Modeling for the Prediction and Evaluation of the Crimp Percentage of Plain Woven Fabric Based on Yarn Count and Thread Density

Kura Alemayehu Beyene; Chirato Godana Korra

doi:10.14502/tekstilec.65.2021027

Authors

Kura Alemayehu Beyene Bahir Dar University, Ethiopian Institute of Textile and Fashion Technology (EiTEX), Bahir Dar Ethiopia Author https://orcid.org/0000-0002-2293-5048
Chirato Godana Korra Bahir Dar University, Ethiopian Institute of Textile and Fashion Technology (EiTEX), Bahir Dar Ethiopia Author

DOI:

https://doi.org/10.14502/tekstilec.65.2021027

Keywords:

crimp percentage, regression modeling, thread density, yarn count, plain-weave

Abstract

Nowadays, modeling is used for evaluating and controlling the weft crimp percentage before and after manufacturing plain woven fabrics. Also, modeling assists in estimating and evaluating crimp percentage without complex and time-consuming experimental procedures. The purpose of this study is to develop a linear regression model that can be employed for the prediction and evaluation of the weft crimp percentage of plain woven fabric. For this study, nine plain woven fabrics of 100% cotton were produced with three different wefts thread densities and weft yarn linear densities. From the findings, the effects of weft count and weft density on the weft crimp percentage of the fabrics were found to be statistically significant with a confidence interval of 95%. The weft crimp percentage showed a positive correlation with weft count and weft density. The weft count and weft density have multicollinearity in the model because the variance inflation factors (VIFs) values are greater than one, which are 1.70 & 1.20, respectively. The model was tested by correlating measured crimp percentage values obtained with a crimp tester instrument to the crimp percentage values calculated by a developed linear model equation. The result disclosed that the model was strongly correlated, with a confidence interval of 95% (R² of 0.9518). Furthermore, the significance value of the t-test is not significant for both the measured weft crimp percentage values and the calculated weft crimp percentage values, which means that they do not differ significantly. Crimp percentage is impacted by fiber, yarn, fabric structural parameters and machine setting parameters. This makes the crimp percentage difficult to control and study, but this developed model can be easily used by manufacturers or researchers for controlling and studying purposes. Thus, the model can be used to produce a fabric with a pre-controlled weft crimp percentage. It can also be used to evaluate and predict the weft crimp percentage before and after fabric production.

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References

HUA, T. Fabric making technologies. In Handbook of fibrous materials. Edited by Jinlian Hu, Bipin Kumar and Jing Lu. Weinheim : Wiley, 2020, 477−498, doi: 10.1002/9783527342587.ch19. DOI: https://doi.org/10.1002/9783527342587.ch19

PEIRCE, F.T. 5 - The geometry of cloth structure. Journal of the Textile Institute Transactions, 1937, 28(3), T45−T96, doi: 10.1080/19447023708658809. DOI: https://doi.org/10.1080/19447023708658809

SIRKOVÁ, B.K., MERTOVÁ, I. Prediction of warp and weft crimp in the construction of dobby woven fabrics. The Journal of The Textile Institute, 2020, 111(10), 1401−1409, doi: 10.1080/00405000.2019.1701967. DOI: https://doi.org/10.1080/00405000.2019.1701967

AFROZ, F., SIDDIKA, A. Effect of warp yarn tension on crimp% in woven fabric. European Scientific Journal, 2014, 10(24), 202−207.

OZKAN, G., EREN, R. Warp tension distribution over the warp width and its effect on crimp distribution in woven fabrics. International Journal of Clothing Science and Technology, 2010, 22(4), 272−284, doi: 10.1108/09556221011048295. DOI: https://doi.org/10.1108/09556221011048295

ALA, D., BAKICI, G.G.E. Effects of weft count and weft density on yarn crimp% of unbleached and bleached 3/1(s) twill woven fabrics. Journal of Textile Engineering, 2017, 24(108), 254−259, doi: 10.7216/1300759920172410804. DOI: https://doi.org/10.7216/1300759920172410804

MERTOVA, I., NECKAR, B., ISHTIAQUE, S.M. New method to measure yarn crimp in woven fabric. Textile Research Journal, 2015, 86(10), 1084−1096, doi: 10.1177/0040517514551464. DOI: https://doi.org/10.1177/0040517514551464

DAS, A., KOTHARI, V.K., BALAJI, M. Studies on cotton–acrylic bulked yarns and fabrics. Part II: fabric characteristics. The Journal of The Textile Institute, 2007, 98(4), 363−376, doi: 10.1080/00405000701550098. DOI: https://doi.org/10.1080/00405000701550098

LORD, P.R. Handbook of yarn production: technology, science and economics. Cambridge : Woodhead Publishing, 2003, 1−17. DOI: https://doi.org/10.1533/9781855738652.1

MYERS, R.H., MONTGOMERY, D.C., VINING, G.G., BORROR, C.M., KOWALSKI, S.M. Response surface methodology: a retrospective and literature survey. Journal of Quality Technology, 2004, 36(1), 53−77, doi: 10.1080/00224065.2004.11980252. DOI: https://doi.org/10.1080/00224065.2004.11980252

BAŞ, D., BOYACI, İ.H. Modeling and optimization I: usability of response surface methodology. Journal of Food Engineering, 2007, 78(3), 836−845, doi: 10.1016/j.jfoodeng.2005.11.024. DOI: https://doi.org/10.1016/j.jfoodeng.2005.11.024

SADA, S. Modeling performance of response surface methodology and artificial neural network. Journal of Applied Sciences and Environmental Management, 2018, 22(6), 875−881, doi: 10.4314/jasem.v22i6.6. DOI: https://doi.org/10.4314/jasem.v22i6.6

BRAUN, M.T., OSWALD, F.L. Exploratory regression analysis: a tool for selecting models and determining predictor importance. Behavior Research Methods, 2011, 43(2), 331−339, doi: 10.3758/s13428-010-0046-8. DOI: https://doi.org/10.3758/s13428-010-0046-8

KHURI, A. Introduction to linear regression analysis, fifth edition by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining (short book review). International Statistical Review, 2013, 81(2), 318−319, doi: 10.1111/insr.12020_10. DOI: https://doi.org/10.1111/insr.12020_10

RYAN, T.P., GRAY, J.B. Introduction to regression modeling. Journal of Quality Technology, 2006, 38(4), 376−377, doi: 10.1080/00224065.2006.11918625. DOI: https://doi.org/10.1080/00224065.2006.11918625

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), 106−114, doi: 10.1080/00031305.2018.1529625. DOI: https://doi.org/10.1080/00031305.2018.1529625

MAQSOOD, M., HUSSAIN, T., NAWAB, Y., SHAKER, K., UMAIR, M. Prediction of warp and weft yarn crimp in cotton woven fabrics. The Journal of The Textile Institute, 2015, 106(11), 1180−1189, doi: 10.1080/00405000.2014.981041. DOI: https://doi.org/10.1080/00405000.2014.981041

HARI, P.K. 1 - Types and properties of fibres and yarns used in weaving. In Woven textiles: principles, technologies and applications. Edited by Kim Gandhi. 2nd edition. Cambridge : Woodhead Publishing; 2020, 3−34, doi: 10.1533/9780857095589.1.3. DOI: https://doi.org/10.1016/B978-0-08-102497-3.00001-5

KADI, N. The effect of warp and weft variables on fabric’s shrinkage ratio. Journal of Textile Science & Engineering, 2015, 5(2), 1−8, doi: 10.4172/2165-8064.1000191. DOI: https://doi.org/10.4172/2165-8064.1000191

CRANEY, T.A., SURLES, J.G. Model-dependent variance inflation factor cutoff values. Quality Engineering, 2002, 14(3), 391−403, doi: 10.1081/QEN-120001878. DOI: https://doi.org/10.1081/QEN-120001878

MIDI, H., SARKAR, S.K., RANA, S. Collinearity diagnostics of binary logistic regression model. Journal of Interdisciplinary Mathematics, 2010, 13(3), 253−267, doi: 10.1080/09720502.2010.10700699. DOI: https://doi.org/10.1080/09720502.2010.10700699

DORMANN, C.F., ELITH, J., BACHER, S., BUCHMANN, C., CARL, G., CARRÉ, G., et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 2013, 36(1), 27−46, doi: https://doi.org/10.1111/j.1600-0587.2012.07348.x. DOI: https://doi.org/10.1111/j.1600-0587.2012.07348.x

BEYENE, K.A., SAMPATH, V. Modeling surface roughness for prediction and evaluation of bed-sheet woven fabric. In International Conference Proceedings CTA- 2019, EiTEX, BDU, Ethiopia. Edited by Tamrat Tesafye and Berihun Bizuneh. Bahir Dar : Ethiopian Institute of Textile and Fashion Technology (EiTEX), 2019, 42−48.

CAVAZZUTI, M. Optimization methods: from theory to design scientific and technological aspects in mechanics. Berlin, Heidelberg : Springer, 2013, 13−42. DOI: https://doi.org/10.1007/978-3-642-31187-1_2

ENDERS, C.K., TOFIGHI, D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychological Methods, 2007, 12(2), 121−138, doi: 10.1037/1082-989X.12.2.121. DOI: https://doi.org/10.1037/1082-989X.12.2.121