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A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns

Year 2016, Volume: 1 Issue: 1, 57 - 63, 02.01.2016

Abstract

Raw material costs constitute the majority of the yarn production costs, therefore it is critically important to select the suitable cotton blend and to know required fibre characteristics for spinning. This article is a part of a comprehensive work including the experimental research and the modeling of the physical and mechanical properties of the cotton sirospun yarns. In this paper, a model for estimating sirospun yarn evenness from cotton fibre properties was investigated. For this purpose, different cotton blends were selected from different spinning mills in Turkey and their properties were measured with AFIS (Advanced Fibre Information System). Besides some yarn production parameters were also selected as independent variable (predictor) due to their significant effect. Sirospun yarns were produced at Ege University Textile Engineering Department’s spinning mill under the same conditions. Linear multiple regression method were performed and statistical evaluation showed that generated equations for predicting yarn evenness had a large R2 and adjusted R2 values. 

References

  • . Rupp, J., 2008, Efficient Yarn Production, September-October, 2008, http://www.textileworld.com, http://www.textileworld.com/Issues/2008/September-October/Features/Efficient_Yarn_Production
  • . The Unevenness Limit, www.rieter.com, http://www.rieter.com/cz/rikipedia/articles/technology-ofshort-staple-spinning/reducing-the-unevenness-of-yarn-mass/unevenness-of-yarn-mass/the-unevenness-limit/ (Date Accessed: April 2015)
  • . El Mogahzy Y., Broughton R. M., Lynch W. K.; Statistical Approach for Determining the Technological Value of Cotton Using HVI Fiber Properties, Textile Res. J. Vol. 60 (1990) pp. 495-500.
  • . Majumdar P. K., Majumdar A.; Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models, Textile Res. J., Vol. 74 (2004) pp. 652-655.
  • . Frydrych I. and MatusakM., 2002, Predicting the Nep Number in Cotton Yarn Determining the Critical Nep Size, Textile Research Journal, 72: 917-923.
  • . Majumdar A., Majumdar P. K. and Sarkar B., 2005, Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns, Indian Journal of Fibre&Texile Research, 30: 19-25.
  • . Üreyen M.E. and Kadoğlu H, "Interactions Between AFIS Fibre Properties and Ring Cotton Yarn Properties", Tekstil ve Konfeksiyon, 18 (1), p.8-14., 2008.
  • . Hunter L., 2004, Predicting cotton yarn properties from fibre properties in practice, 27th International Cotton Conference, Bremen, Germany, 62-70.
  • . Ethridge M. D., Zhu R.; Prediction of Rotor Spun Cotton Yarn Quality: A Comparison of Neural Network and Regression Algori¬thms, Proceedings of the Beltwide Cotton Conference Vol. 2, 1996, pp.1314-1317.
  • . Bedez Ute T., Research On Spinning of Short Staple Fibres by Sirospun System, Ege University Graduate School of Natural and Applied Sciences, Master of Science Thesis, 2007.
  • . Bedez Üte T., Kadoğlu H., 2014, Regressional estimation of Cotton Sirospun Yarn Properties From Fibre propertiers, AUTEX Research Journal, Vol. 14, No:3, September 2014, p.161-167.
  • . Bedez Ute T., Kadoglu H., 2012, "Regressional Estimation of Yarn Hairiness of Cotton Sirospun Yarns From Afis Fiber Properties",6th International Textile, Clothing & Design Conference - Magic World of Textiles, October 07-10, Dubrovnik, Croatia.
  • . Bedez Ute T., Kadoglu H., 2012," The Prediction of Yarn Strength of Cotton Sirospun Yarns From AFIS Fiber Properties By Using Linear Regression Analysis", The Inter-Regional Research Network On Cotton For The Mediterranean& Middle East Regions, November 05-07, Antalya.
  • . Characteristics of the Raw Material, www.rieter.com, http://www.rieter.com/tr/rikipedia/articles/technology-ofshort-staple-spinning/raw-material-as-a-factor-influencing-spinning/characteristics-of-the-raw-material/, (Date Accessed: April 2015)
  • . Uster AFIS Pro Brochures, www.uster.com, Uster Technlogies, http://www.uster.com/en/ instruments/fiber-testing/uster-afis-pro/(Date Accessed: 27 Temmuz 2012)
  • . Frydrych I. and Matusiak M., 2002, “Trends of AFIS Application in Research and Industry”, FIBRES & TEXTILES in Eastern Europe, July/September,: 35-39.
  • . Ureyen M.E. & Kadoğlu H, 2006, Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties, Textile Research Journal, Vol 76, 2006, p360.
Year 2016, Volume: 1 Issue: 1, 57 - 63, 02.01.2016

Abstract

References

  • . Rupp, J., 2008, Efficient Yarn Production, September-October, 2008, http://www.textileworld.com, http://www.textileworld.com/Issues/2008/September-October/Features/Efficient_Yarn_Production
  • . The Unevenness Limit, www.rieter.com, http://www.rieter.com/cz/rikipedia/articles/technology-ofshort-staple-spinning/reducing-the-unevenness-of-yarn-mass/unevenness-of-yarn-mass/the-unevenness-limit/ (Date Accessed: April 2015)
  • . El Mogahzy Y., Broughton R. M., Lynch W. K.; Statistical Approach for Determining the Technological Value of Cotton Using HVI Fiber Properties, Textile Res. J. Vol. 60 (1990) pp. 495-500.
  • . Majumdar P. K., Majumdar A.; Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical, and Artificial Neural Network Models, Textile Res. J., Vol. 74 (2004) pp. 652-655.
  • . Frydrych I. and MatusakM., 2002, Predicting the Nep Number in Cotton Yarn Determining the Critical Nep Size, Textile Research Journal, 72: 917-923.
  • . Majumdar A., Majumdar P. K. and Sarkar B., 2005, Application of linear regression, artificial neural network and neuro-fuzzy algorithms to predict the breaking elongation of rotor-spun yarns, Indian Journal of Fibre&Texile Research, 30: 19-25.
  • . Üreyen M.E. and Kadoğlu H, "Interactions Between AFIS Fibre Properties and Ring Cotton Yarn Properties", Tekstil ve Konfeksiyon, 18 (1), p.8-14., 2008.
  • . Hunter L., 2004, Predicting cotton yarn properties from fibre properties in practice, 27th International Cotton Conference, Bremen, Germany, 62-70.
  • . Ethridge M. D., Zhu R.; Prediction of Rotor Spun Cotton Yarn Quality: A Comparison of Neural Network and Regression Algori¬thms, Proceedings of the Beltwide Cotton Conference Vol. 2, 1996, pp.1314-1317.
  • . Bedez Ute T., Research On Spinning of Short Staple Fibres by Sirospun System, Ege University Graduate School of Natural and Applied Sciences, Master of Science Thesis, 2007.
  • . Bedez Üte T., Kadoğlu H., 2014, Regressional estimation of Cotton Sirospun Yarn Properties From Fibre propertiers, AUTEX Research Journal, Vol. 14, No:3, September 2014, p.161-167.
  • . Bedez Ute T., Kadoglu H., 2012, "Regressional Estimation of Yarn Hairiness of Cotton Sirospun Yarns From Afis Fiber Properties",6th International Textile, Clothing & Design Conference - Magic World of Textiles, October 07-10, Dubrovnik, Croatia.
  • . Bedez Ute T., Kadoglu H., 2012," The Prediction of Yarn Strength of Cotton Sirospun Yarns From AFIS Fiber Properties By Using Linear Regression Analysis", The Inter-Regional Research Network On Cotton For The Mediterranean& Middle East Regions, November 05-07, Antalya.
  • . Characteristics of the Raw Material, www.rieter.com, http://www.rieter.com/tr/rikipedia/articles/technology-ofshort-staple-spinning/raw-material-as-a-factor-influencing-spinning/characteristics-of-the-raw-material/, (Date Accessed: April 2015)
  • . Uster AFIS Pro Brochures, www.uster.com, Uster Technlogies, http://www.uster.com/en/ instruments/fiber-testing/uster-afis-pro/(Date Accessed: 27 Temmuz 2012)
  • . Frydrych I. and Matusiak M., 2002, “Trends of AFIS Application in Research and Industry”, FIBRES & TEXTILES in Eastern Europe, July/September,: 35-39.
  • . Ureyen M.E. & Kadoğlu H, 2006, Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties, Textile Research Journal, Vol 76, 2006, p360.
There are 17 citations in total.

Details

Journal Section Makaleler
Authors

Tuba Bedez Üte

Hüseyin Kadoğlu

Publication Date January 2, 2016
Published in Issue Year 2016 Volume: 1 Issue: 1

Cite

APA Bedez Üte, T., & Kadoğlu, H. (2016). A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns. European Journal of Engineering and Natural Sciences, 1(1), 57-63.
AMA Bedez Üte T, Kadoğlu H. A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns. European Journal of Engineering and Natural Sciences. January 2016;1(1):57-63.
Chicago Bedez Üte, Tuba, and Hüseyin Kadoğlu. “A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns”. European Journal of Engineering and Natural Sciences 1, no. 1 (January 2016): 57-63.
EndNote Bedez Üte T, Kadoğlu H (January 1, 2016) A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns. European Journal of Engineering and Natural Sciences 1 1 57–63.
IEEE T. Bedez Üte and H. Kadoğlu, “A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns”, European Journal of Engineering and Natural Sciences, vol. 1, no. 1, pp. 57–63, 2016.
ISNAD Bedez Üte, Tuba - Kadoğlu, Hüseyin. “A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns”. European Journal of Engineering and Natural Sciences 1/1 (January 2016), 57-63.
JAMA Bedez Üte T, Kadoğlu H. A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns. European Journal of Engineering and Natural Sciences. 2016;1:57–63.
MLA Bedez Üte, Tuba and Hüseyin Kadoğlu. “A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns”. European Journal of Engineering and Natural Sciences, vol. 1, no. 1, 2016, pp. 57-63.
Vancouver Bedez Üte T, Kadoğlu H. A Statistical Model for Predicting Yarn Evenness of Cotton Sirospun Yarns. European Journal of Engineering and Natural Sciences. 2016;1(1):57-63.