Skip to main content
SpringerLink
Log in

Adaptive Neuro-fuzzy Inference System-based Modelling of Cotton Yarn Properties

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

Abstract

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed for effective prediction of yarn tenacity and unevenness based on a set of six input cotton fibre properties, i.e. fibre strength, fibre elongation, upper half mean length, uniformity index, fineness and short fibre content. The ANFIS model integrates the advantageous features of both the systems of fuzzy control and neural network. A neural network is applied with learning and computational capability in fuzzy control. On the other hand, fuzzy control provides high level of knowledge and fuzzy rules for use in the neural network. Using a past experimental dataset, the developed ANFIS model is trained and subsequently tested to envisage yarn tenacity and unevenness values. Its prediction performance is validated with respect to five statistical metrics, i.e. correlation coefficient, mean absolute percentage error, root-mean-square error, coefficient of efficiency and variance performance index, and is also contrasted against other prediction tools, like multivariate regression analysis, artificial neural network, fuzzy logic and support vector machine. Based on their acceptable values, it can be concluded that the ANFIS models can be effectively employed for prediction of diverse yarn quality characteristics while treating fibre properties as the input variables.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. S. Vassiliadis, M. Rangoussi, A. Cay, C. Provatidis, Artificial Neural Networks and Their Applications in the Engineering of Fabrics. In: Woven Fabric Engineering (ed.) P. D. Dubrovski, 111–134 (2010)

  2. C. L. P. Hui, N. S. Fun, C. Ip, Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades. In: Artificial Neural Networks - Industrial and Control Engineering Applications, (ed.) K. Suzuki, 3–34 (2010)

  3. R. Chattopadhyay, Artificial Neural Networks in Yarn Property Modeling. In: Soft Computing in Textile Engineering (ed.) A. Majumder, Woodhead Publisher, UK, 105–125 (2011)

  4. A. Majumdar, P.K. Majumdar, B. Sarkar, Selecting cotton bales by spinning consistency index and micronaire using artificial neural networks. AUTEX Res. J. 4(1), 1–8 (2004)

    Google Scholar 

  5. A. Majumdar, P.K. Majumdar, B. Sarkar, Application of an adaptive neuro-fuzzy system for the prediction of cotton yarn strength from HVI fibre properties. J. Text. Inst. 96(1), 55–60 (2005)

    Article  Google Scholar 

  6. R. Chattopadhyay, Application of neural network in yarn manufacturer. Ind. J. Fibre Text. Res. 31(1), 160–169 (2006)

    Google Scholar 

  7. M.E. Üreyen, H. Kadoğlu, The prediction of cotton ring yarn properties from AFIS fibre properties by using linear regression models. Fibers Text. East. Euro. 15(4), 63–67 (2007)

    Google Scholar 

  8. A. Majumder, A. Ghosh, Yarn strength modeling using fuzzy expert system. J. Eng. Fibers Fabr. 3(4), 61–68 (2008)

    Google Scholar 

  9. A. Majumdar, Modeling of cotton yarn hairiness using adaptive neuro-fuzzy inference system. Ind. J. Fiber Text. Res. 35, 121–127 (2010)

    Google Scholar 

  10. A. Ghosh, P. Chatterjee, Prediction of cotton yarn properties using support vector machine. Fibers Polym. 11(1), 84–88 (2010)

    Article  Google Scholar 

  11. R. Furferi, M. Gelli, Yarn strength prediction: a practical model based on artificial neural networks. Adv. Mech. Eng. (2010). https://doi.org/10.1155/2010/640103

    Article  Google Scholar 

  12. Z-J, Lv, Q. Xiang, J-G. Yang, Application of genetic algorithm-support vector machine for prediction of spinning quality. In: Proc. of the World Congress on Engineering, London, 1–6 (2011)

  13. D. Nurwaha, X.H. Wang, Using intelligent control systems to predict textile yarn quality. Fibers Text. East. Euro. 20(1), 23–27 (2012)

    Google Scholar 

  14. D. Banerjee, A. Ghosh, S. Das, Yarn strength modelling using genetic fuzzy expert system. J. Inst. Eng. India Ser. E 93(2), 83–90 (2013)

    Article  Google Scholar 

  15. K.A.A. Abakar, C. Yu, The performance of data mining techniques in prediction of yarn quality. Int. J. Innov. Manag. Technol. 4(4), 390–393 (2013)

    Google Scholar 

  16. S. Das, A. Ghosh, A. Majumdar, D. Banerjee, Yarn engineering using hybrid artificial neural network-genetic algorithm model. Fibers Polym. 14(7), 1220–1226 (2013)

    Article  Google Scholar 

  17. A.R. Fallahpour, A.R. Moghassem, Yarn strength modelling using adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). J. Eng. Fibers Fabr. 8(4), 6–18 (2013)

    Google Scholar 

  18. İ Özkan, Y. Kuvvetli, P.D. Baykal, R. Erol, Comparison of the neural network model and linear regression model for predicting the intermingled yarn breaking strength and elongation. J. Text. Inst. 105(11), 1203–1211 (2014)

    Google Scholar 

  19. M. Dashti, V. Derhami, E. Ekhtiyari, Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms. J. AI Data Min. 2(1), 73–78 (2014)

    Google Scholar 

  20. H. Ghanmi, A. Ghith, F. Fayala, T. Benameur, Modeling ring spun yarn tensile properties using fuzzy inference system. Int. J. Appl. Res. Text. 4(2), 12–19 (2016)

    Google Scholar 

  21. S.A. Malik, A. Farooq, T. Gereke, C. Cherif, Prediction of blended yarn evenness and tensile properties by using artificial neural network and multiple regression. AUTEX Res. J. 16(2), 43–50 (2016)

    Article  Google Scholar 

  22. S. Chakraborty, P.P. Das, V. Kumar, A grey fuzzy logic approach for cotton fibre selection. J. Inst. Eng. India Ser. E 98(1), 1–9 (2017)

    Article  Google Scholar 

  23. E. Hadavandi, S. Mostafayi, P. Soltani, A grey wolf optimizer based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills. Appl. Soft Comput. J. 72, 1–13 (2018)

    Article  Google Scholar 

  24. A. El-Geiheini, S. ElKateb, M.R. Abd-Elhamied, Yarn tensile properties modeling using artificial intelligence. Alexandria Eng. J. (2020). https://doi.org/10.1016/j.aej.2020.07.049

    Article  Google Scholar 

  25. S.R. Jang, ANFIS: Adaptive-network-based fuzzy interference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)

    Article  Google Scholar 

  26. S.R. Jang, C.T. Sun, E. Mizutani, Neuro Fuzzy and Soft Computing (Prentice Hall, Hoboken, 1997)

    Google Scholar 

  27. Z.-C. Lin, C.-Y. Liu, Analysis and application of the adaptive neuro-fuzzy interference system in prediction of CMP machining parameters. Int. J. Comput. Appl. Technol. 17(2), 80–89 (2003)

    Article  Google Scholar 

  28. U. Çaydaş, A. Hasçalık, S. Ekici, An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst. Appl. 36, 6135–6139 (2009)

    Article  Google Scholar 

  29. R.B. Azhiri, R. Teimouri, M.G. Baboly, Z. Leseman, Application of Taguchi, ANFIS and grey relational analysis for studying, modeling and optimization of wire EDM process while using gaseous media. Int. J. Adv. Manuf. Technol. 71, 279–295 (2014)

    Article  Google Scholar 

Download references

Funding

There was no outside funding or grants received that assisted in this study.

Author information

Authors and Affiliations

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

    Partha Protim Das

  2. Department of Production Engineering, Jadavpur University, Kolkata, India

    Shankar Chakraborty

Authors
  1. Partha Protim Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shankar Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shankar Chakraborty.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Das, P.P., Chakraborty, S. Adaptive Neuro-fuzzy Inference System-based Modelling of Cotton Yarn Properties. J. Inst. Eng. India Ser. E 102, 257–272 (2021). https://doi.org/10.1007/s40034-021-00217-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40034-021-00217-1

Keywords

Search

Navigation