Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective Optimization of Yarn Characteristics Using Evolutionary Algorithms: A Comparative Study

  • Original Contribution
  • Published:
Journal of The Institution of Engineers (India): Series E Aims and scope Submit manuscript

Abstract

In cotton spinning industries, attainment of the most desired yarn characteristics mainly depends on different parameters of the ring or rotor spinning process. Thus, it is often required to determine the optimal parametric settings of a spinning process with the help of some optimization tools. In this paper, two multi-response optimization problems are considered and subsequently solved using four popular evolutionary algorithms, i.e. artificial bee colony algorithm, ant colony optimization algorithm, particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II for searching out the global optimal settings of ring and rotor spinning processes. As the process parameters’ settings derived using single response optimization solutions are often impractical to maintain, it is always recommended to set them based on the results of multi-response optimization techniques. It is observed that among these four algorithms, particle swarm optimization excels over the others with respect to the derived optimal solution, consistency of the solution and convergence speed. The developed scatter diagrams also help in investigating the effects of changing values of different process parameters on various yarn qualities.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. P.K. Majumdar, Process control in ring and rotor spinning, in Process Control in Textile Manufacturing, Woodhead Publishing Series in Textiles, eds. by P.K. Majumdar, A. Majumdar, A. Das, R. Alagirusamy, V.K. Kothari, 1st edn. (New Delhi, India, 2013), pp. 191–224

    Chapter  Google Scholar 

  2. S. Sette, L. Boullart, L. Van Langenhove, P. Kiekens, Optimizing the fiber-to-yarn production process with a combined neural network/genetic algorithm approach. Text. Res. J. 67(2), 84–92 (1997)

    Article  Google Scholar 

  3. S. Sette, L. Van Langenhove, Optimising the fibre-to-yarn production process: finding a blend of fibre qualities to create an optimal price/quality yarn. AUTEX Res. J. 2(2), 57–63 (2002)

    Google Scholar 

  4. L. Van Langenhove, S. Sette, The complex relationships between fibres, production parameters and spinning results, in Proceedings of the 14th European Simulation Symposium, Dresden, 1–5 (2002)

  5. S.M. Ishtiaque, R.S. Rengasamy, A. Ghosh, Optimization of ring frame process parameters for better yarn quality and production. Indian J. Fibre Text. Res. 29(2), 190–195 (2004)

    Google Scholar 

  6. A. Majumdar, S.P. Singh, A. Ghosh, Modelling, optimization and decision making techniques in designing of functional clothing. Indian J. Fibre Text. Res. 36(4), 398–409 (2011)

    Google Scholar 

  7. F.A. Arain, A. Tanwari, T. Hussain, Z.A. Malik, Multiple response optimization of rotor yarn for strength, unevenness, hairiness and imperfections. Fibers Polym. 13(1), 118–122 (2012)

    Article  Google Scholar 

  8. J. Feng, B.G. Xu, X.M. Tao, Systematic investigation and optimization of fine cotton yarns produced in a modified ring spinning system using statistical methods. Text. Res. J. 83(3), 238–248 (2012)

    Article  Google Scholar 

  9. J.R. Ochola, J.I. Mwasiagi, Modelling the influence of cotton fibre properties on ring spun yarn strength using Monte Carlo techniques. Res. Rev. Polym. 3(3), 84–88 (2012)

    Google Scholar 

  10. K.L. Jeyaraj, C. Muralidharan, T. Senthilvelan, S.G. Deshmukh, Genetic algorithm based multi-objective optimization of process parameters in color fact finish process—a textile case study. J. Text. Appar. Technol. Manag. 8(3), 1–26 (2013)

    Google Scholar 

  11. A. Ghosh, S. Das, D. Banerjee, Multi objective optimization of yarn quality and fibre quality using evolutionary algorithm. J. Inst. Eng. (India) Ser. E 94(1), 15–21 (2013)

    Article  Google Scholar 

  12. M. El Messiry, N. Hosny, G. Esmat, Optimization of the combing noil percentage for quality single and ply compact spun yarn. Alex. Eng. J. 52(3), 307–311 (2013)

    Article  Google Scholar 

  13. S. Fattahi, S.A. Hoseini Ravandi, Prediction and quantitative analysis of yarn properties from fibre properties using robust regression and extra sum squares. Fibres Text. East. Eur. 21(4), 48–54 (2013)

    Google Scholar 

  14. S. Das, A. Ghosh, Cotton fibre-to-yarn engineering: a simulated annealing approach. Fibres Text. East. Eur. 23(3), 51–53 (2015)

    Article  Google Scholar 

  15. Hasanuzzaman, P.K. Dan, S. Basu, Optimization of ring-spinning process parameters using response surface methodology. J. Text. Inst. 106(5), 510–522 (2015)

    Article  Google Scholar 

  16. M. Eldeeb, I. Rakha, F. Fahim, E. Elshahat, Optimizing the production process of conventional ring spun and compact plied yarns. Tekstil ve Konfeksiyon 26(1), 48–54 (2016)

    Google Scholar 

  17. A.S.A. Bagwan, A. Patil, Optimization of opening roller speed on properties of open end yarn. J. Text. Sci. Eng. 6(1), 1–3 (2016)

    Google Scholar 

  18. A. Majumdar, P. Mal, A. Ghosh, D. Banerjee, Multi-objective optimization of air permeability and thermal conductivity of knitted fabrics with desired ultraviolet protection. J. Text. Inst. 108(1), 110–116 (2017)

    Article  Google Scholar 

  19. A. Mukhopadhyay, V.K. Midha, N.C. Ray, Multi-objective optimization of parametric combination of injected slub yarn for producing knitted and woven fabrics with least abrasive damage. Res. J. Text. Appar. 21(2), 111–133 (2017)

    Article  Google Scholar 

  20. D. Karaboga, B. Basturk, On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  21. W. Gao, S. Liu, L. Huang, A global best artificial bee colony algorithm for global optimization. J. Comput. Appl. Math. 236(11), 2741–2753 (2012)

    Article  MathSciNet  Google Scholar 

  22. M. Dorigo, E. Bonabeau, G. Theraulaz, Ant algorithms and stigmergy. Future Gener. Comput. Syst. 16(8), 851–871 (2000)

    Article  Google Scholar 

  23. M. Dorigo, C. Blum, Ant colony optimization theory: a survey. Theoret. Comput. Sci. 344(2–3), 243–278 (2005)

    Article  MathSciNet  Google Scholar 

  24. R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  25. M.S. Rao, N. Venkaiah, Parametric optimization in machining of Nimonic-263 alloy using RSM and particle swarm optimization. Proc. Mater. Sci. 10, 70–79 (2015)

    Article  Google Scholar 

  26. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  27. Y. Yusoff, M.S. Ngadiman, A.M. Zain, Overview of NSGA-II for optimizing machining process parameters. Proc. Eng. 15, 3978–3983 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India

    Shankar Chakraborty

  2. Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Majitar, Sikkim, India

    Sunny Diyaley

Authors
  1. Shankar Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunny Diyaley
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shankar Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chakraborty, S., Diyaley, S. Multi-objective Optimization of Yarn Characteristics Using Evolutionary Algorithms: A Comparative Study. J. Inst. Eng. India Ser. E 99, 129–140 (2018). https://doi.org/10.1007/s40034-018-0121-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40034-018-0121-8

Keywords

Search

Navigation