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

Abstract

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.

Keywords

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

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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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