Intelligent process modeling and optimization of die-sinking electric discharge machining
This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm...
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| Vydané v: | Applied soft computing Ročník 11; číslo 2; s. 2743 - 2755 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.03.2011
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| ISSN: | 1568-4946, 1872-9681 |
| On-line prístup: | Získať plný text |
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| Abstract | This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data. A two-dimensional axi-symmetric numerical (FEM) model of single spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time and energy dependent spark radius, etc. to predict the shape of crater, material removal rate (MRR) and tool wear rate (TWR). The model is validated using the reported analytical and experimental results. A comprehensive ANN based process model is proposed to establish relation between input process conditions (current, discharge voltage, duty cycle and discharge duration) and the process responses (crater size, MRR and TWR) .The ANN model was trained, tested and tuned by using the data generated from the numerical (FEM) model. It was found to accurately predict EDM process responses for chosen process conditions. The developed ANN process model was used in conjunction with the evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal process parameters for roughing and finishing operations of EDM. Experimental studies were carried out to verify the process performance for the optimum machining conditions suggested by our approach. The proposed integrated (FEM–ANN–GA) approach was found efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process. |
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| AbstractList | This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data. A two-dimensional axi-symmetric numerical (FEM) model of single spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time and energy dependent spark radius, etc. to predict the shape of crater, material removal rate (MRR) and tool wear rate (TWR). The model is validated using the reported analytical and experimental results. A comprehensive ANN based process model is proposed to establish relation between input process conditions (current, discharge voltage, duty cycle and discharge duration) and the process responses (crater size, MRR and TWR) .The ANN model was trained, tested and tuned by using the data generated from the numerical (FEM) model. It was found to accurately predict EDM process responses for chosen process conditions. The developed ANN process model was used in conjunction with the evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal process parameters for roughing and finishing operations of EDM. Experimental studies were carried out to verify the process performance for the optimum machining conditions suggested by our approach. The proposed integrated (FEM–ANN–GA) approach was found efficient and robust as the suggested optimum process parameters were found to give the expected optimum performance of the EDM process. |
| Author | Pande, S.S. Joshi, S.N. |
| Author_xml | – sequence: 1 givenname: S.N. surname: Joshi fullname: Joshi, S.N. organization: Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India – sequence: 2 givenname: S.S. surname: Pande fullname: Pande, S.S. email: s.s.pande@iitb.ac.in organization: Department of Mechanical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India |
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| Keywords | Finite element method (FEM) Electric discharge machining (EDM) Non-dominated sorting genetic algorithm (NSGA) Scaled conjugate gradient algorithm (SCG) Artificial neural networks (ANN) Process modeling and optimization |
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| SubjectTerms | Artificial neural networks (ANN) Electric discharge machining (EDM) Finite element method (FEM) Non-dominated sorting genetic algorithm (NSGA) Process modeling and optimization Scaled conjugate gradient algorithm (SCG) |
| Title | Intelligent process modeling and optimization of die-sinking electric discharge machining |
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