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1.
This paper presents a support vector machine (SVM) regression approach to forecast the properties of cotton yarns produced
on ring and rotor spinning technologies from the fibre properties measured by HVI and AFIS. Prediction performance of SVM
models have been compared against those of the artificial neural network (ANN) models. A k-fold cross validation technique is applied to assess the expected generalization accuracies of both SVM and ANN models. The
investigation indicates that the yarn properties can be predicted with a very high degree of accuracy using SVM models and
the prediction performance of SVM models are better than that of ANN models. 相似文献
2.
Abhijit Majumdar Anindya Ghosh Shib Sankar Saha Ayan Roy Subir Barman Dhrubajyoti Panigrahi Anjan Biswas 《Fibers and Polymers》2008,9(2):240-245
Aesthetic properties of fabrics have been considered as the most important fabric attribute for years. However, recently there
has been a paradigm shift in the domain of textile material applications and consequently more emphasis is now being given
on the mechanical and functional properties of fabrics rather than its aesthetic appeal. Moreover, in certain woven fabrics
used for technical applications, strength is a decisive quality parameter. In this work, tensile strength of plain woven fabrics
has been predicted by using two empirical modelling methods namely artificial neural network (ANN) and linear regression.
Warp yarn strength, warp yarn elongation, ends per inch (EPI), picks per inch (PPI) and weft count (Ne) were used as input
parameters. Both the models were able to predict the fabric strength with reasonably good precision although ANN model demonstrated
higher prediction accuracy and generalization ability than the regression model. The warp yarn strength and EPI were found
to be the two most significant factors influencing fabric strength in warp direction. 相似文献
3.
In this paper, artificial neural network (ANN) model was used for predicting colour properties of 100 % cotton fabrics, including
colour yield (in terms of K/S value) and CIE L, a, and b values, under the influence of laser engraving process with various combination of laser processing parameters. Variables
examined in the ANN model included fibre composition, fabric density (warp and weft direction), mass of fabric, fabric thickness
and linear density of yarn (warp and weft direction). The ANN model was compared with a linear regression model where the
ANN model produced superior results in prediction of colour properties of laser engraved 100 % cotton fabrics. The relative
importance of the examined factors influencing colour properties was also investigated. The analysis revealed that laser processing
parameters played an important role in affecting the colour properties of the treated 100 % cotton fabrics. 相似文献
4.
This paper demonstrates the application of two soft computing approaches namely artificial neural network (ANN) and neural-fuzzy
system to forecast the unevenness of ring spun yarns. The cotton fiber properties measured by advanced fiber information system
(AFIS) and yarn count have been used as inputs. The prediction accuracy of the ANN and neural-fuzzy models was compared with
that of linear regression model. It was found that the prediction performance was very good for all the three models although
ANN and neural-fuzzy models seem to have some edge over the linear regression model. The linguistic rules developed by the
neural-fuzzy system unearth the role of input variables on the yarn unevenness. 相似文献
5.
In this study artificial neural network (ANN) models have been designed to predict the ring cotton yarn properties from the
fiber properties measured on HVI (high volume instrument) system and the performance of ANN models have been compared with
our previous statistical models based on regression analysis. Yarn count, twist and roving properties were selected as input
variables as they give significant influence on yarn properties. In experimental part, a total of 180 cotton ring spun yarns
were produced using 15 different blends. The four yarn counts and three twist multipliers were chosen within the range of
Ne 20–35 and α
e 3.8–4.6 respectively. After measuring yarn tenacity and breaking elongation, evaluations of data were performed by using
ANN. Afterwards, sensitivity analysis results and coefficient of multiple determination (R2) values of ANN and regression models were compared. Our results show that ANN is more powerful tool than the regression models. 相似文献
6.
Although gradient based Backpropagation (BP) training algorithms have been widely used in Artificial Neural Networks (ANN) models for the prediction of yarn quality properties, they still suffer from some drawbacks which include tendency to converge to local minima. One strategy of improving ANN models trained using gradient based BP algorithms is the use of hybrid training algorithms made of global based algorithms and local based BP algorithms. The aim of this paper was to improve the performance of Levenberg-Marquardt Backpropagation (LMBP) training algorithm, which is a local based BP algorithm by using a hybrid algorithm. The hybrid algorithms combined Differential Evolution (DE) and LMBP algorithms. The yarn quality prediction models trained using the hybrid algorithms performed better and exhibited better generalization when compared to the models trained using the LM algorithms. 相似文献
7.
In the present study, an attempt has been made to obtain the relationships for prediction of yarn diameter from different fibers and spinning technologies. The Peirce formula is found to give high deviation from the observed values of yarn diameter because it does not take the effect of twist, type of fibre and spinning technology into consideration. A new empirical model has been proposed that takes into account the parameters affecting the yarn diameter to a great extent, namely yarn twist, spinning technology and type of fibre in calculating the yarn diameter. The spinning technology, type of fibre and the proportion of fibre in the yarn have significant effect on yarn diameter. The proposed model is able to predict the yarn diameter more accurately. 相似文献
8.
Opening of the fibres in all industrial rotor spinning units is being done by an opening roller, which intakes the fibres
from one feed point. Increasing number of feed rollers from one to two may improve fibre opening on the opening roller by
gradual loading of the opening roller, which may improve fibre orientation in the final yarn and yarn properties. In this
research a modified SE-8 rotor spinning unit of Suessen was used in which two separate fibre feed systems were employed. Raw
material used was 38 mm, 1.7 den viscose fibre, to spin a 40 tex yarn. Yarn properties produced with this unit, were compared
with that of the original yarn. Yarn properties tested were tenacity, extension, work of rupture, mass irregularity and imperfections,
abrasion resistance and hairiness, which were measured on Shirley (SDL) and keisokki yarn testing machines. Test results were
analyzed by ANOVA for any difference between the means, and Tukey and Duncan for classification and ranking of the yarn properties.
Test results showed that, tenacity, extension and work of rupture of the modified yarn increased in comparison to the original
yarn. Its mass irregularity, number of thin places and neps, and hairiness decreased. Number of thick places and yarn abrasion
didn’t change. According to the test results, it was concluded that increasing the number of feed rollers on the opening roller
from one to two has improved yarn properties. 相似文献
9.
The mechanical and physical properties of spun yarns and fabrics depend not only on properties of constituent fibers, but also the yarn structure characterized by geometrical arrangement of fibers in the yarn body. Although there are many studies related to analyzing the migratory properties of spun yarns, there are no studies available about predicting yarn migration parameters. Therefore, the main aim of this research is to introduce a new approach to predict migratory properties of different kinds of spun yarns, namely siro, solo, compact and conventional ring-spun yarns. To achieve the objectives of the research, general physical and mechanical properties of spun yarns together with existing standards were thoroughly studied. Spun yarn migratory properties were predicted using intelligent technique of artificial neural network (ANN). Results signified that the ANN models can predict precisely the yarn migratory properties on the basis of a series of yarn physical and mechanical properties. 相似文献
10.
Anindya Ghosh 《Fibers and Polymers》2006,7(3):310-316
In this article, an attempt has been made to explain the failure mechanism of spun yarns. The mechanism includes the aspects
of generation and distribution of forces on a fibre under the tensile loading of a yarn, the free body diagram of forces,
the conditions for gripping and slipping of a fibre, and the initiation, propagation, and ultimate yarn rupture in its weakest
link. A simple mathematical model for the tenacity of spun yarns has been proposed. The model is based on the translation
of fibre bundle tenacity into the yarn tenacity. 相似文献
11.
In this study, an analysis on the breaking elongation mechanism of the polyester/viscose blended open-end rotor spun yarns
has been carried out. In addition, a back propagation multi layer perceptron (MLP) network and a mixture process crossed regression
model with two mixture components (polyester and viscose blend ratios) and two process variables (yarn count and rotor speed)
are developed to predict the breaking elongation 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. In conclusion, ANN and statistical model both have given satisfactory predictions;
however, the predictions of ANN gave relatively more reliable results than those of statistical models. Since the prediction
capacity of statistical models is also obtained as satisfactory, it can also be used for breaking elongation (%) prediction
of yarns because of its simplicity and non-complex structure. In addition, it is also found in this study that yarn count,
rotor speed and breaking elongation of polyester-viscose fibers and the blend ratios of these fibers in the yarn have major
effects on yarn breaking elongation. 相似文献
12.
Jane Harwood Paul McCormick Dennis Waldron Romano Bonadei 《Industrial Crops and Products》2008,27(1):22-28
Due to recent changes in EC subsidies for flax cultivation it has been difficult to grow short fibre flax profitably in the UK. The Texflax project aimed to demonstrate that high quality flax fibre can be produced and processed on short fibre cotton spinning systems. Initially 92 flax accessions were cultivated on test sites in the UK over three growing seasons to explore the range of fibre diameter found in fibre flax. The efficacy of applying a translocating herbicide at different stages of plant maturity for optimum fine fibre production was explored. A range of factors indicated that application at the midpoint of flowering stage is favourable for the desiccation of flax and onset of retting. Fibre was caustic extracted using a laboratory method developed at De Montfort University, and fibre evaluated in terms of diameter, length, consistency and cleanliness. At the end of the project five accessions from the original 92 were chosen as producing optimal quality fibre suitable for high value textile end uses. Improved agronomy and subsequent processing enabled yarns with a 50:50 cotton:flax blend to be spun at 26 N m yarn count, the normal blend ratio for this count being 70:30. The yarn properties show an improvement when compared to standard products and finer quality fabrics have been prepared using the yarns. 相似文献
13.
From early times, jute fibre has been generally conditioned for easy spinning by adding oil and water in the form of an emulsion. The commonly used oil consists of C12–C31 fractions of mineral oil that sometimes impart different intensities of oily (kerosene) or fishy smell to the end product. In the present work, efforts have been made to find a suitable sustainable substitute of mineral oil based conditioning agent for spinning of jute yarn and for this, three types of vegetable oil (rice bran oil, palmolein oil and castor oil), a silicone emulsion, a mixed enzyme system and glycerine have been used separately or in combinations as conditioning agents for jute fibre before its mechanical processing for making yarn in jute spinning machines. Considering comparable mechanical process performance for spinning of jute fibre (viz., fibre loss as droppings during processing, moisture retention prior to spinning stage and spinning end breakage rate), tensile properties of yarn, and lower yarn hairiness, it may be suggested to use 2.5% castor oil alone, or 2% castor oil in combination with 0.1–0.5% glycerine in the form of oil-in-water emulsion as the most suitable alternatives to conventional mineral oil-based jute conditioning agent to spin ordinary jute yarn. 相似文献
14.
R. Murugan B. S. Dasaradan P. Karnan M. Subramanian Senthilkannan 《Fibers and Polymers》2007,8(6):665-668
The present work relates to the occurrence of fibre rupture during fibre separation in rotor spinning and also discusses the
mechanism of such rupture. The reduction in fibre length during opening has been studied at different span lengths. A correlation
has been drawn showing the influence of combing roller action on yarn tenacity and elongation. Fibre rupture has direct relationship
with opening roller speed. Fibre rupture and surface damage occurring due to action of opening roller together are found to
mar the yarn quality index. 相似文献
15.
M. Bakhtiari S. Shaikhzadeh Najar S. M. Etrati Z. Khorram Toosi 《Fibers and Polymers》2006,7(3):295-304
High-bulk worsted yarns with different shrinkable and non-shrinkable acrylic fibers blend ratios are produced and then single
jersey weft knitted fabrics with three different structures and loop lengths are constructed. The physical properties of produced
yarns and compression properties of produced fabrics at eight pressure values (50, 100, 200, 500, 1000, 1500 and 2000 g/cm2) were measured using a conventional fabric thickness tester. Then, weft-knitted fabric compression behavior was analyzed
using a two parameters model. It is found that at 40% shrinkable fibre blending ratio the maximum yarn bulk, shrinkage, abrasion
resistance and minimum yarn strength are obtained. It is also shown that high-bulk acrylic yarn has the highest elongation
at 20% shrinkable fibre blend ratio. The statistical regression analysis revealed that the compression behavior of acrylic
weft-knitted fabrics is highly closed to two parameter model proposed for woven fabrics. It is also shown that for weft-knitted
structure, there is an incompressible layer (V′) which resists against high compression load. Acrylic weft-knitted fabrics with knit-tuck structure exhibit higher compression
rigidity and lower softness than the plain and knitmiss structures. In addition, at 20% shrinkable fibre blend ratio, the
high-bulk acrylic weft-knitted fabrics are highly compressible. 相似文献
16.
Predicting properties of single jersey fabrics using regression and artificial neural network models
In our previous works, we had predicted cotton ring yarn properties from the fiber properties successfully by regression and
ANN models. In this study both regression and artificial neural network has been applied for the prediction of the bursting
strength and air permeability of single jersey knitted fabrics. Fiber properties measured by HVI instrument and yarn properties
were selected as independent variables together with wales’ and courses’ number per square centimeter. Firstly conventional
ring yarns were produced from six different types of cotton in four different yarn counts (Ne 20, Ne 25, Ne 30, and Ne 35)
and three different twist multipliers (α
e
3.8, α
e
4.2, and α
e
4.6). All the yarns were knitted by laboratory circular knitting machine. Regression and ANN models were developed to predict
the fabric properties. It was found that all models can be used to predict the single jersey fabric properties successfully.
However, ANN models exhibit higher predictive power than the regression models. 相似文献
17.
Farooq Ahmed Arain Anwaruddin Tanwari Tanveer Hussain Zulfiqar Ali Malik 《Fibers and Polymers》2012,13(1):118-122
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. 相似文献
18.
19.
A. R. Moghassem 《Fibers and Polymers》2010,11(4):669-675
Classical statistical analysis has been generally used in obtaining optimum condition such as problems for rotor spinning
machine. In these methods the preferences of the producer about yarn characteristics to achieve the desired end product properties
have not been taken into consideration. However, machine parameters selection from possible alternatives with different performance
levels about yarn quality is difficult task and is inherently a multi-criteria decision making problem. In the present study,
valuable assistance in reaching acceptable solutions in order to select the appropriate doffing tube and its adjustment for
30 Ne rotor yarn spun to raise efficiency of weft knitting machine will be provided by technique for order preference by similarity
ideal solution (TOPSIS) approach. In experimental part 30 Ne rotor yarn samples were spun by considering one quantitative
variable, i.e., two different distances between the nozzle and rotor, and also two qualitative variables, i.e., nozzles in
4 different shapes and a draw-off tube with and without a torque stop. Then quality parameters of the yarns were analyzed
with TOPSIS. 相似文献
20.
Leveraging the antibacterial properties of polyester-cotton knitted fabrics has been attempted in this research by admixture of small proportion of polyester-silver nanocomposite fibres. Polyester-cotton (50:50) yarns were spun by mixing 10, 20 and 30 % (wt.%) polyester-silver nanocomposite fibres with normal polyester fibres so that overall proportion of polyester fibre becomes 50 %. The proportion of cotton fibre was constant (50 %) in all the yarns. Three parameters, namely blend proportion (wt.%) of nanocomposite fibres, yarn count and knitting machine gauge were varied, each at three levels, for producing 27 knitted fabrics. Polyester-cotton knitted fabrics prepared from polyester-silver nanocomposite fibres showed equally good antibacterial activity (65-99 %) against both S. aureus and E. coli bacteria. Antibacterial properties were enhanced with the increase in the proportion of polyester-silver nanocomposite fibres, yarn coarseness and increased compactness of knitted fabrics. Yarn count and blend proportion of nanocomposite fibre were found to have very dominant influence in determining the antibacterial properties of knitted fabrics. 相似文献