Skip to main content
SpringerLink
Log in

Decision making for material selection using the UTA method

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

Abstract

The choice of materials plays an important role in the decision-making process of the manufacturing organizations. The materials affect many aspects of a product and the manufacturing process too. The improper selection of material can result in a defective final product. Thus, if satisfactory results are to be expected, immense importance must be given for proper selection of the materials. There are numerous choices and various criteria influencing the selection of material for a particular application. These criteria range from mechanical, electrical, and physical properties to corrosion resistance and economic considerations of the materials. The large number of available materials, together with the complex relationships between various selection parameters, often makes the selection process a difficult task. The problem of selecting a material for an engineering application from among two or more alternatives on the basis of several criteria can be treated as a multi-criteria decision-making (MCDM) problem. This paper mainly focuses on solving two real-time material selection problems using utility additive (UTA) method, which is an almost unexplored MCDM tool to solve such type of complex decision-making problems. It is observed that the ranking performance of the UTA method is quite comparable with the other MCDM methods as adopted by the past researchers.

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

Similar content being viewed by others

References

  1. Edwards KL (2005) Selecting materials for optimum use in engineering components. Mater Des 26:469–472

    Article  Google Scholar 

  2. Deng Y-M, Edwards KL (2007) The role of materials identification and selection in engineering design. Mater Des 28:131–139

    Article  Google Scholar 

  3. Jee D-H, Kang K-J (2000) A method for optimal material selection aided with decision making theory. Mater Des 21:199–206

    Article  Google Scholar 

  4. Khabbaz RS, Manshadi BD, Abedian A, Mahmudi R (2009) A simplified fuzzy logic approach for materials selection in mechanical engineering design. Mater Des 30:687–697

    Article  Google Scholar 

  5. Shanian A, Savadogo O (2006) TOPSIS multiple-criteria decision support analysis for material selection of metallic bipolar plates for polymer electrolyte fuel cell. J Power Sources 159:1095–1104

    Article  Google Scholar 

  6. Shanian A, Savadogo O (2009) A methodological concept for material selection of highly sensitive components based on multiple criteria decision analysis. Expert Syst Appl 36:1362–1370

    Article  Google Scholar 

  7. Rao RV (2006) A material selection model using graph theory and matrix approach. Mater Sc and Engg A 431:248–255

    Article  Google Scholar 

  8. Rao RV (2008) A decision making methodology for material selection using an improved compromise ranking method. Mater Des 29:1949–1954

    Article  Google Scholar 

  9. Chan JWK, Tong TKL (2007) Multi-criteria material selections and end-of-life product strategy: grey relational analysis approach. Mater Des 28:1539–1546

    Article  Google Scholar 

  10. Dehghan-Manshadi B, Mahmudi H, Abedian A, Mahmudi R (2007) A novel method for materials selection in mechanical design: combination of non-linear linearization and a modified digital logic method. Mater Des 28:8–15

    Article  Google Scholar 

  11. Sharif Ullah AMM, Harib KH (2008) An intelligent method for selecting optimal materials and its application. Advanced Engg Informatics 22:473–483

    Article  Google Scholar 

  12. Thakker A, Jarvis J, Buggy M, Sahed A (2008) A novel approach to materials selection strategy case study: wave energy extraction impulse turbine blade. Mater Des 29:1973–1980

    Article  Google Scholar 

  13. Rao RV, Davim JP (2008) A decision-making framework model for material selection using a combined multiple attribute decision-making method. Int J Adv Manuf Tech 35:751–760

    Article  Google Scholar 

  14. Zhang X-J, Chen K-Z, Feng X-A (2008) Material selection using an improved genetic algorithm for material design of components made of a multiphase material. Mater Des 29:972–981

    Article  Google Scholar 

  15. Zhou C-C, Yi G-F, Hu X-B (2009) Multi-objective optimization of material selection for sustainable products: artificial neural networks and genetic algorithm approach. Mater Des 30:1209–1215

    Article  Google Scholar 

  16. Chatterjee P, Athawale VM, Chakraborty S (2009) Selection of materials using compromise ranking and outranking methods. Mater Des 30:4043–4053

    Article  Google Scholar 

  17. Huang H, Liu Z, Zhang L, Sutherland JW (2009) Materials selection for environmentally conscious design via a proposed life cycle environmental performance index. Int J Adv Manuf Tech 44:1073–1082

    Article  Google Scholar 

  18. Athanasopoulos G, Riba CR, Athanasopoulou C (2009) A decision support system for coating selection based on fuzzy logic and multi-criteria decision making. Expert Syst Appl 36:10848–10853

    Article  Google Scholar 

  19. Jahan A, Ismail MY, Sapuan SM, Mustapha F (2010) Material screening and choosing methods—a review. Mater Des 31:696–705

    Article  Google Scholar 

  20. Maniya K, Bhatt MG (2010) A selection of material using a novel type decision-making method: preference selection index method. Mater Des 31:1785–1789

    Article  Google Scholar 

  21. Jahan A, Ismail MY, Mustapha F, Sapuan SM (2010) Material selection based on ordinal data. Mater Des 31:3180–3187

    Article  Google Scholar 

  22. Rao RV, Patel BK (2010) A subjective and objective integrated multiple attribute decision making method for material selection. Mater Des 31:4738–4747

    Article  Google Scholar 

  23. Cicek K, Celik M (2010) Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm. Mater Des 31:2129–2133

    Article  Google Scholar 

  24. Chatterjee P, Athawale VM, Chakraborty S (2011) Materials selection using complex proportional assessment and evaluation of mixed data methods. Mater Des 32:851–860

    Article  Google Scholar 

  25. Gupta N (2011) Material selection for thin-film solar cells using multiple attribute decision making approach. Mater Des 32:1667–1671

    Article  Google Scholar 

  26. Jahan A, Mustapha F, Ismail MY, Sapuan SM, Bahraminasab M (2011) A comprehensive VIKOR method for material selection. Mater Des 32:1215–1221

    Article  Google Scholar 

  27. Huang H, Zhang L, Liu Z, Sutherland JW (2011) Multi-criteria decision making and uncertainty analysis for materials selection in environmentally conscious design. Int J Adv Manuf Tech 52:421–432

    Article  Google Scholar 

  28. Jacquet-Lagreze E, Siskos J (1982) Assessing a set of additive utility functions for multi-criteria decision-making, the UTA method. Eur J Oper Res 10:151–164

    Article  MATH  Google Scholar 

  29. Figueira J, Greco S, Ehrgott M (2005) Multiple criteria decision analysis: state of the art surveys. Springer-Verlag, Bostan

    MATH  Google Scholar 

  30. Hatush Z, Skitmore M (1998) Contractor selection using multicriteria utility theory: an additive model. Build Environ 33:105–115

    Article  Google Scholar 

  31. Beuthe M, Scannella G (2001) Comparative analysis of UTA multicriteria methods. Eur J Oper Res 130:246–262

    Article  MATH  Google Scholar 

  32. Köksalana M, Özpeynirci SB (2009) An interactive sorting method for additive utility functions. Comp & Oper Res 36:2565–2572

    Article  Google Scholar 

  33. Chien T-L, Chen C-C, Huang Y-C, Lin W-J (2008) Stability and almost disturbance decoupling analysis of nonlinear system subject to feedback linearization and feed forward neural network controller. IEEE Trans Neural Netw 19:1220–1230

    Article  Google Scholar 

  34. Chen C-W, Yeh K, Liu KF-R (2009) Adaptive fuzzy sliding mode control for seismically excited bridges with lead rubber bearing isolation. Int J Uncertain Fuzziness Knowl -based Syst 17:705–727

    Article  MATH  Google Scholar 

  35. Chen C-Y, Lin J-W, Lee W-J, Chen C-W (2010) Fuzzy control for an oceanic structure: a case study in time-delay TLP system. J Vib Control 16:147–160

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Government Polytechnic, Amravati, 444 603, India

    Vijay Manikrao Athawale

  2. Department of Production Engineering, Jadavpur University, Kolkata, 700 032, West Bengal, India

    Rajanikar Kumar & Shankar Chakraborty

Authors
  1. Vijay Manikrao Athawale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajanikar Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shankar Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shankar Chakraborty.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Athawale, V.M., Kumar, R. & Chakraborty, S. Decision making for material selection using the UTA method. Int J Adv Manuf Technol 57, 11–22 (2011). https://doi.org/10.1007/s00170-011-3293-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-011-3293-7

Keywords

Search

Navigation