-Genetic Machine Learning Models for the Engineering of Ring-spun Cotton Yarns

ABSTRACT Yarn engineering is a long-standing problem for the cotton spinning industry as the functional relationship between fiber and yarn properties is quite complex. The objective of this research is to develop a hybrid machine learning-based prescriptive yarn engineering system that can foretell the properties of cotton fiber for achieving desired yarn properties. Artificial neural network (ANN) and genetic algorithm (GA) were used to develop the predictive model for cotton yarn properties and optimization of cotton fiber properties, respectively. Two separate ANN models were developed for predicting yarn tenacity and yarn unevenness. The functional relationships approximated by the ANN models were used to formulate the fitness function for GA. The validation of the ANN-GA system demonstrated good accuracy as cotton fiber strength, length and length uniformity were predicted with very good accuracy (mean error < 5%). The developed machine learning system can supplant the intuition-based decision making in textile spinning industry and pave the way for yarn engineering.

Combined Effect of Carding Machine Process Parameters on Yarn Properties; Process Optimization

Yarn tensile properties, unevenness, and irregularities are the most important properties of the yarn quality parameters that determine the fabric’s mechanical properties and appearance. In the current study, the combined effect of carding machine parameters (cylinder speed, flat speed, cylinder to flat setting, and taker-in speed) on yarn properties (yarn strength, elongation, unevenness, and total yarn imperfection) have been examined. In this research, 40 samples of open-end yarn were produced and each of them was tested for their strength, elongation, unevenness, and imperfection. Design-expert 7.0.0 software and Factorial designing have been employed to analyze the results. The results from statistical analysis have showed that increasing the gap between the cylinder and flat setting and increasing the taker-in speed increased yarn unevenness and increasing cylinder speed, flat speed, and taker-in speed increased total yarn imperfection significantly. Similarly, increasing the flat speed, cylinder speed, and taker-in speed as well as increasing the gap between the cylinder and flat reduced yarn strength and elongation significantly.

Numerical analysis and experimental investigation of a multi-principle drafting system in ring spinning

The multi-principle drafting device is investigated to improve the comprehensive properties of ring spun yarns. In this study, the front gathering area on the multi-principle drafting device was mathematically modeled to analyze the effects of inlet width, outlet width and path length of the grooved collector on friction forces. The numerical analysis results demonstrate that the outlet width and path length play important roles in frictional forces, while the variation of the inlet widths has little effect on the friction forces. The experiment results also reveal that for 18.4 tex yarns, different outlet widths of the grooved collector have considerable influence on yarn tenacity and hairiness, whereas they show an insignificant effect on yarn evenness and imperfections. Moreover, the effects of process parameters (different roller gauges and break draft ratios) on the novel drafting device with an optimized grooved collector to spin 18.4 tex yarns were investigated by response surface methodology. The results indicate that although the break draft ratio and front roller gauge create significant effects on yarn unevenness, the back roller gauge has no apparent influence on it. In addition, response surface methodology predicted the minimum yarn unevenness of 10.71% under optimal conditions of 1.2 break draft ratio, 32.5 mm back roller gauge and 17.5 mm front roller gauge, which is very close to the experimental result of 10.57%, reaching an advanced level compared to Uster Statistics 2018 of 5%.

Yarn unevenness prediction using generalized regression neural network under various optimization algorithms

Unevenness is one of the important parameters for evaluating yarn quality, but the current prediction accuracy of yarn unevenness is low. One of the important reasons is that there are few sample dataset for yarn unevenness prediction. For this problem, this paper applies generalized regression neural network to predict the unevenness of the yarn. Then, the generalized regression neural network is optimized by using particle swarm optimization, fruit fly optimization algorithm, and gray wolf optimizer, respectively. Finally, the optimized models were experimentally validated for their effectiveness. The experimental results show that the generalized regression neural network optimized by gray wolf optimizer has the best effect and the fastest optimization speed; the generalized regression neural network optimized by particle swarm optimization algorithm has the middle optimization speed; the generalized regression neural network optimized by fruit fly optimization algorithm has the worst effect and the slowest optimization speed.

Evaluation of sizing parameters on cotton using the modified sizing agent

For producing a woven fabric, sizing is one of the important steps to increase the efficiency of weaving productions. Various sizing agents such as starch, carboxymethyl cellulose (CMC), wax, emulsifier, and polyvinyl alcohol (PVA) are frequently used in the textile industry. The aim of this study is to evaluate the sizing parameters of cotton using the modified sizing agent and the frequently used sizing agent in the industry. For modification of sizing agent, various sizing chemicals are mixed together by our own recipes. Then modified sizing agents are applied to the cotton fabric along with frequently used sizing recipes. After sizing, all treated yarn and fabrics are subjected to evaluate the performance of sizing parameters and the modified sizing agent shows tenacity (20.35–21.58 cN/Tex), elongation (18.50–10.78%), initial modulus (475–486 cN/Tex), yarn hairiness reduction (95.55–95.91%), yarn unevenness (0.10–0.35 CV%), abrasion resistance (13,990 times), weight loss during abrasion (0.05–0.07%), breaking force (900–1000 F/cN), extension during maximum load (3.4–4.15 mm), and thread breakage during weaving (2.7–3.1 CMPX). The result indicated that the modified sizing agent has an acceptable range of sizing parameters and even in some cases the performances are higher than the frequently used sizing agent. The recipe of modified sizing agent contains EKACELL DT-250, EKAVIOL BEZ and EKAWAX chemicals. The most important thing that the modified sizing agent reduces the chemical consumption as well as makes cost-effectiveness of weaving production. Which is definitely be economically sustainable development in the textile industry.

Investigation of Open End Yarn Tension Using an Elastic Yarn-Guide

This paper presents the results of Open End (OE) yarn tension investigation using a yarn-guide funnel with an elastic element. Obtained were the dependencies of tension change on such factors as body mass, elastic element rigidity, yarn coordinates on the funnel surface, and the speed of yarn moving. The values of these factors cause fluctuations in yarn tension and, consequently, in yarn structure and characteristics. On the basis of experiments, it was shown that due to using of a yarn-guide funnel with an elastic element, a decrease in yarn unevenness is achieved. It is easily regulated by the funnel mass, the rigidity rate of the elastic element, and the spinning speed.

Comparative analysis of the properties of siro yarn spinned by natural and chemical fibers

In this article are study, Siro-spun yarns were produced from cotton and chemical fibers, and the properties of the yarns were studied. Siro spun yarns from a blend of cotton and polyester (50%/50%) fiber, cotton and bamboo (50%/50%) polyester and bamboo (50/50%) fiber, were produced separately and compared on physical and mechanical performance. Parameters such as yarn unevenness (U%, CVm%, CV10m), Siro yarn hairiness and tension were tested and analyzed.