Skip to main content
Log in

Tool steel material selection using PROMETHEE II method

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In the recent century, tools for machining, forming, or other types of metalworking industries have consumed several tons of steel materials. Although the earliest tool steels were plain carbon steels, the modern tools contain different alloying elements, like tungsten, vanadium, molybdenum, manganese, and chromium, to provide the properties desired for their wide applications. The presence of a large number of such tool steel materials is a result of the fact that no single material combines the maximum wear resistance, toughness, machinability, safety in hardening, and non-deformability properties. Each tool steel material has its own mechanical and physical characteristics that help it to be suited for a particular application. Also for a specific application, more than one alternative tool steel material may exist, which makes it essential to select the most appropriate tool steel material with the desired properties to meet the manufacturer’s requirements. This paper considers a list of ten tool steel materials whose performances are evaluated based on nine selection criteria. Preference ranking organization method for enrichment evaluation (PROMETHEE II) is then applied to solve this tool steel material selection problem to obtain the full ranking of the considered material alternatives. Molybdenum-type high-speed steel (AISI M2) and tungsten base high-speed tool steel (AISI T1) are the two best choices. Alloy steel (AISI 4140) is the worst preferred tool steel material.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Bryson B (1997) Heat treatment, selection and application of tool steels. Hanser Gardner, Ohio

    Google Scholar 

  2. Kalpakjian S, Schmind RS (2004) Manufacturing engineering and technology. Pearson Education Inc., Singapore

    Google Scholar 

  3. Boyes WE (1989) Handbook of jig and fixture design. SME, Michigan

    Google Scholar 

  4. Wang M-JJ, Chang TC (1995) Tool steel materials selection under fuzzy environment. Fuzzy Sets Syst 72:263–270

    Article  Google Scholar 

  5. Chen SM (1997) A new method for tool steel materials selection under fuzzy environment. Fuzzy Sets Syst 97:265–274

    Google Scholar 

  6. Kumar S, Singh R (2007) A short note on an intelligent system for selection of materials for progressive die components. J Mater Process Technol 182:456–461

    Article  Google Scholar 

  7. Caiyuan L, Jianjun L, Jianyong W, Xiangzhi X (2001) HPRODIE: using feature modeling and feature mapping to speed up progressive die design. Int J Prod Res 39:4133–4151

    Article  MATH  Google Scholar 

  8. Towhidi N, Tavakkoli-Moghaddam R, Vahdat SE (2005) The use of fuzzy logic theory for selecting appropriate tool steels with price analysis. Iranian J Sci Technol Trans B Eng 29:559–567

    Google Scholar 

  9. Calıskan H, Kursuncu B, Kurbanoğlu C, Güven SH (2013) Material selection for the tool holder working under hard milling conditions using different multi criteria decision making methods. Mater Des 45:473–479. doi:10.1016/j.matdes.2012.09.042#doilink

    Article  Google Scholar 

  10. Shirai K, Murakami H (1985) Development of a CAD/CAM system for progressive dies. CIRP Ann 34:187–190

    Article  Google Scholar 

  11. Prasad YKDV, Somasundaram S (1992) CADDS: an automated die design system for sheet metal blanking. Comput Control Eng 3:185–191

    Article  Google Scholar 

  12. Huang K, Ismail HS, Hon KKB (1996) Automated design of progressive dies. Proc Inst Mech Eng B J Eng Manuf 210:367–376

    Article  Google Scholar 

  13. Ismail HS, Hon KKB, Huang K (1993) CAPTD: a low-cost integrated computer aided design system for press tool design. Proc Inst Mech Eng B J Eng Manuf 207:117–127

    Article  Google Scholar 

  14. Cheok BT, Foong KY, Nee AYC, Teng CH (1994) Some aspects of a knowledge-based approach for automating progressive metal stamping die design. Comput Ind 24:81–96

    Article  Google Scholar 

  15. Kumar S (2011) An intelligent system for selection of materials for press tool components. J Eng Res Stud 2:119–130

    Google Scholar 

  16. Doumpos M, Zopounidis C (2004) A multi-criteria classification approach based on pair-wise comparison. Eur J Oper Res 158:378–389

    Article  MATH  MathSciNet  Google Scholar 

  17. Silva V, Morais D, Almeida A (2010) A multicriteria group decision model to support watershed committees in Brazil. Water Resour Manag 24:1–17

    Article  Google Scholar 

  18. Doumpos M, Zopounidis C (2010) A multicriteria decision support system for bank rating. Decis Support Syst 24:4075–4091

    Google Scholar 

  19. Luk J, Fernandes H, Kumar A (2010) A conceptual framework for siting biorefineries in the Canadian Prairies. Biofuels Bioprod Bioref 4:408–422

    Article  Google Scholar 

  20. Rao RV, Patel B (2010) Decision making in the manufacturing environment using an improved PROMETHEE method. Int J Prod Res 48:4665–4682

    Article  MATH  Google Scholar 

  21. Roy B, Bouyssou D (1993) Multi-criteria decision: methods and cases. Economica, Paris

    Google Scholar 

  22. Brans JP, Mareschal B, Vincke P (1984) PROMETHEE: A new family of outranking methods in multicriteria analysis. In: Brans JP (ed) Operational Research IFORS 84, 1st edn. North Holland, Amsterdam, pp 477–490

    Google Scholar 

  23. Brans JP, Vincke P, Mareschal B (1986) How to select and how to rank projects: the PROMETHEE method. Eur J Oper Res 24:228–238

    Article  MATH  MathSciNet  Google Scholar 

  24. Brans JP, Vincke P (1985) A preference ranking organisation method: the PROMETHEE method for MCDM. Manag Sci 31:647–656

    Article  MATH  MathSciNet  Google Scholar 

  25. Hajkowicz S, Higgins A (2008) A comparison of multiple criteria analysis techniques for water resource management. Eur J Oper Res 184:255–265

    Article  MATH  Google Scholar 

  26. Chen T, Wang YC, Tsai HR (2009) Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM-FBPN-ensemble approach with multiple buckets and partial normalization. Int J Adv Manuf Technol 42:1206–1216

    Article  Google Scholar 

  27. Davis JR (1996) ASM specialty handbook —carbon and alloy steels. American Society for Metals, Ohio

    Google Scholar 

  28. Boyer HE, Gall TL (1985) Metals handbook. American Society for Metals, Ohio

    Google Scholar 

  29. SAE ferrous materials standards manual (1999). Society of Automotive Engineers Inc., Pennsylvania

  30. The Sousa Corp ‘Technical’ (2012) Tool steel properties. http://www.sousacorp.com/tool1.htm# HIGH SPEED TOOL STEELS. Accessed on 30 November, 2012

  31. Simply Tool Steel ‘Data Sheets’ (2011) http://www.simplytoolsteel.com/CPM-3V-tool-steel-data-sheet.html. Accessed on 30 November, 2012

  32. Rao RV (2007) Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer, London

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

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

Maity, S.R., Chakraborty, S. Tool steel material selection using PROMETHEE II method. Int J Adv Manuf Technol 78, 1537–1547 (2015). https://doi.org/10.1007/s00170-014-6760-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6760-0

Keywords

Navigation