Abstract
Yarn count is predominant element in the production of specific quality of yarn. As the quality of the natural fibre cotton is blended with various impurities as its growth depends on weather condition, harvesting process, etc. Raw material is a remarkable component for the continuous production and high quality of the product. The texture of cotton raw material is very coarse and unstructured. Textile experts expect good quality of yarn which can be obtained by controlling the impurities. Controlling such impurities of yarn is unbearable to the textile industry manually, and the fuzzy intelligent system helps to overcome all such difficulties through the computational process. Thickness degree of yarn is obtained from the yarn count. The capability of cotton yarn count depends on various parameters like fibre tenacity (FT), mean length (ML), micronaire (MN), length uniformity (LU) and fibre maturity (FM). This paper presents the use of fuzzy inference theory to predict an appropriate cotton yarn count.
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Visalakshi, V., Yogalakshmi, T., Castillo, O. (2022). Analysis of Cotton Yarn Count by Fuzzy Logic Model. In: Tiwari, R., Mishra, A., Yadav, N., Pavone, M. (eds) Proceedings of International Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-3802-2_29
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DOI: https://doi.org/10.1007/978-981-16-3802-2_29
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