caa a22 4500 605470111 CHVBK 20210128100326.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00500-014-1400-z doi (NATIONALLICENCE)springer-10.1007/s00500-014-1400-z Modeling of ECDM micro-drilling process using GA- and PSO-trained radial basis function neural network [Elektronische Daten] [K. Shanmukhi, Pandu Vundavilli, B. Surekha] 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. Springer-Verlag Berlin Heidelberg, 2014 Electrochemical discharge machining nationallicence Radial basis function neural network nationallicence Genetic algorithm nationallicence Particle swarm optimization nationallicence Shanmukhi K. Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut Vundavilli Pandu School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odisha, India aut Surekha B. School of Mechanical Engineering, KIIT University, 751024, Bhubaneswar, Odisha, India aut Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2193-2202 1432-7643 19:8<2193 2015 19 500 https://doi.org/10.1007/s00500-014-1400-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1400-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shanmukhi K. Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India aut NATIONALLICENCE 700 1- Vundavilli Pandu School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odisha, India aut NATIONALLICENCE 700 1- Surekha B. School of Mechanical Engineering, KIIT University, 751024, Bhubaneswar, Odisha, India aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2193-2202 1432-7643 19:8<2193 2015 19 500