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
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).
___
- Ashdown, S., Improving body movement comfort in apparel, in Improving comfort in clothing. 2011, Elsevier. p. 278-302.
- Roy Choudhury, A.K., P.K. Majumdar, and C. Datta, 1 - Factors affecting comfort: human physiology and the role of clothing, in Improving Comfort in Clothing, G. Song, Editor. 2011, Woodhead Publishing. p. 3-60.
- Pense-Lheritier, A.-M., et al., Sensory evaluation of the touch of a great number of fabrics. Food quality and preference, 2006. 17(6): p. 482-488.
- Atalie, D. and G.K. Rotich, Impact of cotton parameters on sensorial comfort of woven fabrics. Research Journal of Textile and Apparel, 2020. 24(3): p. 281-302.
- Akgun, M., The effect of fabric balance and fabric cover on surface roughness of polyester fabrics. Fibers and Polymers, 2013. 14(8): p. 1372-1377.
- Mao, N., Y. Wang, and J. Qu. Smoothness and roughness: Characteristics of fabric-to-fabric self-friction properties. in The Proceedings of 90th Textile Institute World Conference. 2016. The Textile Institute.
- Akgun, M., B. Becerir, and H.R. Alpay, The effect of fabric constructional parameters on percentage reflectance and surface roughness of polyester fabrics. Textile Research Journal, 2012. 82(7): p. 700-707.
- Classen, E., 3 - Comfort testing of textiles, in Advanced Characterization and Testing of Textiles, P. Dolez, O. Vermeersch, and V. Izquierdo, Editors. 2018, Woodhead Publishing. p. 59-69.
- Beyene, K.A. and S. Gebeyaw, The effects of yarn and fabric structural parameters on surface friction of plain-woven fabrics. Research Journal of Textile and Apparel, 2021. 25(.4): p. 210-218.
- Beyene, K.A. and V. Sampath, Modeling Surface Roughness for prediction and evaluation of Bed-Sheet woven Fabric. CTA-2019, 2019: p. 42.
- Beyene, K. A., Mengie, W., & Korra, C. G., Effects of weft count and weft density on the surface roughness of 3/1 (Z) twill woven fabric. Research Journal of Textile and Apparel, ahead-of-print(ahead-of-print). https://doi.org/10.1108/RJTA-08-2021-0104
- Beyene, K. A., & Kumelachew, D. M., An investigation of the effects of weave types on surface roughness of woven fabric. Textile Research Journal, 2022. 92(14-15).
- Beyene KA. Comparative study of linear and quadratic model equations for prediction and evaluation of surface roughness of a plain-woven fabric. Research Journal of Textile and Apparel. 2022 Feb 22.
- Beyene KA, Korra CG. Modeling for the Prediction and Evaluation of the Crimp Percentage of Plain Woven Fabric Based on Yarn Count and Thread Density. Tekstilec. 2022 Jan 1;65(1).
- Kawabata, S. and M. Niwa, Objective Measurement of Fabric Mechanical Property And Quality. International Journal of Clothing Science and Technology, 1991. 3(1): p. 7-18.
- Myers, R.H., et al., Response surface methodology: a retrospective and literature survey. Journal of quality technology, 2004. 36(1): p. 53-77.
- Raissi, S. and R.-E. Farsani, Statistical process optimization through multi-response surface methodology. World Academy of Science, Engineering and Technology, 2009. 51(46): p. 267-271.
- Normand, S.-L.T., Some old and some new statistical tools for outcomes research. Circulation, 2008. 118(8): p. 872-884.
- Chihara, L., Introduction to Linear Regression Analysis. The American Mathematical Monthly, 2002. 109(7): p. 681.
- Greenland, S., Valid p-values behave exactly as they should: Some misleading criticisms of p-values and their resolution with s-values. The American Statistician, 2019. 73(sup1): p. 106-114.
- Becerir, B., M. Akgun, and H.R. Alpay, Effect of some yarn properties on surface roughness and friction behavior of woven structures. Textile Research Journal, 2016. 86(9): p. 975-989.
- Kayseri, G.Ö., N. Özdil, and G.S. Mengüç, Sensorial comfort of textile materials. Woven fabrics, 2012: p. 235-66.