Optimization of EDM parameters on surface quality and MRR of WC-40Cocomposites using NSGA-II

Authors

  • D. Kanagarajan  Department of Manufacturing Engineering, Annamalai university, Annamalai Nagar, Tamil Nadu, India

Keywords:

WC/Co composite. Electrical discharge machining (EDM). Non-dominated sorting genetic algorithm(NSGA-II)

Abstract

The correct selection of manufacturing conditions is one of the most important aspects to take into consideration in the majority of manufacturing processes and particularly, in processes related to Electrical Discharge Machining (EDM). It is a capable of machining geometrically complex or hard material components, that are precise and difficult-to-machine such as heat treated tool steels, composites, super alloys, ceramics, carbides, heat resistant steels etc. In the present work, the effectivenessof the EDM process with tungsten carbide and cobalt composites is evaluated in terms of the material removal rate and the surface finish quality of the workpiece produced. The objective of this research is to study the influence of operating parameters of EDM such as pulse current, pulse on time, electrode rotation and flushing pressure on material removal rate and surface roughness. The experimental results are used to develop the statistical models based on second order polynomial equations for the different process characteristics. The non-dominated sorting genetic algorithm (NSGA-II) has been used to optimize the processing conditions. A non-dominated solution set has been obtained and reported.

References

  1. Chen Y, Mahdavian SM (1999) Parametric study into erosionwear in a computer numerical controlled electro-dischargemachining process. J Wear 236:350–354
  2. Kuriakose S, Shunmugam MS (2005) Multi objective optimizationof wire electro discharge machining process by nondominated sorting genetic algorithm. J Mater Process Technol170:133–141
  3. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast elitistmulti-objective genetic algorithm: NSGA-II. IEEE T EvolutComput 6(2):182–197
  4. Raghuwanshi MM, Kakde OG (2004) Survey on multiobjectiveevolutionary and real coded genetic algorithms. In: Proceedings ofthe 8th Asia Pacific symposium on intelligent and evolutionarysystems, Cairns, Australia, pp 150–161
  5. Srinivas N, Deb K (1994) Multiobjective optimization usingnondominated sorting in genetic algorithms. EvolComput 2(3):221–248
  6. Deb K, Sundar J, Rao N UB, Chaudhuri S (2006) Reference pointbased multi objective optimization using evolutionary algorithms.Int J Comp Intell Res 2(3):273–286
  7. lgnizio JP (1976) Goal programming and its extensions. LexingtonBooks, Lexington, MA, USA
  8. Kanagarajan D, Karthikeyan R, Palanikumar K, Paulo DavimJ(2009) Application of goal programming technique for electrodischarge machining characteristics of cemented carbide (WC/Co). Int J Mater Product Technol,Vol. 35, Nos. ½.
  9. Eng.M, Bo Fu, CmsSichuan, "Piezo electric actuator design via multi objective optimization methods”, PhD thesis, VR China, pp39-41.
  10. Aravind seshadri, “A fast elitist multi objective genetic algorithm NSGA-II”- ,http://www.mathworks.com, Internet specification.
  11. Raghuwanshi.M.M, Kakde.O.G,(2004) “Survey on multi objective evolutionary and real coded genetic algorithms”, Proceedings of the 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, pp150-161.
  12. Kalyanmoy Deb, Agarwal.R.P,(1995) “Simulated binary crossover for continuous search space”, Journal of Complex Systems. Vol. 9, pp115-148.
  13. Nassif N, Kajl S, Sabourin R (2005) Optimization of HVAC control system strategy using two objective genetic algorithms. JHVAC&R Res 11(3):459-48613.
  14. Montgomery DC, Peck EA, Vining GG (2003) Introduction tolinear regression analysis, 3rd edn. Wiley, New York
  15. Sarin.V.K,(1981) Cemented carbide cutting tools, Advances in Powder Technology, Ed, D.Y. Chin, ASM,pp253-287.
  16. Kanagarajan.D, Karthikeyan.R, Palanikumar.K, PauloDavim.J (2008),“Optimization of electrical discharge machining characteristics of WC/Co composites using Non-dominated sorting genetic algorithms (NSGA-II) ”, Journal of Advanced Manufacturing Technology, 36:1124–1132,
  17. Liao.Y.S, Huang.J.T, Chen.Y.H,(2004) “A study to achieve a fine surface finish in wire EDM”, Journal of Material Processing Technology, Vol.149, pp165-171.
  18. Srivastava, Vineet, and Pulak M. Pandey.(2011) "Performance Evaluation of Electricaldischarge Machining (Edm) Process Using Cryogenically Cooled Electrode."Materials and Manufacturing Processes 27, no. 6: 683-692
  19. Khan, M. A. R., Rahman, M. M., &Kadirgama, K. (2014) Neural network modeling and analysis for surface characteristics in electrical discharge machining. Procedia Engineering, 90, 631-636.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2015-08-30

Issue

Volume 1, Issue 3, July-August-2015

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
D. Kanagarajan "Optimization of EDM parameters on surface quality and MRR of WC-40Cocomposites using NSGA-II" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 1, Issue 3, pp.196-206, July-August-2015.