A brief review of grey fuzzy logic technique research progression from 2010 to 2016

  • Authors

    • Suhaila Yacob
    • Mohd Amran Md Ali
    • Qumrul Ahsan
    • Nurulhuda Arifin
    • Rafidah Ali
    2018-05-22
    https://doi.org/10.14419/ijet.v7i2.29.13125
  • Grey fuzzy logic, welding parameter, multiple optimizations.

  • Grey fuzzy logic method is a new artificial intelligent method that   can make significant improvement in the performance characteristics of the process. In the present study an attempt has been made to provide a brief understanding of the advancement of the Grey Fuzzy Logic from 2010 to 2016. The first half of this paper presents the publication trend of Grey Fuzzy Logic. The remaining of this paper briefly explains the contribution of the individual publication related to Grey Fuzzy Logic. It is believed that this paper will greatly benefit the reader who needs a bird-eyes view of the Grey Fuzzy Logic ’s publications trend.

     

  • References

    1. 1. KaraoÄŸlu S, Secgin A. Sensitivity analysis of submerged arc welding process parameters. journal of materials processing technology. 2008;202(1-3):500-7.

      2. Dhas ERJ, Satheesh M. Multiple objective optimization of submerged arc welding process parameters using grey based fuzzy logic. Advances in Production Engineering & Management. 2012;7(1):5.

      3. Yang Y-S, Huang W. A grey-fuzzy Taguchi approach for optimizing multi-objective properties of zirconium-containing diamond-like carbon coatings. Expert Systems with Applications. 2012;39(1):743-50.

      4. Krishnamoorthy A, Boopathy SR, Palanikumar K, Davim JP. Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement. 2012;45(5):1286-96.

      5. Tamang S, Chandrasekaran M, editors. Application of grey fuzzy logic for simultaneous optimization of surface roughness and metal removal rate in turning Al-SiCp metal matrix composites. 5th International & 26th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014); 2014.

      6. Hanafi I, Khamlichi A, Cabrera FM, Fuentes D, Jabbouri A. Grey-fuzzy optimisation model for multi performance in CNC turning processes. International Journal of Computational Systems Engineering. 2012;1(2):108-17.

      7. Tamiloli N, Venkatesan J, Ramnath BV. A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert. Measurement. 2016;84:68-82.

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  • How to Cite

    Yacob, S., Amran Md Ali, M., Ahsan, Q., Arifin, N., & Ali, R. (2018). A brief review of grey fuzzy logic technique research progression from 2010 to 2016. International Journal of Engineering & Technology, 7(2.29), 41-42. https://doi.org/10.14419/ijet.v7i2.29.13125