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A new approach to the unsupervised detection and classification of the spliced yarn joint

Issa K., Nagahashi H.

Autex Research Journal

2008

Vol. 8, no. 1

6--12

This paper presents an automatic vision-based system for unsupervised detection and classification of spliced yarn joints. In the splice detection process, a competitive learning method based on an LBG algorithm is used. In the splice classification process, a dynamic time warping (DTW) algorithm is used to classify the extracted splice joint into one of three categories, based on the degree of similarity between the spliced joint and the non- spliced remaining part of the same yarn. The use of DTW in the classification makes the proposed method adaptable to different types of yarns. Consequently, this method might be universally applicable for the classification of all spliced yarn joints. The proposed method has been evaluated using three types of experiments, yielding a promising result.

New technique for optimising yarn-end preparation on splicer, and a method for rating the quality of yarn-end

Issa K., Grütz R.

Autex Research Journal

2005

Vol. 5, no. 1

1--19

This paper presents a method for improving the yarn-end preparation on a splicer by forming a loop outside the opening tube which assures gentle delivery of the yarn to the opening tube, as well as control of the yarn-end length which will be prepared. Twelve groups of yarns were selected and tested using the new technique and a standard splicer. The new technique for yarn-end preparation showed better results in comparison to the standard splicer, especially for high twisted and plied yarn.