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AI InsightThe correlation between measured surface roughness by KES-FB4 and calculated surface roughness by the model equation show how they are strongly correlated at 95% (R² of 0.97).
📌 3 Maddede Bu Makale
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 (18 picks per c, 21ppc & 24 picks per c) and then the surface roughness of plain-woven fabric was tested by using Kawabata (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% (R² of 0.97).