2011 International Conference of Information Technology, Computer Engineering and Management Sciences 2011
DOI: 10.1109/icm.2011.387
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Applying artificial neural network technique and theory to study the hairiness of polyester/cotton blended yarn in warping process

Abstract: The polyester/cotton blended yarn hairiness in warping process is affected by fiber performance and processing parameters, which makes its prediction difficult. Among these processes, warping process parameters play an important role in warping yarn hairiness through warping process. To examine the effect of various warping process parameters on yarn hairiness, in this work, we used the ANN method to predict the hairiness of polyester/cotton yarn in warping process with warping process parameters. The results … Show more

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“…Zhao in his multiple papers reported models of cotton yarn hairiness developed through multi-layer perceptrons. [59][60][61] The hairiness of spun yarns was predicted from ring processing parameters, i.e. aperture of guide wire, nip gauge, spindle speed, and back draw time.…”
Section: Modeling and Predicting Yarn Hairinessmentioning
confidence: 99%
“…Zhao in his multiple papers reported models of cotton yarn hairiness developed through multi-layer perceptrons. [59][60][61] The hairiness of spun yarns was predicted from ring processing parameters, i.e. aperture of guide wire, nip gauge, spindle speed, and back draw time.…”
Section: Modeling and Predicting Yarn Hairinessmentioning
confidence: 99%