Machinability evaluation of work materials using a combined multiple attribute decision-making method

  • R. Venkata RaoEmail author
Original Article


This paper presents a logical procedure to evaluate the machinability of work materials for a given machining operation. The procedure is based on a combined TOPSIS and AHP method. The proposed global machinability index helps to evaluate and rank the work materials for a given machining operation. Two examples are included to illustrate the approach.


Machinability evaluation  Multiple attribute decision-making  


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hindustan Machine Tools (1980) Production technology. Tata – McGraw-Hill, New DelhiGoogle Scholar
  2. 2.
    Mills B, Redford AH (1983) Machinability of engineering materials. Applied Science, LondonGoogle Scholar
  3. 3.
    Trent EM (1991) Metal cutting. Butterworth-Heinemann, LondonGoogle Scholar
  4. 4.
    Rao RV (2002) Machinability evaluation, cutting fluid selection and failure analysis of machine tools using a unified graph theory and matrix approach. Dissertation, BITS Pilani, Rajasthan, IndiaGoogle Scholar
  5. 5.
    Ostafev VA, Mirzaev AA, Kokarovtsev VV (1989) Fast method for determining machinability of materials. Soviet Eng Res 9(8):113–114Google Scholar
  6. 6.
    Notoya H, Yamada S, Yoshikawa K, Takatsuji Y (1990) Effects of tool materials on machinability of commercially pure titanium. J Japan Inst Metals 54(5):596–602Google Scholar
  7. 7.
    Eyada OK (1992) Reliability of cutting forces in machinability evaluation. Flexible Automat Inf Manage 20:937–946Google Scholar
  8. 8.
    Hung NP, Boey FYC, Khor KA, Oh CA, Lee HF (1995) Machinability of cast and powder-formed aluminium alloys reinforced with SiC particles. J Mater Process Technol 48(1):291–297CrossRefGoogle Scholar
  9. 9.
    Dravid SV, Utpat LS (2001) Machinability evaluation based on the surface finish criterion. J Inst Eng (India) Product Eng Division 81(2):47–51Google Scholar
  10. 10.
    Jin LZ, Sandstrom R (1994) Evaluation of machinability data. J Testing Eval 22:204–210CrossRefGoogle Scholar
  11. 11.
    Yoshikawa T, Miyazawa S, Mori K (1994) Machinability of Ni3Al-based intermetallic compounds. J Mech Eng Lab 48(4):190–196Google Scholar
  12. 12.
    Hartung PD, Kramer BM (1982) Tool wear in titanium machining. Ann CIRP 31(1):75–80Google Scholar
  13. 13.
    Strafford KN, Audy J (1997) Indirect monitoring of machinability in carbon steels by measurement of cutting forces. J Mater Process Technol 67(1–3):150–156Google Scholar
  14. 14.
    Konig W, Erinski D (1983) Machining and machinability of aluminium cast alloys. Ann CIRP 32(2):535–540Google Scholar
  15. 15.
    Bech HG (1963) Untersuchung der Zerspanbarkeit von Leichtmetallgulegierungen. Dissertation, RWTH, AachenGoogle Scholar
  16. 16.
    Ezugwu EO, Wang ZM, Machado AR (1999) The machinability of nickel based alloys: a review. J Mater Process Technol 86:1–16CrossRefGoogle Scholar
  17. 17.
    Choudhury A, El Baradie MA (1998) Machinability of nickel based super alloys: a general review. J Mater Process Technol 77(1–3):278–284Google Scholar
  18. 18.
    Ezugwu EO, Bonney J, Yamane Y (2003) An overview of the machinability of aeroengine alloys. J Mater Process Technol 134:233–253CrossRefGoogle Scholar
  19. 19.
    Gupta SK, Dana SN (1995) Systematic approach to analyzing the manufacturability of machined parts. Comput Aided Des J 27:323–342CrossRefzbMATHGoogle Scholar
  20. 20.
    Sandstrom R, Grahn B (1986) The assessment and evaluation of property data for materials selection purposes. Mater Des J 7:198–204Google Scholar
  21. 21.
    Leisk G, Saigal A (1992) Machinabiliy of alumina/aluminum metal matrix composites. In: Proceedings of the International Symposium on Advances in Production and Fabrication of Light Metals and Metal Matrix Composites, Edmonton, Alberta, Canada, pp 673–687Google Scholar
  22. 22.
    Hamann JC, Grolleau V, Maitre FL (1996) Machinability improvement of steels at high cutting speeds – study of tool/work material interaction. Ann CIRP 45:87–92Google Scholar
  23. 23.
    Hong H, Riga AT, Cahoon JM, Scott CG (1993) Machinability of steels and titanium alloys under lubrication. Wear 162:34–39CrossRefGoogle Scholar
  24. 24.
    Carpenter ID (1996) Machinability assessment and tool selection for milling. Dissertation, University of Durham, DurhamGoogle Scholar
  25. 25.
    Seo Y, Ramulu M, Kim D (2003) Machinability of Ti alloys on the abrasive waterjet machining process. In: Proceedings of the Institution of Mechanical Engineers, Part B J Eng Manuf 217:1709–1721Google Scholar
  26. 26.
    Davim JP, Reis P (2004) Machinability study on composite (polyetheretherketone reinforced with 30% glass fibre-PEEK OKGF 30) using polycrystalline diamond (PCD) and cemented carbide (K20) tools. Int J Adv Manuf Technol 23(5–6):412–418Google Scholar
  27. 27.
    Malakooti B, Wang J, Tandler EC (1990) Sensor based accelerated approach for multi-attribute machinability and tool life evaluation. Int J Prod Res 28:2373–2392Google Scholar
  28. 28.
    Enache S, Strajescu E, Opran C, Minciu C, Zamfirache M (1995) Mathematical model for the establishment of the materials machinability. Ann CIRP 44:79–82CrossRefGoogle Scholar
  29. 29.
    Ong SK, Chew LC (2000) Evaluating the manufacturability of machined parts and their setup plans. Int J Prod Res 38(11):2397–2415CrossRefzbMATHGoogle Scholar
  30. 30.
    Rao RV, Gandhi OP (2002) Digraph and matrix methods for the machinability evaluation of work materials. Int J Mach Tools Manuf 42(3):320–330Google Scholar
  31. 31.
    Hwang CL, Yoon K (1982) Multiple attribute decision-making – methods and applications – a state of art survey. Springer, Berlin Heidelberg New YorkGoogle Scholar
  32. 32.
    Yoon YP, Hwang CL (1995) Multiple attribute decision-making. SAGE Publications, Beverly Hills, CAGoogle Scholar
  33. 33.
    Saaty TL (1980) Analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
  34. 34.
    Saaty TL (1994) Fundamentals of decision-making and priority theory with AHP. RWS, Pittsburgh, PAGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringKumaraguru College of TechnologyCoimbatoreIndia

Personalised recommendations