Woven fabric defect control methods in shuttle loom

Author:

Amanuel Lami1ORCID

Affiliation:

1. Textile Engineering Department, Wollo University, Kombolcha Institute of Technology, Kombolcha, Ethiopia

Abstract

The productivity and product quality of a shuttle loom are comparatively less than that of a shuttleless loom because of its high power consumption, more losses of energy, and fault susceptible picking mechanism. The economical commercialization of shuttle loom weaving requires systematic aspects of quality control which enable the mill to adhere to the methods of defect control methods. The current study focuses on the effectiveness of loom patrolling in minimizing fabric defects in the quality inspection department. The t-critical Value distribution of the recorded loom patrol defects and defects recorded in the quality inspection section were calculated to get the rejection region. The study demonstrated how much loom patrol minimizes the number of defects in the inspection department and emphasized loom patrolling as a decisive defect control method for shuttle looms. The t-critical value was calculated from the recorded data of the snap study done through direct observation, interview, and check sheet and these data were also analyzed using the Pareto technique, and focus group discussion. It was found that reed mark, temple mark, over pick and double pick were frequent in the shuttle looms. The causes of the defects were material, process, and human-related problems ranging from spinning section up to finishing section. Scientific Remedies were applied to avoid the successive coming of the faults and minimized the frequency of the defects significantly.

Publisher

SAGE Publications

Subject

General Materials Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Patterned Fabric Defect Detection Based on Separate Convolutional Unet;2024 International Conference on Data Science and Its Applications (ICoDSA);2024-07-10

2. Classification of Woven Fabric Faulty Images Using Convolution Neural Network;Mathematical Problems in Engineering;2022-08-27

3. Zero-shot learning and classification of steel surface defects;Fourteenth International Conference on Machine Vision (ICMV 2021);2022-03-05

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