Evaluating the surface hairiness of woven fabric belts with a yarn hairiness tester

Xia, Zhigang; Zhou, Mian; Wang, Hongshan; Wang, Kezuo; Wan, Youshun

doi:10.1080/00405000.2020.1865505

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Several methods can be used in the industry for yarn production which will affect various yarns parameters [1,2]. During the yarn production process, factors such as irregularities and dirt on the machine rolls can impair the quality of the yarn produced throughout the system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

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The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some textile yarn characteristic parameters. The irregularity of the textile thread influences its physical properties/characteristics and there may be a possibility of a break in the textile thread during the fabric manufacturing process. This can contribute to the occurrence of unwanted patterns in fabrics that deteriorate their quality. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. The main findings/results of the study are the yarn analysis method as well as the developed algorithm, which allows the analysis of defects in a more precise way. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation, and standard hairiness deviation, as well as performing spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

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Section: Introductionmentioning

confidence: 99%

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

et al. 2023

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Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes

Chauhan

2024

Communications in Computer and Information Science

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Yarn hairiness measurement based on multi-camera system and perspective maximization model

Cao,

Chen,

et al. 2024

J. Electron. Imag.

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Accurate measurement and identification of the number and length of yarn hairiness is crucial for spinning process optimization and product quality control. However, the existing methods have problems, such as low detection accuracy and efficiency, and incomplete detection. In order to overcome the above defects, an image acquisition device based on a multi-camera system is established to accurately obtain multiple perspectives of hairiness images. An automatic threshold segmentation method based on the local bimodal is proposed based on image difference, convolution kernel enhancement, and histogram equalization. Then, the clear and unbroken yarn hairiness segmentation images are obtained according to the hairiness edge extraction method. Finally, a perspective maximization model is proposed to realize the calculation of the hairiness H value and the number of hairiness in interval. Six kinds of cotton ring-spun yarn with different linear densities are tested using the proposed method, YG133B/M instrument, manual method, and single perspective method. The results show that the proposed multi-camera method can realize the index measurement of the yarn hairiness.

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Evaluating the surface hairiness of woven fabric belts with a yarn hairiness tester

Cited by 3 publications

References 41 publications

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes

Yarn hairiness measurement based on multi-camera system and perspective maximization model

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