scholarly journals Analysis of Ring Yarn Properties Produced from Different Traveller Brands

2019 ◽  
Vol 15 (18) ◽  
Author(s):  
Toufiqua Siddiqua ◽  
S. M. Farhana Iqbal
Keyword(s):  
2008 ◽  
Vol 99 (6) ◽  
pp. 533-538 ◽  
Author(s):  
S. M. Ishtiaque ◽  
A. Mukhopadhyay ◽  
A. Kumar
Keyword(s):  

Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.


2021 ◽  
pp. 1-13
Author(s):  
Duo Xu ◽  
Han Fan ◽  
Jian Li ◽  
Chong Gao ◽  
Wangwang Yang ◽  
...  

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Baneswar Sarker ◽  
Shankar Chakraborty

Purpose Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue. Design/methodology/approach This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables. Findings Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness. Originality/value The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.


2019 ◽  
Vol 19 (1) ◽  
pp. 86-96
Author(s):  
R Maheswaran ◽  
V Srinivasan

Abstract The influence of Modal–cotton (MC) fibre blend ratio and ring frame machine parameters such as front top roller loading and break draft on the blended yarn properties has been studied. Compact MC blended yarn samples of 14.75 tex with three different MC fibre blend ratio has been produced in a LR 6 ring spinning frame fitted with Suessen Compact drafting system. A robust design optimisation to minimise the variations of the output yarn properties such as blended yarn tenacity, yarn unevenness and hairiness caused because of the variations in the material as well as machine setting parameters is achieved through the Taguchi parametric design approach. It is found that the maximum compact MC blended yarn tenacity is 23.76 g/tex, which is influenced very much by MC fibre blend ratio but meagrely by top roller loading and break draft. Similarly, the minimum 9.54 U% and 3.59 hairiness index are achieved with 100:0 and 70:30 MC fibre blend ratio, respectively, at 23-kg top roller loading. Statistical ANOVA analysis is performed on the results and optimum values are obtained within the 95% confidential level through confirmation experiments.


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