Advertisement

Development of an intuitionistic fuzzy ranking model for nontraditional machining processes

  • Mustafa Yurdakul
  • Yusuf Tansel İçEmail author
  • Kumru Didem Atalay
Methodologies and Application
  • 15 Downloads

Abstract

Nontraditional machining processes (NTMPs) are capable of processing very small parts, producing intricate geometries, operating on very narrow machining areas and machining high strength materials. These capabilities lead to a very diverse and large application area for NTMPs. Such a diverse and large application area along with more than one hundred NTMPs requires development of systematic and comprehensive models to help manufacturing engineers in their NTMP selection decisions. Furthermore, fuzzy models instead of crisp ones are being used in the literature in recent years to represent preferences of decision makers more realistically. This study proposes intuitionistic and triangular fuzzy NTMP ranking models and compares their ranking results with the crisp ranking model. The comparisons show that there are statistically significant differences among all three ranking models’ NTMP ranking results.

Keywords

Nontraditional machining approaches Multi-criteria decision making (MCDM) Intuitionistic fuzzy NTMP ranking model Triangular fuzzy NTMP ranking model 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal participants

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abu Arqub O (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28:1591–1610CrossRefGoogle Scholar
  2. Abu Arqub O, Al-Smadi M, Momani S, Hayat T (2016) Numerical solutions of fuzzy differential equations using reproducing kernel Hilbert space method. Soft Comput 20:3283–3302CrossRefGoogle Scholar
  3. Abu Arqub O, Al-Smadi M, Momani S, Hayat T (2017) Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems. Soft Comput 21:7191–7206CrossRefGoogle Scholar
  4. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96CrossRefGoogle Scholar
  5. Chakladar N, Chakraborty S (2008) A combined TOPSIS-AHP method based approach for non-traditional machining processes selection. Proc Inst Mech E J Eng Manuf 222:1613–1623CrossRefGoogle Scholar
  6. Chakroborty S, Dey S (2007) QFD-based expert system for non-traditional machining processes selection. Exp Syst Appl 32:1208–1217CrossRefGoogle Scholar
  7. Chandrasselan ER, Jehadeesan R, Raajenthiren M (2011) A knowledge base for non-traditional machining process selection. Int J Technol Knowl Soc 4:37–46CrossRefGoogle Scholar
  8. Chang DY (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655MathSciNetCrossRefGoogle Scholar
  9. Chen S-J, Hwang C-L (1992) Fuzzy multiple attribute decision making. Springer, BerlinCrossRefGoogle Scholar
  10. Chen TY, Tsao CY (2008) The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets Syst 159(11):1410–1428MathSciNetCrossRefGoogle Scholar
  11. Cogun C (1993) Computer-aided system for selection of nontraditional machining operations. Comput Ind 22:169–179CrossRefGoogle Scholar
  12. Cogun C (1994) Computer aided preliminary selection of non-traditional machining processes. Int J Mach Tools Manuf 34:315–326CrossRefGoogle Scholar
  13. Das S, Chakraborty S (2011) Selection of non-traditional machining processes using analytic network process. J Manuf Syst 30(1):41–53CrossRefGoogle Scholar
  14. Duran O, Aguilo J (2008) Computer-aided machine-tool selection based on a fuzzy-AHP approach. Expert Syst Appl 34:1787–1794CrossRefGoogle Scholar
  15. Franceschini F, Galetto M (2001) A new approach for evaluation of risk priorities of failure modes in FMEA. Int J Prod Res 39(13):2991–3002CrossRefGoogle Scholar
  16. Glasser GJ, Winter RF (1961) Critical values of the coefficient of rank correlation for testing the hypothesis of independence. Biometrika 48(3/4):444–448CrossRefGoogle Scholar
  17. Groover MP (2008) Automation production systems and computer–integrated manufacturing, 3rd edn. Prentice Hall, New YorkGoogle Scholar
  18. Ic YT, Yurdakul M, Dengiz B (2013) Development of a decision support system for robot selection. Robot Comput Integr Manuf 29:142–157CrossRefGoogle Scholar
  19. Jafarian M, Vahdat SE (2012) A fuzzy multi-attribute approach to select the welding process at high pressure vessel manufacturing. J Manuf Process 14(3):250–256CrossRefGoogle Scholar
  20. Karpak B, Zionts S (1987) Multiple criteria decision making and risk analysis using microcomputers. Springer, BerlinzbMATHGoogle Scholar
  21. Kul Y, Şeker A, Yurdakul M (2014) Usage of fuzzy multi criteria decision making methods in selection of nontraditional manufacturing methods. J Fac Eng Archit Gazi Univ 29(3):589–603 (In Turkish) Google Scholar
  22. Parkan C, Wu M-L (1999) Decision making and performance measurement models with applications to robot selection. Comput Ind Eng 36:503–523CrossRefGoogle Scholar
  23. Prasad K, Chakraborty S (2018) A decision guidance framework for non-raditional machining processes selection. Ain Shams Eng J 9:203–214CrossRefGoogle Scholar
  24. Sadhu A, Chakraborty S (2007) Non-traditional machining processes selection using data envelopment analysis (DEA). Expert Syst Appl 38:8770–8781CrossRefGoogle Scholar
  25. Sáenz DC, Castillo NG, Romeva CR, Macià JL (2015) A fuzzy approach for the selection of non-traditional sheet metal cutting processes. Expert Syst Appl 42:6147–6154CrossRefGoogle Scholar
  26. Siegel S, Castellan NJ (1956) Nonparametric statistics for the behavioral sciences, vol 7. McGraw-hill, New York, pp 202–204Google Scholar
  27. Tabucanon MT (1988) Multiple criteria decision making in industry. Elsevier, USAGoogle Scholar
  28. Torfi F, Farahani RZ, Rezapour S (2011) Fuzzy AHP to determine the relative weights of evaluation attributes and fuzzy TOPSIS to rank the alternatives. Appl Soft Comput 10:520–528CrossRefGoogle Scholar
  29. Yurdakul M, Cogun C (2003) Development of a multi-attribute selection procedure for non-traditional machining processes. Proc Inst Mech Eng J Eng Manuf 217:993–1009CrossRefGoogle Scholar
  30. Yurdakul M, Ic YT (2005) Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches. Int J Prod Res 43(21):4609–4641CrossRefGoogle Scholar
  31. Yurdakul M, Ic YT (2009) Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. J Mater Process Technol 209(1):310–317CrossRefGoogle Scholar
  32. Yurdakul M, Ic YT (2019) Comparison of fuzzy and crisp versions of an AHP and TOPSIS model for nontraditional manufacturing process ranking decision. J Adv Manuf Syst 19(2):1–28Google Scholar
  33. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mustafa Yurdakul
    • 1
  • Yusuf Tansel İç
    • 2
    Email author
  • Kumru Didem Atalay
    • 2
  1. 1.Department of Mechanical EngineeringGazi UniversityMaltepeTurkey
  2. 2.Department of Industrial EngineeringBaskent UniversityEtimesgutTurkey

Personalised recommendations