Soft Computing

, Volume 19, Issue 8, pp 2193–2202 | Cite as

Modeling of ECDM micro-drilling process using GA- and PSO-trained radial basis function neural network

Methodologies and Application


Electrochemical discharge machining (ECDM) is a non-traditional manufacturing process potentially used to machine electrically non-conductive materials, such as ceramics and glass. The present paper explains the modeling of multi-input–multi-output ECDM micro-drilling of silicon nitride ceramics using radial basis function neural network (RBFNN). To establish the model, the process parameters such as applied voltage, electrolyte concentration and inter-electrode gap are treated as inputs and the important machining criteria namely material removal rate, radial overcut and heat affected zone are considered as outputs. A batch mode of training has been implemented to tune the developed RBFNN by utilizing a genetic algorithm (GA) and particle swarm optimization (PSO) methods, separately. Once, the optimal RBFNN is obtained, the performances of GA-trained RfBFNN (GA-RBFNN) and PSO-trained RBFNN (PSO-RBFNN) are compared with the help of experimental test cases. It has been observed that PSO-RBFNN is found to perform marginally better than GA-RBFNN.


Electrochemical discharge machining Radial basis function neural network Genetic algorithm Particle swarm optimization 


  1. Basak I, Ghosh A (1997) Mechanism of material removal in electrochemical discharge machining: a theoritical model and experimental verification. J Mater Process Technol 71:350–359CrossRefGoogle Scholar
  2. Bhattacharya B, Doloi BN, Sorkhel SK (1999) Experimental investigations into electrochemical discharge machining (ECDM) of non-conductive ceramic materials. J Mater Process Technol 95:145–154CrossRefGoogle Scholar
  3. Broomhead DS, Lowe D (1988) Multi-variable functional interpolation and adaptive networks. Complex Syst 11:321–355MathSciNetGoogle Scholar
  4. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Pearson Education IncGoogle Scholar
  5. Han MS, Min BK, Lee SJ (2011) Micro-electrochemical discharge cutting of glass using a surface-textured tool. CIRP J Manuf Sci Technol 4:363–369CrossRefGoogle Scholar
  6. Hen MS, Min BK, Lee SJ (2008) Modeling gas film formation in electrochemical discharge machining processes using a side-insulated electrode. J Micromech Microeng 18:1–8Google Scholar
  7. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948Google Scholar
  8. Kulkarni A, Sharan R, Lal GK (2002) An experimental study of discharge mechanism in electrochemical discharge machining. Int J Mach Tools Manuf 42:1121–1127CrossRefGoogle Scholar
  9. Laio YS, Wu LC, Peng WY (2013) A study to improve drilling quality of electrochemical discharge machining process. The seventeenth CIRP conference on electro physical and chemical machining. Procedia CIRP 6:609–614 Google Scholar
  10. Mollah AA, Pratihar DK (2008) Modeling of TIG welding and abrasive flow machining processes using radial basis function networks. Int J Adv Manuf Technol 37:937–952CrossRefGoogle Scholar
  11. Partihar DK (2014) Soft computing. Narosa Publishing House, New DelhiGoogle Scholar
  12. Paul L, Hiremath SS (2013) Response surface modelling of micro holes in electrochemical discharge machinig process. International conference on design and manufacturing. Precedia Eng 64:1395–1404Google Scholar
  13. Peng WY, Liao YS (2004) Study of electrochemical discharge machining technology for slicing non-conductive brittle materials. J Mater Process Technol 149:363–369CrossRefGoogle Scholar
  14. Samanta S, Chakraborty S (2011) Parametric optimization of some non-traditional machining processes using artificial bee colony algorithm. Eng Appl Artif Intell 24:946–957CrossRefGoogle Scholar
  15. Sarkar BR, Doloi B, Bhattacharyya B (2006) Parametric analysis on electrochemical discharge machining of silicon nitride ceramics. Int J Adv Manuf Technol 28:873–881CrossRefGoogle Scholar
  16. Yang CK, Wu KL, Hung JC, Lee SM, Lin JC, Yan BH (2011) Enhancement of ECDM efficiency and accuracy by spherical tool electrode. Int J Mach Tools Manuf 51:528–535Google Scholar
  17. Yang CK, Cheng CP, Mai CC, Wang AC, Hung JC, Yan BH (2010) Effect of surface roughness of tool electrode materials in ECDM performance. Int J Mach Tools Manuf 50:1088–1096CrossRefGoogle Scholar
  18. Zhang L, Liu C (2011) Application of PSO-RBFNN to the prediction of moisture content in crude oil of wellheat metering. In: International conference on computational and information sciences, pp 571–574Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • K. Shanmukhi
    • 1
  • Pandu R. Vundavilli
    • 2
  • B. Surekha
    • 3
  1. 1.Department of Mechanical EngineeringDVR & Dr. HS MIC College of TechnologyKanchikacherlaIndia
  2. 2.School of Mechanical SciencesIIT BhubaneswarBhubaneswarIndia
  3. 3.School of Mechanical EngineeringKIIT UniversityBhubaneswarIndia

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