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Visual Analysis of Yarn Tension Research

Published: 31 December 2024 Publication History

Abstract

To better understand the research hotspots and development trends of yarn tension in the textile industry, this study analyzed relevant literature on yarn tension over the past 20 years by retrieving data from the Web of Science core dataset and employing scientometric analysis methods. Network maps were created using scientific literature analysis tools such as VOSviewer and Citespace. Through visual analysis of literature statistics, keyword co-occurrence and clustering, and keyword bursts, this study revealed the current research hotspots and development trends in yarn tension, providing a reference for future research work.

References

[1]
Celik O, Eren R. Experimental investigation of effect of balloon length on yarn tension during unwinding[J]. International Journal of Clothing Science and Technology, 2019, 31(4): 495-509.
[2]
Zhang H, Xia H, Lu Y, et al. Tension control of a yarn winding system based on the nonlinear active disturbance-rejection control algorithm[J]. Textile Research Journal, 2022, 92(23-24): 5049-5065.
[3]
Ji Y, Ma J, Zhou Z, et al. Dynamic Yarn-Tension Detection Using Machine Vision Combined with a Tension Observer[J]. Sensors, 2023, 23(8): 3800.
[4]
Ding Y, Lu W, Zhang Y, et al. Study on the practical application of surface acoustic wave yarn tension sensor[J]. IEEE Transactions on Industrial Electronics, 2021, 69(12): 13781-13790.
[5]
Duo X, Can G, Chong G, et al. Novel composite yarn with a wavy-network structure produced by various delivery speed ratios and untwisting factors[J]. Textile Research Journal, 2022, 92(23-24): 4551-4562.
[6]
Zheng B, Jiang G. Design of yarn tension self-adaptive control system on warp knitting machine[J]. The Journal of The Textile Institute, 2024, 115(2): 173-187.
[7]
Li Jie. Principles and Applications of Scientific Knowledge Mapping [M]. Beijing: Higher Education Press, 2018.
[8]
Van Eck N J, Waltman L. Software survey: VOSviewer, acomputer program for bibliometric mapping [J]. Scientometrics, 2010, 84(2): 523-38.
[9]
Ye Jiyuan. Research on Academic Norms and Methodology of Library Science [M]. Beijing: Science Press, 2014.
[10]
Wang Z Y, Li G, Li C Y, et al. Research on the semantic-based co-word analysis[J]. Scientometrics, 2012, 90(3): 855-875.
[11]
Li Haohao, Shan Zhongde, Sun Qili, et al. Non-contact Tension Control Method for Yarn Based on Machine Vision and Experimental Verification [J]. Journal of Nanjing University of Aeronautics and Astronautics, 2024, 56(02): 334-340.
[12]
An Haodong, Yao Entao, Wu Jun. Identification of Yarn Tension Control System Based on Dynamic Forgetting Factor and Parameter Prediction [J/OL]. Control Engineering, 1-8, 2024, 10-20.

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  1. Visual Analysis of Yarn Tension Research

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    CITCE '24: Proceedings of the 4th International Conference on Computer, Internet of Things and Control Engineering
    November 2024
    168 pages
    ISBN:9798400711848
    DOI:10.1145/3705677
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 31 December 2024

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    Author Tags

    1. development trends
    2. research hotspots
    3. scientometric methods
    4. visualization
    5. yarn tension

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