Predicting the Hairiness of Cotton Yarn in Winding Process

摘要：

The objective of this work is to investigate the predictability of the hairiness of the cotton yarn from a cone winding machine using a multilayered perception (MLP) feed -forward back-propagation network in an artificial neural network system. A five-quality index (feeder distance, winding speed, thread cleaner gauge, tension washer weight, and rupture ring highness) and cotton yarn hairiness of winding are rated in controlled conditions. A good correlation between predicted and actual cotton yarn hairiness of winding shows that winding yarn hairiness can be predicted by neural networks. It shows the neural network provides a powerful tool for yarn prediction.

关键词： winding hairiness yarn process parameter artificial neural network

作者: Zhao Bo

作者单位: College of Textiles Zhong yuan University of Technology Henan, Zhengzhou, 450007, China

会议类型: 国际会议

会议名称: 2010 International Conference on Educational and Network Technology(2010教育与网络技术国际会议 ICENT 2010)

会议地点: 秦皇岛

会议语种:英文

页码: 393-396

在线出版日期: 2010-06-25（万方平台首次上网日期，不代表论文的发表时间）