Volume : 5, Issue : 8, AUG 2019

OPTIMIZATION OF GREEN ELECTRO-DISCHARGE MACHINING USING VIKOR

BRIJESH PATEL, NARENDRA JAISWAL

Abstract

In the present study an efficient Multi-Criteria Decision Making (MCDM) approach has been proposed for optimization of green electro-discharge machining, because it is a commonly used non-traditional machining process. Green electro-discharge machining is a Multi-Criteria Decision Making (MCDM) problem influenced by multiple performance criteria/attributes. These criteria attributes are of two types, qualitative and quantitative. Qualitative criteria estimates are generally based on previous experience and expert opinion on a suitable conversion scale. This conversion is based on human judgment; therefore, obtained result may not be accurate always. These are analyzed using AHP, QFD, Fuzzy techniques etc. reported in literature. So to find the solution of MCDM problems there should be converted quantitative criteria values into an equivalent single performance index called Multi-attribute Performance Index (MPI). Selection of the best alternative can be made in accordance with the MPI values of all the alternatives. In this text, present study highlights application of VIKOR method adapted from MCDM techniques for obtaining the accurate result. Detail methodology of VIKOR method has been illustrated in this report through a case study.

Keywords

DECISION- MAKING METHODS, VIKOR METHOD, ELECTRO-DISCHARGE MACHINING, OPTIMIZATION PROCEDURE.

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References

1. Samantra, C. (2012), Decision-making In fuzzy environment, M. Tech thesis, NIT Rourkela. 2. Rao, R. (2006), Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods, Springer, New Mexico. 3. Abbas, N. M., Solomon, D. G., and Bahari, M. F. (2007), A review on current research trends in electrical discharge machining (EDM), International journal of machine tools & manufacture, Vol. 47, pp. 1214–1228. 4. Tong, L. I., Chen, C. C. and Wang, C. H. (2007), Optimization of multi-response processes using the VIKOR method, International journal of advanced manufacturing technology, Vol. 31, pp. 1049-1057. 5. Derringer, G. C. and Suich, R. (1980), Simultaneous optimization of several response variables, Journal of quality technology, Vol. 12, pp. 214-219. 6. Khuri, A. I. and Conlon, M. (1981), Simultaneous optimization of multiple responses represented by polynomial regression functions, American statistical association and american society for quality, Vol. 23, pp. 367-35. 7. Logothetis, N. and Haigh, A. (1988), Characterizing and optimizing multi-response processes by the taguchi method, Quality and reliability engineering international, Vol. 4, pp. 159-169. 8. Phadke, M. S. (1989), Quality engineering using robust design. Prentice-Hall. 9. Bortolan, G. and Degani, R. (1985), A review of some methods for ranking fuzzy subsets, Fuzzy sets and systems, Vol. 15, pp. 1–19.