Skip to main content
SpringerLink
Log in

Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS

  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

The Technique for order preference by similarity to ideal solution (TOPSIS) method of optimization is used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics. The Taguchi method of experimental design L27 is performed to obtain the optimal parameters for inputs, including feed rate, current, pulse on time, and gap voltage. Several output responses, such as the material removal rate, electrode wear rate, overcut, taper angle, and circularity at entry and exit points, are analyzed for the optimal conditions. Among all the investigated parameters, feed rate exerts a greater influence on the hole quality. ANOVA is employed to identify the contribution of each experiment. The optimal level of parameter setting is maintained at a feed rate of 4 μm/s, a current of 10 A, a pulse on time of 10 μs, and a gap voltage of 10 V. Scanning electron microscope analysis is conducted to examine the hole quality. The experimental results indicate that the optimal level of the process parameter setting over the overall performance of the micro-EDM is improved through TOPSIS.

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. H. Tong, Y. Li and L. Zhang, Mechanism design and process control of micro EDM for drilling spray holes of diesel injector nozzles, Precision Engineering, 37 (1) (2013) 213–221.

    Article  Google Scholar 

  2. P. Tiwary, B. Pradhan and B. Bhattacharyya, Application of multi-criteria decision making methods for selection of micro-EDM process parameters, International Journal of Advanced Manufacturing Technology, 2 (2014) 251–258.

    Google Scholar 

  3. S. P. Sivapirakasam, J. Mathew and M. Surianarayanan, Multi-attribute decision making for green electrical discharge machining, Expert Systems with Applications, 38 (7) (2014) 8370–8374.

    Article  Google Scholar 

  4. V. K. Meena and M. S. Azad, Grey relational analysis of Micro-EDM machining of Ti-6Al-4V alloy, Materials and Manufacturing Processes, 27 (2012) 973–977.

    Article  Google Scholar 

  5. K. P. Somashekhar, N. Ramachandran and J. Mathew, Optimization of material removal rate in Micro-EDM using artificial neural network and genetic algorithms, Materials and Manufacturing Processes, 25 (2010) 467–475.

    Article  Google Scholar 

  6. N. Pellicer, J. Ciurana and T. Ozel, Influence of process parameters and electrode geometry on feature microaccuracy in electro discharge machining of tool steel, Materials and Manufacturing Processes, 24 (2009) 1282–1289.

    Article  Google Scholar 

  7. M. Sundaram, B. Ganesh, Pavalarajan and P. K. Rajurkar, A study on process parameters of ultrasonic assisted Micro EDM Based on Taguchi Method, ASM Journal of Materials Engineering and Performance, 17 (2) (2007) 210–215.

    Article  Google Scholar 

  8. U. Aich and S. Banerjee, Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization, Applied Mathematical Modelling, 38 (11-12) (2013) 2800–2818.

    Article  Google Scholar 

  9. C. Rashed, L. Romoli, F. Tantussi, F. Fuso, L. Bertoncini, M. Fiaschi and G. Dini, Experimental optimization of microelectrical discharge drilling process from the perspective of inner surface enhancement measured by shear-force microscopy, CIRP Journal of Manufacturing Science and Technology, 7 (1) (2014) 11–19.

    Article  Google Scholar 

  10. N. Natarajan and R. Arunachalam, Optimization of micro-EDM with multiple performance characteristics using Taguchi method and Grey relational analysis, Journal of Scientific & Industrial Research, 70 (2011) 500–505.

    Google Scholar 

  11. M. Ay, U. Caydas and A. Hascaik, Optimization of micro-EDM drilling of inconel 718 super alloy, International Journal of Advvanced Manufacturing Technology, 66 (2012) 1015–1023.

    Article  Google Scholar 

  12. U. Natarajan, P. R. Periyannan and S. H. Yang, Mutipleresponse optimization for micro-endmiling process using response surface methodology, International Journal of Advanced Manufacturing Technology, 56 (2011) 177–185.

    Article  Google Scholar 

  13. L. Zhang, Z. Jia, F. Wank and W. Liu, A hybrid mode us-ing supporting vector machine and multi-objective genetic algorithm for processing parameters optimization in micro-EDM, International Journal of Advanced Manufacturing Technology, 51 (2010) 575–586.

    Article  Google Scholar 

  14. S. V. Kumar and M. P. Kumar, Optimization of cryogenic cooled EDM process parameters using Grey relational analysis, Journal of Mechanical Science and Technology, 28 (9) (2014) 3777–3784.

    Article  Google Scholar 

  15. M. Mahardika, G. S. Prihandana and K. Mitsui, Precision machining by discharge pulse counting methods in micro EDM processes, Journal of Mechanical Science and Technology, 26 (11) (2012) 3597–3603.

    Article  Google Scholar 

  16. S. Pushpendra, S. Bharti, S. Maheshwari and C. Sharma, Multi-objective optimization of electric-discharge machining process usingcontrolled elitist NSGA-II, Journal of Mechanical Science and Technology, 26 (6) (2012) 1875–1883.

    Article  Google Scholar 

  17. T. Temucin, H. Tozan, O. Vayvay, M. Harnicarova and J. Valicek, A fuzzy based decision model for nantraditional machining process selection, International Journal of Advanced Manufacturing Technology, 70 (2014) 2275–2282.

    Article  Google Scholar 

  18. S. J. Chen and C. L. Hwang, Fuzzy multiple attribute decision making methods and applications, Springer-verlag: New York (1992).

    Book  MATH  Google Scholar 

  19. C. L. Hwang and K. Yoon, Multiple attribute decision making methods and applications, Springer-verlag: Berlin (1981).

    Book  MATH  Google Scholar 

  20. J. Figueira and B. Roy, Determining the weights of criteria in the ELECTRE type methods with a revised Simos procedure, European Journal of Operational Research, 139 (2002) 317–326.

    Article  MATH  Google Scholar 

  21. D. L. Olson, Comparision of weights in TOPSIS models, Mathematical and Computer Modeling, 40 (2004) 721–727.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, CEG, Anna University, Chennai, India

    R. Manivannan & M. Pradeep Kumar

Authors
  1. R. Manivannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Pradeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Manivannan.

Additional information

R. Manivannan is currently completing his Ph.D. in the Department of Mechanical Engineering at Anna University, Chennai. He received his B.E. degree in Mechanical Engineering from Saveetha Engineering College and M.E degree in Manufacturing Systems Management at Anna University, India. His research interests include manufacturing and optimization technique.

M. Pradeep Kumar completed his Ph.D. in Mechanical Engineering in the Department of Mechanical Engineering at Anna University Chennai, India. He works as an associate professor in the same department of the same university. He has almost 15 years of teaching and research experience. He has published many research papers in international and national Journals. His research interests include cryogenic machining and the application of FEM in machining and micromachining.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manivannan, R., Kumar, M.P. Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS. J Mech Sci Technol 30, 137–144 (2016). https://doi.org/10.1007/s12206-015-1217-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-015-1217-4

Keywords

Search

Navigation