FORMULATION OF COTTON MIX: DEVELOPMENT FROM INDECISIVE TO DECISION SUPPORT SYSTEMS (original) (raw)
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Development of a Technique for the Selection of Cotton/Cotton Mix Components
n spinning mills, raw cotton is the prime factor that influences quality of yarn. Cotton mixing has a significant impact on end product cost and quality. As cotton Fibers contribute to about 60% of the yarn cost, we can deduce that mixing different cotton varieties plays an important role both for end product quality and its cost. In this paper, the effects of mixing different cotton varieties on fiber property distributions are explored both theoretically and experimentally. Cotton fibers from different varieties but from same categories and with compatible colors and surface integrity are mixed with different mixing ratios. The resultant mixes are tested and the results are analyzed in such a way to provide means for predicting the properties of cotton mixes efficiently. The acceptable range for mixing different cotton varieties is also investigated and specific index values are suggested to ensure a correct selection of mix components yielding acceptable properties of cotton mixes. *
Development of a Technique for the Selection of Cotton Cotton Mix Components20190712 91846 rtldsd
2013
n spinning mills, raw cotton is the prime factor that influences quality of yarn. Cotton mixing has a significant impact on end product cost and quality. As cotton Fibers contribute to about 60% of the yarn cost, we can deduce that mixing different cotton varieties plays an important role both for end product quality and its cost. In this paper, the effects of mixing different cotton varieties on fiber property distributions are explored both theoretically and experimentally. Cotton fibers from different varieties but from same categories and with compatible colors and surface integrity are mixed with different mixing ratios. The resultant mixes are tested and the results are analyzed in such a way to provide means for predicting the properties of cotton mixes efficiently. The acceptable range for mixing different cotton varieties is also investigated and specific index values are suggested to ensure a correct selection of mix components yielding acceptable properties of cotton mixes. *
Selecting Cotton Bales by Spinning Consistency Index and Micronaire Using Artificial Neural Networks
Autex Research Journal
This paper presents a method of selecting cotton bales to meet the specified ring yarn properties using artificial neural networks. Five yarn properties and yarn count were used as inputs, whereas the Spinning Consistency Index (SCI) and micronaire were the outputs to the neural network models. Bales were selected according to the predicted combinations of SCI and micronaire. The properties of yarns spun from selected bales show good association with the target yarn properties.
2020
This investigation employs a Multi-Criteria Decision Making (MCDM) procedure to derive equation used in ranking the cotton materials regarding yarn evenness (CV %) based on three quality criteria of fiber properties being tensile, fineness and length properties. The weight or relative importance of each criterion is decided by Analytic Hierarchy Process (AHP). The study utilized three Egyptian cotton cultivars being Giza 90, Giza 95 and Giza 86 in addition to three imported cotton cultivars namely; Acala, Kemian and Medling from Sudan, China and Greece, respectively following the Long Staple category. Three yarn counts of 24's, 30's and 36's (Ne) with twist multiplier (4) were applied to produce carded ring yarns. The efficiency of the proposed equation was determined by running Spearman rank correlation coefficients between the yarn evenness (CV %) estimated by the proposed formula and actual yarn evenness (CV %) measured at counts 24's, 30's and 36's Ne. Results revealed that the Egyptian local cultivars were superior to the imported cultivars considering all tested fiber properties. According to the equation derived by AHP method, the power values indicated that the fiber strength, upper half mean length, uniformity index and short fiber index plays an important role in determining yarn evenness (CV %) while the fiber fineness and fiber elongation had low influence. It is obvious that Spearman rank correlation coefficient between the actual yarn evenness (CV %) and that calculated by Analytic Hierarchy Process (AHP) were positive and highly significant indicating the validity of the proposed equation to be applied for ranking cotton materials regarding yarn evenness (CV %).
Journal of Applied and Emerging Sciences, 2019
Ring spinning frame is the most common and widely used technique for the production of yarn. This yarn subsequently utilized for the production of fabrics. Recently the research studies are more emphasis to create the living comfort condition. The yarn package density has a vital role to determine the feel, comfort, bulkiness and dyeing characteristics of the spun yarn. The yarn packaging density is largely influenced by the yarn process parameters. Ring spinning technique is the most common and widely used for the production of yarn. This yarn subsequently utilized for the production of fabrics. Recently the research studies are more emphasis to create the living comfort condition. The yarn package density is a vital role to determine the feel, comfort, bulkiness and dyeing characteristics of the spun yarn. It is largely influenced by the yarn manufacturing techniques and raw material used. The effects of spinning process parameters on packing density will be helpful to predetermine the yarn comfort behavior prior to the complete the fabric manufacturing process. Mechanical behavior of the staple yarn depends on the fiber characteristics and the yarn structure i.e. the arrangement of the individual fiber on the cross-section of the yarn. The fiber distribution on the cross section of the yarn and packing density of yarn has been investigated. The study provides how the spinning process parameters affect the internal structure of ring spun yarn. The aim of the study is to improve the package density of the yarn to achieve dynamometric and comfort properties of yarn to attract a wide variety of customers. Yarn Packing Density represents inter-fiber distance and friction, hence controlling the mechanical and comfort properties of the yarn. The main objective of this study is to control the packing density of yarn during the spinning process. In this work the 100 percent cotton used as raw material and spun on a ring spinning machine. The 135 ring yarns samples have been produced from three different types of yarn counts include (Ne 8, 16 and 24) with nine different samples of identical counts were produced. The three different variables within the count sample are TPI and spindle speed and count. Yarn packing density was calculated by using the driven equation by the application of multiple regression models. The final result shows that packing density has directly related to the TPI, Spindle speed and count..
Fibres and Textiles in Eastern Europe
Fibre properties are influential factors for yarn properties. Cotton, whose physical properties vary depending on the cultivation region, is still a very common fibre used in the textile industry. Properties such as fibre length, fineness, strength and maturity affect yarn tensility, evenness, imperfections and hairiness. Four different 100% cotton blends were used as raw material (American cotton, Aegean cotton, Urfa cotton, Greek cotton) to be converted into 20 tex compact yarns separately. HVI parameters of each blend type starting from the bale until the 2nd drawing passage machine revealed that yarn processing stages and machinery are influential factors for fibre the properties of fibres that are produced on a spinning line. Additionally ANOVA tests supported the idea that the evenness, tensility, yarn imperfections, and hairiness parameter of yarns produced from various cotton blends were statistically different. Principal Component Analyses (PCA) and the Correlation Matrix w...
Development of Yarn Integrated Index For Spun Yarns
8th Asian Textile conference, 2005
In the present context the spinning mills are producing the yarns and the different properties of the yarn are assessed at the R & D centre in the mills. Different properties so tested lead to different inference from the manufacturer and consumer viewpoints. The selection of the yarn is made by the consumer depending upon the need and mindset. This situation can vary from consumer to consumer even if the final place where the yarn is consumed for given end use remaining the same. Further the different qualities of fabrics are made, which means that there is absence of forward predication. This may be due to non-availability of a single value of the yarn by combining all different measured properties.
Fibers and Polymers, 2009
In this study, an artificial neural network (ANN) and a statistical model are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Seven different blend ratios of polyester/viscose slivers are produced and these slivers are manufactured with four different rotor speed and four different yarn counts in rotor spinning machine. A back propagation multi layer perceptron (MLP) network and a mixture process crossed regression model (simplex lattice design) with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed) are developed to predict the unevenness of polyester/viscose blended open-end rotor spun yarns. Both ANN and simplex lattice design have given satisfactory predictions, however, the predictions of statistical models gave more reliable results than ANN.
Global Journal of Research In Engineering, 2018
The demand of blended yarn has been increasing gradually due to some of its distinctive properties. It is a challenging task for textile technologists to ensure the appropriate blend composition and blending ratio for the developments of the spinning industry. We should reduce dependency from natural fiber as their properties are not adequate in advancing textile industry and so they are used together in blends with synthetic fibers to compensate their limitations. The aim of this research work was to study the comparative properties of cotton/viscose and cotton/modal blended yarn. Cotton was blended with viscose and modal fibers separately in 50/50 ratio. Blending was carried out at draw frame, and finally 31/1Ne blended yarns were produced. The yarn properties such as unevenness, imperfection, hairiness, single yarn strength (cN/tex) and bundle yarn strength (CSP) were tested, and their comparative results were analyzed. Cotton/modal 50/50 blended yarn showed significantly better ...
Qualitative and statistical analysis of cotton-flax blend yarn
Heliyon
Purely cotton fabric lacks different functional and physical properties than cotton-flax blends fabric. The experimental part of the present study is to investigate the influence of flax fibres content in cotton-flax blended yarn quality. Five ring yarn samples of the same count, 16/1Ne but from different blend ratios (100C, 80C:20F, 70C:30F, 45C:55F, and 100F) were produced from the same spinning preparatory conditions. The primary outcome was measured by analyzing test results obtained from different testing instruments (Uster Evenness tester, Mesdan Tensile Tester, and Instron Lea strength tester). The secondary outcome was measured by statistical analysis. Evenness properties were better for 80:20 blends in CVm% (17.13), IPI (1346), and H (6.89). 100% cotton yarn possessed the richest evenness quality CVm% (13.23), IPI (168.4), and H (7.97) in contrast to the poorest result of 100% flax yarn. On the other hand, the highest strength (CSP 4631, RKM 30.22) and the lowest elongation, 2.29%, were both achieved for 100% flax yarn. Evenness properties and elongation deteriorated with the gradual increase in flax content. In terms of strength, tenacity and CSP both decreased with an increase in flax content, but 100% flax yarn exhibited the highest performance.