Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

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Tekstil ve Konfeksiyon
Volume:33 Issue:1
Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Linear Model Equation for Prediction and Evaluation of Surface Roughness of Plain-Woven Fabric

Authors : Kura Alemayehu BEYENE, Nuredin MUHAMMED

Pages : 88-94

Doi:10.32710/tekstilvekonfeksiyon.1026926

View : 90 | Download : 13

Publication Date : 2023-03-31

Article Type : Research Paper

Abstract :Nowadays, evaluating fabric touch can be a great interest of industries to match the quality needs of consumers and parameters for the manufacturing process. Modeling helps to determine how structural parameters of fabric affect the surface of a fabric and also identify the way they influence fabric properties. Moreover, it helps estimate and evaluates without the complexity and time-consuming experimental procedures. In this research paper, the linear regression model was developed that was utilized for the prediction and evaluation of surface roughness of plain-woven fabric. The model was developed based on nine different half-bleached plain-woven fabrics with three weft Yarn counts 42 tex, 29.5 tex & 14.76 tex, and three weft thread densities insert ignore into journalissuearticles values(18 picks per c, 21ppc & 24 picks per c); and then the surface roughness of plain-woven fabric was tested by using Kawabata insert ignore into journalissuearticles values(KES-FB4); testing instrument. The findings reveal that the effects of count and density on the roughness of plain-woven fabric were found statistically significant at the confidence interval of 95%. The weft yarn count has a positive correlation with surface roughness values of plain-woven fabrics. On the other hand, pick density has a negative correlation with the surface roughness values of plain-woven fabrics. The correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95% insert ignore into journalissuearticles values(R² of 0.97);.
Keywords : Surface Roughness, Regression Modeling, Design Expert, Prediction, Plain Woven Fabric, and Structural Parameters

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