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Prediction of polyester/cotton blended rotor-spun yarns hairiness based on the machine parameters

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Title Statement Prediction of polyester/cotton blended rotor-spun yarns hairiness based on the machine parameters
 
Added Entry - Uncontrolled Name Ghorbani, Vahid
Vadood, Morteza
Johari, Majid Safar
 
Uncontrolled Index Term Artificial neural network; Partial derivatives method; Polyester/cotton blended yarn; Rotor spinning; Yarn hairiness
 
Summary, etc. <p class="Abstract" style="text-align: justify;">Effect of rotor type, rotor diameter, doffing-tube nozzle, and torque-stop on polyester/cotton rotor-spun yarn hairiness have been studied. To model the hairiness of polyester/cotton blended yarn, artificial neural networks and regression models have been used. The results show that there are significant differences in performance of network with different architectures and training algorithms. The network with two hidden layers has the best performance and can predict hairiness with high accuracy. Relative importance of input variables is studied with partial derivatives method based on the optimum network. The results indicate that rotor type and rotor diameter have the greatest and least effect on the blended yarn hairiness.</p>
 
Publication, Distribution, Etc. Indian Journal of Fibre & Textile Research (IJFTR)
2016-07-05 09:16:14
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/IJFTR/article/view/5762
 
Data Source Entry Indian Journal of Fibre & Textile Research (IJFTR); ##issue.vol## 41, ##issue.no## 1 (2016): Indian Journal of Fibre & Textile Research
 
Language Note en