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Prediction of Mechanical, Evenness and Imperfection Properties of 100% Cotton Ring Spun Yarns with Different Twist Levels
Author(s) -
Gashaw Ashagre Walle,
AUTHOR_ID,
Desalegn Atalie,
Ermiyas Tarekegn,
Addisu Wudneh,
Addisu Desalegn,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
mehran university research journal of engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2413-7219
pISSN - 0254-7821
DOI - 10.22581/muet1982.2201.02
Subject(s) - yarn , twist , ultimate tensile strength , composite material , elongation , materials science , abrasion (mechanical) , species evenness , mathematics , geometry , paleontology , biology , species richness
The purpose of this research work is to develop a model to predict the effect of twist on mechanical, evenness and imperfection properties of 100% cotton ring spun yarns. Eight yarns were manufactured from 30Tex with four yarn twist increments (700, 740, 780 and 820TPM), and 15Tex with twist levels of 900, 1020, 1080 and 1140TPM (turns/meter). Except yarn count and twist level, the yarns were made from the same fiber properties and machine settings. Yarn mechanical properties such as tensile strength, elongation and abrasion resistance, evenness (CVm) and imperfection characteristics like hairiness, neps, thin and thick places were measured and statistically analyzed. Equations were developed to predict the desired mechanical, evenness, and imperfection properties of yarns based on yarn count and twist levels. Statistical analysis showed that yarn properties are significantly influenced by count and twist level changes. The tensile strength, elongation, abrasion resistance, CVm, hairiness and thick places of the studied yarns were significant at P-values of 0.000, 0.003, 0.000, 0.004, 0.000 and 0.015 respectively. Generally, as yarn twist increases, breaking strength and abrasion resistance increase, whereas elongation, hairiness, coefficient of mass variation (CVm), thin and thick places, and nep of yarn decrease. Therefore, the formulated multiple regression equations would be useful in predicting yarn properties based on its count and twist.

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