Advertisement

Optimization of Electrical Parameters for Machining of Ti–6Al–4V Through TOPSIS Approach

  • T. PraveenaEmail author
  • J. Prasanna
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

This paper deals with improvisation of electrical parameters which are necessary for working of micro-electrical discharge machining (micro-EDM/μ-EDM). TOPSIS formulation was used for the evaluation purpose. The advancement of electrical parameters was done to increase the material removal rate (MRR) and understate the tool wear rate (TWR) and overcut (OC). A prototype has been built up to carry out the investigations. Being difficult to cut material, Ti–6Al–4V was used for the assessment. The experiments were conducted by the design of experiments through Taguchi’s orthogonal array with three electrical factors, viz., peak current (Ip), pulse on time (Ton), and duty factor (DF) at three levels. The results were analyzed by TOPSIS method. It leads to better results than other optimization methods. Finally, ANOVA test was performed to accomplish the contributions of the working parameters toward the quality characteristics.

Keywords

Micro-EDM Micro-hole Optimization Taguchi TOPSIS 

References

  1. 1.
    Liu, K., Lauwers, B., Reynaerts, D.: Process capabilities of Micro-EDM and its applications. Int. J. Adv. Manuf. Technol. 47, 11–19 (2010)CrossRefGoogle Scholar
  2. 2.
    Diver, C., Atkinson, J., Helmi, H.J., Li, L.: Micro-EDM drilling of tapered holes for industrial applications. J. Mater. Process. Technol. 149, 296–303 (2004)CrossRefGoogle Scholar
  3. 3.
    Masuzawa, T.: State of the art of micromachining. Ann. CIRP 49(2), 473–488 (2000)CrossRefGoogle Scholar
  4. 4.
    Xiao, Z., Dahmardeh, M., Moghaddam, M.V., Nojeh, A., Takahat, K.: Scaling approach toward nano electro-discharge machining: nanoscale patterning of carbon nanotube forests. Microelectron. Eng. 150, 64–70 (2016)CrossRefGoogle Scholar
  5. 5.
    Braganca, I.M.F., Rosa, P.A.R., Dias, F.M., Martins, P.A.F., Alves, L.L.: Experimental study of micro-EDM discharges. J. Appl. Phys. 113, 1–14 (2013)CrossRefGoogle Scholar
  6. 6.
    Ho, K.H., Newman, S.T.: State of the art electrical discharge machining (EDM). Int. J. Mach. Tools Manuf. 43, 1287–1300 (2003)CrossRefGoogle Scholar
  7. 7.
    Shao, B., Rajurkar, K.P.: Modelling of the crater formation in micro-EDM. Proc. CIRP 33, 376–381 (2015)CrossRefGoogle Scholar
  8. 8.
    Prasanna, J., Karunamoorthy, L., Raman, M., Prashanth, S., Chordia, D.R.: Optimization of process parameters of small hole dry drilling in Ti–6Al–4V using Taguchi and grey relational analysis. Measurement 48, 346–354 (2014)CrossRefGoogle Scholar
  9. 9.
    Pradhan, B.B., Masanta, M., Sarkar, B.R., Bhattacharyya, B.: Investigation of electrodischarge micro-machining of titanium super alloy. Int. J. Adv. Manuf. Technol. (2008)Google Scholar
  10. 10.
    Hasçalık, A., Çaydaş, U.: Electrical discharge machining of titanium alloy (Ti–6Al–4V). Appl. Surf. Sci. 253, 9007–9016 (2007)CrossRefGoogle Scholar
  11. 11.
    Lin, Y.C., Yan, B.H., Chang, Y.S.: Machining characteristics of titanium alloy (Ti–6Al–4V) using a combination process of EDM with USM. J. Mater. Process. Technol. 104, 171–177 (2000)CrossRefGoogle Scholar
  12. 12.
    Natarajan, N., Arunachalam, R.: Optimization of micro-EDM with multiple performance characteristics using Taguchi method and grey relational analysis. J. Sci. Ind. Res. 70, 500–505 (2011)Google Scholar
  13. 13.
    Tiwary, P., Pradhan, B., Bhattacharyya, B.: Application of multi-criteria decision making methods for selection of micro-EDM process parameters. Int. J. Adv. Manuf. Technol. 2, 251–258 (2014)Google Scholar
  14. 14.
    Rahman, M.M.: Modeling of machining parameters of Ti-6Al-4 V for electric discharge machining: a neural network approach. Sci. Res. Essays 7(8), 881–890 (2012)Google Scholar
  15. 15.
    Somashekhar, K.P., Ramachandran, N., Mathew, J.: Optimization of material removal rate in Micro-EDM using artificial neural network and genetic algorithms. Mater. Manuf. Process. 25, 467–475 (2010)CrossRefGoogle Scholar
  16. 16.
    Senthil Kumar, V.S., Lokesh, R., Rathinasuriyan, C., Sankar, R.: Multi response optimization of submerged friction stir welding process parameters using topsis approach. In: Proceedings of the ASME 2015 International Mechanical Engineering Congress and Exposition, 13–19 Nov 2015Google Scholar
  17. 17.
    Athawale, V.M., Chakraborty, S.: A TOPSIS method-based approach to machine tool selection. In: Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management Dhaka, Bangladesh, 9–10 Jan 2010Google Scholar
  18. 18.
    Roszkowska, E.: Multi-criteria decision making models by applying the topsis method to crisp and interval dataGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringCEG, Anna UniversityChennaiIndia

Personalised recommendations