Selection of non-conventional machining processes using the OCRA method

  • Miloš Madić Faculty of Mechanical Engineering University of Nis
  • Dušan Petković Faculty of Mechanical Engineering University of Nis
  • Miroslav Radovanović Faculty of Mechanical Engineering University of Nis

Abstract


Selection of the most suitable nonconventional machining process (NCMP) for a given machining application can be viewed as multi-criteria decision making problem with many conflicting and diverse criteria. To aid these selection processes, different multi-criteria decision making (MCDM) methods have been proposed. This paper introduces the use of an almost unexplored MCDM method, i.e. operational competitiveness ratings analysis (OCRA) method for solving the NCMP selection problems. Applicability, suitability and computational procedure of OCRA method have been demonstrated while solving three case studies dealing with selection of the most suitable NCMP. In each case study the obtained rankings were compared with those derived by the past researchers using different MCDM methods. The results obtained using the OCRA method have good correlation with those derived by the past researchers which validate the usefulness of this method while solving complex NCMP selection problems.

Author Biographies

Miloš Madić, Faculty of Mechanical Engineering University of Nis

Department of Production Engineering

Research Assistant

Dušan Petković, Faculty of Mechanical Engineering University of Nis

Department of Production Engineering

Teaching Assistant

Miroslav Radovanović, Faculty of Mechanical Engineering University of Nis

Department of Production Engineering

Full Professor

References

Chakladar, N.D., & Chakraborty, S. (2008). A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222 (12), 1613-1623.

Chakladar, N.D., Das, R., & Chakraborty, S. (2009). A digraph-based expert system for non-traditional machining processes selection. International Journal of Advanced Manufacturing Technology, 43 (3-4), 226-237.

Chakraborty, S., & Dey, S. (2006). Design of an analytic-hierarchy-process-based expert system for non-traditional machining process selection. International Journal of Advanced Manufacturing Technology, 31 (5-6), 490-500.

Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. International Journal of Advanced Manufacturing Technology, 54 (9-12), 1155-1166.

Chatterjee, P., & Chakraborty, S. (2012). Material selection using preferential ranking methods. Materials and Design, 35 (1), 384-393.

Chatterjee, P., & Chakraborty, S. (2013). Nontraditional machining processes selection using evaluation of mixed data method. International Journal of Advanced Manufacturing Technology, 68 (5-8), 1613-1626.

Das, S., & Chakraborty, S. (2011). Selection of non-traditional machining processes using analytic network process. Journal of Manufacturing Systems, 30 (1), 41-53.

Hajkowicz, S., & Higgins, A. (2008). A comparison of multiple criteria analysis techniques for water resource management. European Journal of Operational Research, 184 (1), 255-265.

Kalpakjian, S., & Schmid, S.R. (2000). Manufacturing engineering and technology. New York, Prentice Hall.

Karande, P., & Chakraborty, S. (2012). Application of PROMETHEE-GAIA method for non-traditional machining processes selection. Management Science Letters, 2 (6), 2049-2060.

Kovačević, M., Madić, M., Radovanović, M., & Rančić, D. (2014). Software prototype for solving multi-objective machining optimization problems: application in non-conventional machining processes. Expert Systems with Applications, 41 (13), 5657-5668.

Parkan, C. (1991). The calculation of operational performance ratings. International Journal of Production Economics, 24 (1), 165-173.

Parkan, C., &, Wu, M.L. (1997). On the equivalence of operational performance measurement and multiple attribute decision making. International Journal of Production Research, 35 (11), 2963-88.

Parkan, C., &, Wu, M.L. (2000). Comparison of three modern multi criteria decision-making tools. International Journal of Systems Science, 31 (4), 497-517.

Prasad, K., & Chakraborty, S. (2014). A decision-making model for non-traditional machining processes selection. Decision Science Letters, 3 (4), 467–478.

Roy, M.K., Ray, A., & Pradhan, B.B. (2014). Non-traditional machining process selection using integrated fuzzy AHP and QFD techniques: a customer perspective. Production & Manufacturing Research, 2 (1), 530-549.

Sadhu, A., & Chakraborty, S. (2011). Non-traditional machining processes selection using data envelopment analysis (DEA). Expert Systems with Applications, 38 (7), 8770-8781.

Samanta, S., & Chakraborty, S. (2011). Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Engineering Applications of Artificial Intelligence, 24 (6), 946-957.

Stanujkić, D., Đorđević, B., & Đorđević, M. (2013). Comparative analysis of some prominent MCDM methods: a case of ranking Serbian banks. Serbian Journal of Management, 8 (2), 213-241.

Temuçin, T., Tozan, H., Valíček, J., & Harničárová, M. (2013). A fuzzy based decision support model for non-traditional machining process selection. Technical Gazette, 20 (5), 787-793.

Venkata Rao, R. (2007). Decision Making in the Manufacturing Environment: using graph theory and fuzzy multiple attribute decision making methods. Springer-Verlag.

Yurdakul, M., & Cogun, C. (2003). Development of a multi-attribute selection procedure for nontraditional machining processes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217 (7), 993-1009.

Published
2014/09/26
Section
Original Scientific Paper