Processing parameters optimization of multiple quality characteristics of open-end rotor spinning process for Bamboo charcoal and CVC blended fibers

In the field of yarn spinning engineering, the importance of the processing parameters taken depends directly on the quality characteristics of the yarn. This study aimed to find the optimal processing parameters for an open-end rotor spinning frame at work to identify its multiple quality characteristics for yarn. In this study, Bamboo charcoal and cotton 70 %/polyester 30 % (CVC) blended fibers were adopted as the materials, and the open-end rotor spinning frame was used to spin the yarn. In order to identify optimal conditions of an open-end rotor spinning frame, the Taguchi experimental method was applied to design open-end rotor spinning experiments, and the L₉ orthogonal array was chosen in accordance with nine sets of experiments and contained four control factors and three levels. Furthermore, a response surface methodology (RSM) was used to obtain the models of significant processing parameters for the strength, unevenness, I.P.I, and hairiness. Based on experiments designed to obtain an open-end rotor spun yarn Ne 30, the strength, unevenness, imperfection indicator/km (I.P.I) and hairiness were then chosen as the quality characteristics. In addition, grey relational analysis integrated the optimal processing parameter of multiple quality characteristics, and a confirmation experiment was performed. In conclusion, the optimal processing parameters under steady spinning conditions were a rotor speed of 88000 rpm, a feed speed of 0.392 m/min, and a winding speed of 39.466 m/min.     

黄麻与棉转杯混纺纱的纺制

本文对黄麻／棉混纺纱的生产工艺作了初步的探索，利用经过脱胶后的精细化黄麻纤维，通过对纺纱加工工艺与设备的选择、调整，纺制成了黄麻／棉55／45的转杯混纺纱.结果表明，黄麻纤维在脱胶精细化处理后，是可以加工成应用领域更广泛的产品的.     

Prediction of rotor spun yarn strength from cotton fiber properties using adaptive neuro-fuzzy inference system method

In this study, we present the application of a hybrid neuro-fuzzy system for the prediction of cotton rotor spun yarn strength from cotton fiber properties. The proposed system possesses the advantages of both artificial neural networks and fuzzy logic, and is thus more intelligent. HVI (high volume instrument) and Uster AFIS (advanced fiber information system) fiber test results are used to train the neuro-fuzzy inference system. We also study the degree of impact of each fiber property on the rotor spun yarn strength. Fiber strength, upper half mean length, length uniformity and yarn count have a positive impact whereas micronaire, yellowness and short fiber content have a negative impact on rotor spun yarn strength.     

Comparison of the new methodologies for predicting the CSP strength of rotor yarn

This paper provides preliminary results on the relative performance of the adaptive neuro-fuzzy system inference (ANFIS) model versus linear multiple regression method, when applied to the use of cotton fiber properties to predict spun yarn strength obtained from open-end rotor spinning. Fiber properties and yarn count are used as inputs to train the two models and the output (dependent variable) would be the count-strength-product (CSP) of the yarn. The predictive performances of the two models are estimated and compared. We found that the ANFIS has a better average prediction successful in comparison with linear multiple regression model.     

Spinning performance and antibacterial activity of SeaCell® active/cotton blended rotor yarns

Microorganisms can lead to functional, hygienic and aesthetic (e.g. deterioration, staining) problems on textile products. Natural fibers especially cotton are more easily affected by microorganisms. Blending of cotton fibers with antimicrobial fibers can enhance the protective properties of products against microorganisms. Demand of antimicrobial performance from the products changes depending on the application area. Therefore determination of suitable antimicrobial fiber quantity for the desired application is important. In this study the spinning performance of SeaCell Active/cotton blended open end rotor yarns and antibacterial activities of fabrics produced by these blended yarns were investigated. Five different cotton/SeaCell Active blended slivers with SeaCell Active content from 3 % up to 53 % were prepared on drawframe machine and all slivers were spun into yarns on open end rotor spinning machine at a yarn count of 20 tex with α_Tt=3827 twist coefficient. The effects of rotor speed, opening roller speed, rotor, opening roller and navel type on the quality parameters of SeaCell Active/cotton blended yarns were investigated. Tensile properties, hairiness, unevenness and IPI values of the yarns were reported. All types of cotton/SeaCell Active blended yarns were knitted on a circular knitting machine. Antibacterial activity of the fabrics was analyzed quantitatively. Antibacterial tests showed that good antibacterial activity can be achieved after several washings even with 3 % of SeaCell Active fibers in fabrics.     

亚麻／大豆蛋白纤维转杯纺纱产品的开发与研制

介绍了亚麻和大豆蛋白纤维的性能，结合生产实践，通过工艺试验，探讨了亚麻／大豆蛋白纤维转杯纱的一些工艺参数，为提高成纱质量提供参考依据。     

Prediction system of open-end rotor spinning process based on LM-neural network for bamboo charcoal fibers

In line with the environmental protection trends of the 21st century, bamboo charcoal fiber is invented to meet the requirements of the fields of science and technology. Its special functionalities, namely, antistatic, moisture adsorptive, perspiring, antibacterial, deodorizing, anti-radiation, and far infrared properties make it extremely suitable for applications in medicine, sports, and recreational fields, as well as an important breakthrough for environmentally-friendly textile materials. To achieve rapid manufacturing, this study processes bamboo charcoal fibers by open-end (OE) rotor spinning. The Taguchi orthogonal array is applied to the design of this experiment, and the significant factors of fibers quality are obtained through ANOVA in order to facilitate the follow-up processes of quality control. The process prediction system is built based on the test data, and is combined with the back-propagation neural network and the Levenberg-Marquardt (LM) algorithm in order to establish an OE rotor spinning process prediction system. The rotor speed, feed speed, and winding speed are set as the network input parameters, while the yarn strength, the hairiness, the unevenness, and the imperfections indicator (I.P.I.) are the output parameters. Through network learning and training, this system reports a prediction error below 5 %, proving that this prediction system has excellent predictability.     

Effects of spinning processes on HVI fiber characteristics and spun yarn properties

The effects of opening, carding, and repeated drawings on single fiber and bundle cotton characteristics were studied by employing Mantis®, AFIS® and HVI Testers. Some of the significant changes in single fiber properties were found to be due to process parameters as well as the changes in the fiber crimps, parallelness of fibers within HVI beards, and the actual changes in the tensile properties of the fibers. The study showed that the HVI test data taken just prior to spinning had the highest correlation with the yarn tensile properties. Based on the study results, we point out the potential of HVI for future quality and process control in spinning by recommending a set of expanded HVI output that is more scientific and comprehensive for the future control needs.     

Prediction of rotor spun yarn strength using support vector machines method

A new method for rotor spun yarn prediction from fiber properties based on the theory of support vector machines (SVM) was introduced. The SVM represents a new approach to supervised pattern classification and has been successfully applied to a wide range of pattern recognition problems. In this study, high volume instrument (HVI) and advanced fiber information system (Uster AFIS) fiber test results consisting of different fiber properties are used to predict the rotor spun yarn strength. The results obtained through this study indicated that the SVM method would become a powerful tool for predicting rotor spun yarn strength. The relative importance of each fiber property on the rotor spun yarn strength is also expected. The study shows also that the combination of SVM parameters and optimal search method chosen in the model development played an important role in better performance of the model. The predictive performances are estimated and compared to those provided by ANFIS model.     

苎麻纤维／棉变弹竹节纱的生产实践及工艺研究

结合对FA502型细纱机进行改造，采用瞬时改变中后罗拉速度的装置，通过生产实践和工艺试验，分析了纤维性能、纺纱工艺等因素对苎麻纤维／棉变弹竹节纱质量的影响，探讨了生产变弹竹节纱的工艺措施。     

Comparative study on the effect of blend ratio on tensile properties of ring and rotor cotton-polyester blended yarns using concept of the hybrid effect

Study on the characteristics of blended ring and rotor spun yarns is a topic of major interest to the researchers. The overall properties of these blended yarns are affected by the relative proportion, properties of the components and their interactions. The main focus of this work is on comparing and analyzing effects of blend ratio on tensile properties of the yarns produced in different spinning systems using concept of hybrid effects that has not received enough attention from researchers. Various blends of cotton-polyester ring and rotor spun yarns were prepared. Tensile properties of the samples were examined as well. Interactions between cotton and polyester fibers was evaluated through predicting strength and elongation at break of the yarns using simple rule of mixtures (ROM) and hybrid model. Experimental results showed that, the effect of different blend ratios on tensile properties of the samples is different. In comparison with 100 % cotton yarn, promotion in braking strength of the ring and rotor spun samples occurred after increasing fraction of the polyester fiber to 50 and 66.5 % respectively. The prominent finding of the present work is that the trend of change in tensile properties of different yarns versus blend ratio is predictable via hybrid model and migration behavior of the constituent fibers. Coefficients representing the intensity of the interaction and migration index of the fibers were calculated and all results were discussed based on these calculated factors.     

Prediction of strength and weavability of blended spun yarns

In this paper, we report on predicting the strength of polyester/viscose spun yarns made on ring, rotor and air-jet spinning systems. A system has been developed to measure the weavability of yarns. Hamburger’s fibre bundle theory is modified to predict the strength of blended yarns from the strengths of single-fibre component yarns. The modified model predicts blended yarn strength more accurately than the original Hamburger’s model emphasizing the importance of yarn structure on blended yarn strength. The weavability of blended yarns is measured on a CTT instrument incorporating a shedding device which addresses the stresses viz., cycle extension, flex abrasion and beat up occur during weaving. The measured weavability compared well with that obtained on a commercial Sulzer Ruti Reutlingen Webtester. Yarn structure and strength and cohesion of fibres affect the strength and weavability of yarns.     

A joint influence of the distributions of fiber length and fineness on the strength efficiency of the fibers in yarn

The length and fineness of fibers are critical to the strength of yarns. Much research has been conducted on the issue in the past decades. Zeidman and Sawhney introduced a new parameter called strength efficiency (SE) of fibers in a yarn using an elaborate probabilistic method. Their final formula, a non-dimensional measure, describes the influence of the fiber length distribution on the strength of yarn. The result, however, is based on the assumption that the fibers are identical in all respects including their cross-sectional area. The influence of fiber fineness can not be seen in their formula. In fact the joint influence of fiber length and fineness is rarely studied. We derive a new strength efficiency of the joint influence of fiber length and fineness on the basis of Zeidman’s result. The conclusion is helpful to the understanding of the comprehensive influence of fiber length and fineness on the strength of yarn. Furthermore, we give a plausible method to estimate the critical length defined by Zeidman. The result can be applied to the research of the properties between fibers and yarns.     

Multiple response optimization of rotor yarn for strength,unevenness, hairiness and imperfections

In this study, a multiple response optimization model based on response surface methodology was developed to determine the best rotor speed and yarn twist level for optimum rotor yarn strength and unevenness, and minimum yarn hairiness and imperfections. Cotton yarn of 30 tex, was produced on rotor spinning machine with different twist levels (i.e. 500, 550, 600 and 700 tpm) at different rotor speeds (i.e. 70000, 80000, 90000 and 100000 rpm). Yarn quality characteristics were determined for all the experiments. Based on the results, multiple response optimization model was developed using response surface regression on MINITAB® 16 statistical tool. Optimization results indicate that with the quality of raw material selected for this study, top 50 % quality level, according to USTER® yarn quality benchmarks, can be achieved with 100 % desirability satisfaction for all the selected yarn quality parameters at rotor speed of 77,800 rpm and yarn twist of 700 twists per meter.     

Effects of bleaching and dyeing on the quality of alpaca tops and yarns

This paper reports the effects of bleaching of alpaca tops and dyeing of bleached alpaca tops/yarns on the quality of tops and yarns. A dark brown alpaca top was bleached with hydrogen peroxide. Two bleaching methods were tried for effectiveness of color removal. A portion of each bleached top was dyed after bleaching. Color parameters were examined for unbleached, bleached and bleached/dyed tops, these tops were then converted into yarns of different twist levels and counts using a worsted spinning system. Some of the bleached yarn from each bleaching method was dyed in a package dye vat to compare the difference of top dyeing versus yarn package dyeing on yarn quality. Fiber diameter, yarn strength, yarn evenness, yarn hairiness and fiber degradation were tested to examine the effects of bleaching and dyeing on these properties at top and yarn stages. A processing route for bleaching and dyeing alpaca fiber was recommended.     

Structural analysis of finer cotton yarns produced by conventional and modified ring spinning system

Yarn structure plays an important role in determining the properties of spun yarns. Recently, a modified spinning technique has been developed for producing a low torque and soft handle singles yarn by modifying the fiber arrangement in a yarn. Comparative studies revealed that the finer modified yarns possess significantly higher strength and lower hairiness over the conventional yarns of the same twist level, implying a different structure of finer modified yarn. Thus this paper aims to quantitatively study the structures of the finer conventional and modified cotton yarn (80 Ne) produced at the same twist level. Various measuring techniques, namely the Scanning Electron Microscope (SEM), cross section technique and tracer fiber technique, are adopted to analyze their structural characteristics, including fiber configuration, fiber spatial orientation angle, fiber packing density, yarn surface appearance, and fiber migration behavior. Results showed that finer modified yarns exhibit a smoother surface and much more compact structure with less hairiness. The fibers in the finer modified yarn have a complicated fiber path with relatively lower fiber radial position, larger migration frequency and magnitudes. In addition, it was noted that 73% of fibers in the finer conventional yarn follow concentric conical helix, which is contrary to those in the coarser conventional yarn. The analyses conducted in this paper provide deep insights into the mechanism of modified spinning technique and evidential explanations on the difference of properties between the finer conventional and modified yarns.     

Study on characteristics of compact yarns under dynamic state

The dynamic testing conditions simulate actual manufacturing conditions more closely than static testing. In most cases, as results from dynamic tests differ significantly from static tests, dynamic tests are more relevant from the point of view of processing of yarn. The yarns are in motion when they are running on different machines during the production process viz. weaving; knitting etc. Compact ring spun yarns have created a fundamental change how the industry views the ring spinning. The new technology compact yarns such as EliTe® yarns need to be compared with the normal doubled yarns in a dynamic way. This study involves dynamic testing of the EliTe® compact yarns and normal ring spun doubled yarns using CTT (Constant Tension Transport) machine, a versatile test instrument to measure the yarn properties such as dynamic breaking strength, elongation, abrasion, lint, yarn faults (thick, thin places, neps), hairiness etc. EliTe® compact yarns showed higher breaking strength, more elongation, with lesser yarn faults and hairiness, less abrasiveness and less lint generating tendencies during the dynamic testing as compared to the normal ring spun doubled yarns.     

Effect of chemical softening of coconut fibres on structure and properties of its blended yarn with jute

Coconut fibres were subjected to chemical treatment to obtain softer and finer fibres, suitable to blend with other finer fibre like jute. The chemical softening recipe was optimized using Box-Behnken design of experiments as 40 % Na₂S, 10 % NaOH and 6 % Na₂CO₃, which notably reduced the fineness (33 %) and flexural rigidity (74 %) and improved tensile property of coconut fibre. Effect of softening of coconut fibre on its process performance was studied in high speed mechanized spinning system at different blend ratios with jute. Blending with jute assists in spinning of coconut fibre to produce yarn of 520 tex at production rate of 5-6 kg/h, as compared to 15 kg/day for hand spun 5300 tex raw coconut fibre yarn in manual system. Analysis of blended yarn structure in terms of packing density, radial distribution of fiber components (SEM) and mass irregularity were investigated. SEM shows yarns made from softened coconut fibre -jute blends are more compact than raw coconut fibre -jute blend yarns. Coconut fibres were preferentially migrated to core of the yarn. Major yarn properties viz., tensile strength, and flexural rigidity of raw and chemically softened blended yarns were compared against their finest possible 100 % coconut fibre yarn properties. Yarn made up to 50:50 chemically softened coconut fibre-jute blend showed much better spinning performance, and having superior property in terms of reduced diameter, higher compactness, strength, initial modulus and less flexural rigidity than 100 % raw, 100 % chemically softened coconut fibre rope, and raw coconut fibre-jute blend yarns.     

科棉3号纤维品质特性及成纱质量的研究

本文系统分析了科棉3号品质的特点．探索了其纺纱性能。试验结果表明．科棉3号纤维长度30mm以上、麦克隆值A级、比强度25gf/tex以上．达到高品质棉的标准。从纺织性能看，科棉3号与苏棉9号相比，无论是纺C14.5还是JC14.5纱，条干水平均有明显的改善，条干变异系数下降0．8％～1．7％，细节降低4—40个／km，断力强度增加3．4—4．0gf/tex，粗节和强力变异系数均有不同程度的下降，唯一的缺点就是棉结相对较多，对成纱外观质量有一定负面影响。     

Cotton yarn engineering via fuzzy least squares regression

Modeling of yarn and fiber properties has been a popular topic in the field of textile engineering in recent decades. The common method for fitting models has been to use classical regression analysis, based on the assumptions of data crispness and deterministic relations among variables. However, in modeling practical systems such as cotton spinning, the above assumptions may not hold true. Prediction is influential and we should therefore attempt to analyze the behavior and structure of such systems more realistically. In the present research, we investigate a procedure to provide a soft regression method for modeling the relationships between fiber properties, roving properties, and yarn count as independent variables and yarn properties as dependent (response) variable. We first selected the effective variables by multivariate test (mtest) and then considered fuzzy least squares regression for evaluating relationship between cotton yarn properties such as tensile, hairiness, unevenness and fiber properties that were measured by HVI system. We also used mean of capability index (MCI) to evaluate the goodness of fit of the fuzzy regression models. The results showed that the equations were significant at very good MCI levels.