Modeling of ECDM micro-drilling process using GA- and PSO-trained radial basis function neural network
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| 024 | 7 | 0 | |a 10.1007/s00500-014-1400-z |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1400-z | ||
| 245 | 0 | 0 | |a Modeling of ECDM micro-drilling process using GA- and PSO-trained radial basis function neural network |h [Elektronische Daten] |c [K. Shanmukhi, Pandu Vundavilli, B. Surekha] |
| 520 | 3 | |a 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. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a Electrochemical discharge machining |2 nationallicence | |
| 690 | 7 | |a Radial basis function neural network |2 nationallicence | |
| 690 | 7 | |a Genetic algorithm |2 nationallicence | |
| 690 | 7 | |a Particle swarm optimization |2 nationallicence | |
| 700 | 1 | |a Shanmukhi |D K. |u Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India |4 aut | |
| 700 | 1 | |a Vundavilli |D Pandu |u School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odisha, India |4 aut | |
| 700 | 1 | |a Surekha |D B. |u School of Mechanical Engineering, KIIT University, 751024, Bhubaneswar, Odisha, India |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/8(2015-08-01), 2193-2202 |x 1432-7643 |q 19:8<2193 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1400-z |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1400-z |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Shanmukhi |D K. |u Department of Mechanical Engineering, DVR & Dr. HS MIC College of Technology, 521180, Kanchikacherla, AP, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Vundavilli |D Pandu |u School of Mechanical Sciences, IIT Bhubaneswar, 751013, Bhubaneswar, Odisha, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Surekha |D B. |u School of Mechanical Engineering, KIIT University, 751024, Bhubaneswar, Odisha, India |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/8(2015-08-01), 2193-2202 |x 1432-7643 |q 19:8<2193 |1 2015 |2 19 |o 500 | ||