Abstract
The polyester/cotton blended yarn hairiness of winding is related to the winding processing parameters (winding tension, winding speed, balloon position controller, ring yarn hairiness, and ring yarn twist). However, it is difficult to establish physical models on the relationship between the processing parameters and the winding yarn hairiness. Due to the ANN model has excellent abilities of nonlinear mapping and self-adaptation. Therefore, it can be well used to predict yarn properties quantitatively. In this research, two modeling methods are used to predict the hairiness of polyester/cotton winding yarn. The results show that ANN model is more effective than linear regression model, which is an excellent approach for predictors.
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Bo, Z. (2011). A Prediction Model Based on Linear Regression and Artificial Neural Network Analysis of the Hairiness of Polyester Cotton Winding Yarn. In: Jin, D., Lin, S. (eds) Advances in Multimedia, Software Engineering and Computing Vol.1. Advances in Intelligent and Soft Computing, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25989-0_18
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DOI: https://doi.org/10.1007/978-3-642-25989-0_18
Publisher Name: Springer, Berlin, Heidelberg
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