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PREDICTING THE DYNAMIC COHESION IN DRAFTED SLIVERS AT DRAW FRAME USING ARTIFICIAL NEURAL NETWORKS

Year 2014, Volume: 24 Issue: 3, 286 - 290, 01.12.2014

Abstract

The cohesion among the fibers in a sliver assembly plays an important role in determining the material behavior during further drafting operations. A proper control exerted on fiber to fiber friction can help to eliminate the drafting problems during the spinning process and positively influence yarn quality. The present research work aims to explain the influence on various draw frame parameters on the sliver cohesion. Cotton, Polyester and Cotton polyester blend were selected and processed using different draw frame variables. The dynamic cohesion force was measured using Rothschild Cohesion Meter. Different materials showed different level of cohesion, whereas, draw frame variables also influenced the cohesion forces in drafted slivers. An artificial neural network (ANN) model was developed to predict the sliver cohesion by using draw frame parameters as input to the ANN. The results showed that cohesion force in drafted slivers can be successfully predicted with the help of ANNs

References

  • 1. Korkmaz Y.A., Behery H.M. Relationship between Fiber Fineness, Break Draft and Drafting Force in Roller Drafting. Textile Research Journal 2004; 74, 5: 405-408.
  • 2. Grishin P. Analysis of the Drafting Process. Journal of Textile Institute 1954; 45, 2: 179-191.
  • 3. Djiev S.N. Modeling a Double-Zone Drafter as an Object of Control. Textile Research Journal 1994; 64, 8: 449-456.
  • 4. Huh Y., Kim J.S. Modeling the Dynamic Behavior of the Fiber Bundle in a Roll-Drafting Process. Textile Research Journal 2004; 74, 10: 872-878.
  • 5. Gupta B.S., El-Mogahzy Y.E. Friction in Fibrous Materials: Part I: Structural Model. Textile Research Journal 1991; 61, 9: 547-555.
  • 6. Skelton J. Frictional Effects in Fibrous Assemblies. Textile Research Journal 1974; 44, 10: 746-752.
  • 7. Vrooman F., Monfort F. Studies in Modern Yarn Production. The Textile Institute, Manchester, 1968, pp. 13-14.
  • 8. Korkmaz Y. The Effect of Fine Denier Polyester Fiber Fineness on Dynamic Cohesion Force. FIBERS & TEXTILES in Eastern Europe 2004; 12, 1: 24-26.
  • 9. Farooq A., Cherif C. Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame. Textile Research Journal 2008; 78, 6: 502-509.
  • 10. Farooq A., Cherif C. Development of Prediction System Using Artificial Neural Networks for the Optimization of Spinning Process. Fibers and Polymers 2012; 13, 2: 253-257.
  • 11. Farooq A. Development of Prediction Systems Using Artificial Neural Networks for Intelligent Spinning Machines. Technische Universität Dresden, Germany, 2010.
  • 12. Ünal, P.G., Arikan, C., Özdil, N., Taskin, C. The Effect of Fiber Properties on the Characteristics of Spliced Yarns: Part II: Prediction of Retained Spliced Diameter. Textile Research Journal, 2010; 80, 17: 1751-1758.
  • 13. Behera, B.K., Goyal, Y. Artificial Neural Network System for the Design of Airbag Fabrics. Journal of Industrial Textiles, 2009; 39, 1: 45-55.
  • 14. Rothschild. Rothschild Operating Manual Cohesion Meter R-2020. Rothschild, Zurich, Switzerland, 2008.
  • 15. Rumelhart D.E., Durbin R., Golden R., Chauvin Y. Backpropagation: The Basic Theory, Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum, Hillsdale, 1995, pp. 1-34.
  • 16. Desai J.V., Kane C.D., Bandyopadhayay B. Neural Networks: An Alternative Solution for Statistically Based Parameter Prediction. Textile Research Journal 2004; 74, 3: 227-230.
  • 17. Fine T.L. Feedforward Neural Network Methodology. Springer Verlag, New York Inc. 1999.
  • 18. Caruana R., Lawrence S., Giles L. Overfitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping. Advances in Neural Information Processing Systems, The MIT Press, 2001, pp. 402-409
  • 19. Mackay D.J.C. Bayesian Interpolation. Neural Computation 1992; 4, 3: 415-447.
  • 20. Foresee F.D., Hagan M.T. Gauss-Newton Approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks, Bd. 3 Piscataway: IEEE, 1997, pp. 1930-1935.
  • 21. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. International joint Conference on Artificial Intelligence, Volume 2, 1995, pp. 1137-1143.

YAPAY SİNİR AĞLARI KULLANILARAK CER MAKİNESİNDE ÇEKİLMİŞ ŞERİTTE DİNAMİK KOHEZYONUN TAHMİNLENMESİ

Year 2014, Volume: 24 Issue: 3, 286 - 290, 01.12.2014

Abstract

References

  • 1. Korkmaz Y.A., Behery H.M. Relationship between Fiber Fineness, Break Draft and Drafting Force in Roller Drafting. Textile Research Journal 2004; 74, 5: 405-408.
  • 2. Grishin P. Analysis of the Drafting Process. Journal of Textile Institute 1954; 45, 2: 179-191.
  • 3. Djiev S.N. Modeling a Double-Zone Drafter as an Object of Control. Textile Research Journal 1994; 64, 8: 449-456.
  • 4. Huh Y., Kim J.S. Modeling the Dynamic Behavior of the Fiber Bundle in a Roll-Drafting Process. Textile Research Journal 2004; 74, 10: 872-878.
  • 5. Gupta B.S., El-Mogahzy Y.E. Friction in Fibrous Materials: Part I: Structural Model. Textile Research Journal 1991; 61, 9: 547-555.
  • 6. Skelton J. Frictional Effects in Fibrous Assemblies. Textile Research Journal 1974; 44, 10: 746-752.
  • 7. Vrooman F., Monfort F. Studies in Modern Yarn Production. The Textile Institute, Manchester, 1968, pp. 13-14.
  • 8. Korkmaz Y. The Effect of Fine Denier Polyester Fiber Fineness on Dynamic Cohesion Force. FIBERS & TEXTILES in Eastern Europe 2004; 12, 1: 24-26.
  • 9. Farooq A., Cherif C. Use of Artificial Neural Networks for Determining the Leveling Action Point at the Auto-leveling Draw Frame. Textile Research Journal 2008; 78, 6: 502-509.
  • 10. Farooq A., Cherif C. Development of Prediction System Using Artificial Neural Networks for the Optimization of Spinning Process. Fibers and Polymers 2012; 13, 2: 253-257.
  • 11. Farooq A. Development of Prediction Systems Using Artificial Neural Networks for Intelligent Spinning Machines. Technische Universität Dresden, Germany, 2010.
  • 12. Ünal, P.G., Arikan, C., Özdil, N., Taskin, C. The Effect of Fiber Properties on the Characteristics of Spliced Yarns: Part II: Prediction of Retained Spliced Diameter. Textile Research Journal, 2010; 80, 17: 1751-1758.
  • 13. Behera, B.K., Goyal, Y. Artificial Neural Network System for the Design of Airbag Fabrics. Journal of Industrial Textiles, 2009; 39, 1: 45-55.
  • 14. Rothschild. Rothschild Operating Manual Cohesion Meter R-2020. Rothschild, Zurich, Switzerland, 2008.
  • 15. Rumelhart D.E., Durbin R., Golden R., Chauvin Y. Backpropagation: The Basic Theory, Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum, Hillsdale, 1995, pp. 1-34.
  • 16. Desai J.V., Kane C.D., Bandyopadhayay B. Neural Networks: An Alternative Solution for Statistically Based Parameter Prediction. Textile Research Journal 2004; 74, 3: 227-230.
  • 17. Fine T.L. Feedforward Neural Network Methodology. Springer Verlag, New York Inc. 1999.
  • 18. Caruana R., Lawrence S., Giles L. Overfitting in Neural Networks: Backpropagation, Conjugate Gradient, and Early Stopping. Advances in Neural Information Processing Systems, The MIT Press, 2001, pp. 402-409
  • 19. Mackay D.J.C. Bayesian Interpolation. Neural Computation 1992; 4, 3: 415-447.
  • 20. Foresee F.D., Hagan M.T. Gauss-Newton Approximation to Bayesian learning. Proceedings of the 1997 International Joint Conference on Neural Networks, Bd. 3 Piscataway: IEEE, 1997, pp. 1930-1935.
  • 21. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. International joint Conference on Artificial Intelligence, Volume 2, 1995, pp. 1137-1143.
There are 21 citations in total.

Details

Other ID JA88ZA42PH
Journal Section Articles
Authors

Assad Farooq This is me

Publication Date December 1, 2014
Submission Date December 1, 2014
Published in Issue Year 2014 Volume: 24 Issue: 3

Cite

APA Farooq, A. (2014). PREDICTING THE DYNAMIC COHESION IN DRAFTED SLIVERS AT DRAW FRAME USING ARTIFICIAL NEURAL NETWORKS. Textile and Apparel, 24(3), 286-290.

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