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Experimental analysis was performed to characterize the crimped yarn properties.
One of the remarkable materials for improving paper yarn properties is carbon nanotubes (CNTs).
Modeling yarn properties from fiber parameters has been a theme of research for many years.
The comparison between woollen and semi-worsted spun yarn properties are also discussed.
Several researchers have reported the influence of DREF spinning variables on yarn properties.
Abrasion resistance and yarn hairiness are other important yarn properties which significantly affect the performance of yarn during weaving.
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Moreover, the relative effects of the yarn material properties are determined using different weave architectures.
The design of ANN models suitable for the prediction of yarn quality properties could take a variety of forms.
In this research work a one hidden layer MLP was designed and used for the prediction of yarn tensile properties.
Yarn quality properties can be predicted by modelling selected inputs and outputs of the cotton spinning system.
This research explores the effects of mesoscale (yarn) material properties on the macroscale mechanical response of various woven fabric architectures.
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