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RESPONSE SURFACE DESIGNS IN QUALITY CONTROL: YARN IRREGULARITY EXERCISE

Year 2017, Volume: 27 Issue: 3, 289 - 299, 30.09.2017

Abstract




It is proposed that response surface designs with feasable region is an effective tool in prediction of a specific property of a product
from the known properties of raw material, with a yarn irregularity exercise, for the first time in textile literature in this paper. The data
used in this research is obtained from a cotton yarn spinning mill, being real production data. Response surface designs with feasible
region are used to obtain the relationship between the response variable (yarn irregularity) and effecting factors (fiber properties) in 3D
graphs and contour lines both for yarn in bobbin and cop form separately. It is concluded that this novel method of response surface
designs with feasible region provides valuable results, is effective in prediction, is benificial in textile quality control, and can also be
widely used in other industry branches when it is incorporated into a statistical quality control computer program. 




References

  • 1. Üreyen, M. E. and Kadoğlu, H., “Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties”, Textile Research Journal, 2006, 76(5): 360-366.
  • 2. Üreyen, M. E. and Kadoğlu, H., “The Prediction of Cotton Ring Yarn Properties from AFIS Fibre Properties by Using Linear Regression Models”, Fibres & Textiles in Eastern Europe, 2007, 15(4): 63-67.
  • 3. Üreyen, M. E. and Gürkan, P., “Comparison of Artificial Neural Network and Linear Regression Models for Prediction of Ring Spun Yarn Properties I. Prediction of Yarn Tensile Properties”, Fibers And Polymers, 2008, 9(1): 87-91.
  • 4. Üreyen, M. E. and Gürkan, P., “Comparison of Artificial Neural Network and Linear Regression Models for Prediction of Ring Spun Yarn Properties II. Prediction of Yarn Hairiness and Unevenness”, Fibers And Polymers, 2008, 9(1): 92-96.
  • 5. Chattopadhyay, R. and Guha, A., “Performance of Neural Networks for Predicting Yarn Properties Using Principal Component Analysis”, Journal of Applied Polymer Science, 2004, 91(3): 1746-1751.
  • 6. Babay, A., Cheikhrouhou, M., Vermeulen, B., Rabenasolo, B., and Castelain, J.M., “Selecting The Optimal Neural Network Architecture For Predicting Cotton Yarn Hairness”, The Journal of Textile Institute, 2004, 96(3): 185-192.
  • 7. Majumdar, A., Majumdar, P.K., and Sarkarve, B., “Prediction of Single Yarn Tenacity of Ring and Rotor Spun Yarns from HVI Results Using Artificial Neural Networks”, Indian Journal of Fibre & Textile Research, 2004, 29:157-162.
  • 8. Majumdar, P. K. and Majumdar, A., “Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical and Artificial Neural Network Models”, Textile Research Journal, 2004, 74(7): 652-655.
  • 9. Mwasiagi, J.I., XiuBao, H., and XinHouve, W., “Predicting Yarn Tensile Strength Using Elman Network”, In: Proceedings of the Beltwide Cotton Conferences, New Orleans, Louisiana, 2007, pp.1924-1929.
  • 10. Cheng, L. and Adams, D. L., “Yarn Strength Prediction Using Neural Networks Part I: Fiber Properties and Yarn Strength Relationship”, Textile Research Journal, 1995, 65(9): 495-500.
  • 11. Üreyen, M. E. and Kadoğlu, H., “Ring Pamuk İplikleri ile AFIS Lif Özellikleri Arasındaki İnteraksiyonlar” (Interactions Between AFIS Fibre Properties and Ring Cotton Yarn Properties), Tekstil ve Konfeksiyon Dergisi (Journal of Textile & Apparel), 2008, 1:8-13.
  • 12. Üreyen, M. E. and Kadoğlu, H., “Ring Pamuk İplikleri ile HVI Lif Özellikleri Arasındaki İnteraksiyonlar” (Interactions Between HVI Fibre Properties and Ring Cotton Yarn Properties), Tekstil ve Konfeksiyon Dergisi (Journal of Textile & Apparel), 2006, 3:180-184.
  • 13. Şengöz, G., “Application of Data Chart To Yarn Characteristic Values”, In: The 39th Textile Research Symposium, New Delhi, India, Dec. 16-18, 2010, pp.251-260.
  • 14. “Nurwaha, D.; and Wang, X. H., “Using Intelligent Control Systems to Predict Textile Yarn Quality”,. Fibres & Textiles in Eastern Europe, 2012, 20, 1(90): 23-27”.
  • 15. Arslan, P., İplik Kalite Kontrolünde Yorumsal Analizler (Interpretational Analysis for Yarn Quality Control). MSc. Thesis, Uşak University, Turkey, 2011, Supervisor : Asist.Prof.Dr.N.Gönül Şengöz, p.208.
  • 16. Box, G. E. P. and Draper, N. R., Empirical Model-Building and Response Surfaces. John Wiley & Sons, New York, 1987, p.357.
  • 17. www.statsoft.com/design-of-experiments.
  • 18. Montgomery, D. C., Design and Analysis of Experiments. 7th ed.”, John Wiley & Sons, New York, 2009, p.656.
  • 19. Antony, J., Design of Experiments for Engineers and Scientists. Elsevier Science & Technology Books, Great Britain, UK, 2003, p.287.
  • 20. JMP 8 Design of Experiments Guide. 2nd ed. Sas Publishing, Cary, North Carolina, 2009, p.273.
  • 21. www.mathworks.com/design-of-experiments
  • 22. Bradley, N., The Response Surface Methodology. MSc. Thesis, Indiana University, South Bend, USA, 2007, p.84.
  • 23. Amago, T., “Response Surface Methodology and Its Application to Automotive Suspension Designs”, Toyota Central R&D Labs., Technology Public Relations Sec., Intellectural Property Div., Nagoya, Japan, 2000, p.32.
  • 24. Myers, R. H. and Montgomery, D. C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 2nd ed. John Wiley & Sons, New York, 2002, p.380.

KALİTE KONTROLDE YANIT YÜZEY DESENLERİ: İPLİK DÜZGÜNSÜZLÜĞÜ ÖRNEĞİ

Year 2017, Volume: 27 Issue: 3, 289 - 299, 30.09.2017

Abstract




Bu çalışmada, yanıt yüzey desenlerinin elverişli bölge ile beraber kullanılmasının, hammaddenin bilinen özelliklerinden ürünün belli bir
özelli
ğinin tahminlenmesinde etkili bir araç olduğu, tekstil literatüründe bir ilk olarak bu çalışmada ortaya konulmuştur. Bu araştırmada, bir
pamuk ipli
ği fabrikasının gerçek üretim verileri kullanılmıştır. Hem kops hem bobin halindeki iplik için, yanıt değişkeni (iplik
düzgünsüzlü
ğü) ve etken faktörler (lif özellikleri) arasındaki ilişkinin elde edilmesi için 3-boyutlu grafik olan yanıt yüzey desenleri elverişli
bölge ile beraber kullan
ılmıştır. Sonuç olarak, özgün bir metod olan yanıt yüzey desenlerinin elverişli bölge ile beraber kullanılmasının
de
ğerli sonuçlar verdiği, tahminlenme için etkili olduğu, tekstil kalite kontrolu için faydalı olduğu ve istatistiksel kalite kontrolu yapan bir
bilgisayar program
ının içine eklendiği zaman endüstrinin diğer dallarında da yaygın olarak kullanılabileceği belirlenmiştir. 




References

  • 1. Üreyen, M. E. and Kadoğlu, H., “Regressional Estimation of Ring Cotton Yarn Properties from HVI Fiber Properties”, Textile Research Journal, 2006, 76(5): 360-366.
  • 2. Üreyen, M. E. and Kadoğlu, H., “The Prediction of Cotton Ring Yarn Properties from AFIS Fibre Properties by Using Linear Regression Models”, Fibres & Textiles in Eastern Europe, 2007, 15(4): 63-67.
  • 3. Üreyen, M. E. and Gürkan, P., “Comparison of Artificial Neural Network and Linear Regression Models for Prediction of Ring Spun Yarn Properties I. Prediction of Yarn Tensile Properties”, Fibers And Polymers, 2008, 9(1): 87-91.
  • 4. Üreyen, M. E. and Gürkan, P., “Comparison of Artificial Neural Network and Linear Regression Models for Prediction of Ring Spun Yarn Properties II. Prediction of Yarn Hairiness and Unevenness”, Fibers And Polymers, 2008, 9(1): 92-96.
  • 5. Chattopadhyay, R. and Guha, A., “Performance of Neural Networks for Predicting Yarn Properties Using Principal Component Analysis”, Journal of Applied Polymer Science, 2004, 91(3): 1746-1751.
  • 6. Babay, A., Cheikhrouhou, M., Vermeulen, B., Rabenasolo, B., and Castelain, J.M., “Selecting The Optimal Neural Network Architecture For Predicting Cotton Yarn Hairness”, The Journal of Textile Institute, 2004, 96(3): 185-192.
  • 7. Majumdar, A., Majumdar, P.K., and Sarkarve, B., “Prediction of Single Yarn Tenacity of Ring and Rotor Spun Yarns from HVI Results Using Artificial Neural Networks”, Indian Journal of Fibre & Textile Research, 2004, 29:157-162.
  • 8. Majumdar, P. K. and Majumdar, A., “Predicting the Breaking Elongation of Ring Spun Cotton Yarns Using Mathematical, Statistical and Artificial Neural Network Models”, Textile Research Journal, 2004, 74(7): 652-655.
  • 9. Mwasiagi, J.I., XiuBao, H., and XinHouve, W., “Predicting Yarn Tensile Strength Using Elman Network”, In: Proceedings of the Beltwide Cotton Conferences, New Orleans, Louisiana, 2007, pp.1924-1929.
  • 10. Cheng, L. and Adams, D. L., “Yarn Strength Prediction Using Neural Networks Part I: Fiber Properties and Yarn Strength Relationship”, Textile Research Journal, 1995, 65(9): 495-500.
  • 11. Üreyen, M. E. and Kadoğlu, H., “Ring Pamuk İplikleri ile AFIS Lif Özellikleri Arasındaki İnteraksiyonlar” (Interactions Between AFIS Fibre Properties and Ring Cotton Yarn Properties), Tekstil ve Konfeksiyon Dergisi (Journal of Textile & Apparel), 2008, 1:8-13.
  • 12. Üreyen, M. E. and Kadoğlu, H., “Ring Pamuk İplikleri ile HVI Lif Özellikleri Arasındaki İnteraksiyonlar” (Interactions Between HVI Fibre Properties and Ring Cotton Yarn Properties), Tekstil ve Konfeksiyon Dergisi (Journal of Textile & Apparel), 2006, 3:180-184.
  • 13. Şengöz, G., “Application of Data Chart To Yarn Characteristic Values”, In: The 39th Textile Research Symposium, New Delhi, India, Dec. 16-18, 2010, pp.251-260.
  • 14. “Nurwaha, D.; and Wang, X. H., “Using Intelligent Control Systems to Predict Textile Yarn Quality”,. Fibres & Textiles in Eastern Europe, 2012, 20, 1(90): 23-27”.
  • 15. Arslan, P., İplik Kalite Kontrolünde Yorumsal Analizler (Interpretational Analysis for Yarn Quality Control). MSc. Thesis, Uşak University, Turkey, 2011, Supervisor : Asist.Prof.Dr.N.Gönül Şengöz, p.208.
  • 16. Box, G. E. P. and Draper, N. R., Empirical Model-Building and Response Surfaces. John Wiley & Sons, New York, 1987, p.357.
  • 17. www.statsoft.com/design-of-experiments.
  • 18. Montgomery, D. C., Design and Analysis of Experiments. 7th ed.”, John Wiley & Sons, New York, 2009, p.656.
  • 19. Antony, J., Design of Experiments for Engineers and Scientists. Elsevier Science & Technology Books, Great Britain, UK, 2003, p.287.
  • 20. JMP 8 Design of Experiments Guide. 2nd ed. Sas Publishing, Cary, North Carolina, 2009, p.273.
  • 21. www.mathworks.com/design-of-experiments
  • 22. Bradley, N., The Response Surface Methodology. MSc. Thesis, Indiana University, South Bend, USA, 2007, p.84.
  • 23. Amago, T., “Response Surface Methodology and Its Application to Automotive Suspension Designs”, Toyota Central R&D Labs., Technology Public Relations Sec., Intellectural Property Div., Nagoya, Japan, 2000, p.32.
  • 24. Myers, R. H. and Montgomery, D. C., Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 2nd ed. John Wiley & Sons, New York, 2002, p.380.
There are 24 citations in total.

Details

Journal Section Articles
Authors

Nefise Gönül Şengöz This is me

Pınar Arslan This is me

Publication Date September 30, 2017
Submission Date September 30, 2017
Acceptance Date June 20, 2017
Published in Issue Year 2017 Volume: 27 Issue: 3

Cite

APA Şengöz, N. G., & Arslan, P. (2017). RESPONSE SURFACE DESIGNS IN QUALITY CONTROL: YARN IRREGULARITY EXERCISE. Textile and Apparel, 27(3), 289-299.

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