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Modeling of Magnetic Assisted EDM of EN24 Steel Using Artificial Neural Network


Affiliations
1 Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India
2 Walchand College of Engineering, Sangli, Maharashtra, India
     

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EDM is a non-conventional method of electro-thermal machining process where electrical energy produces electrical sparks. To learn the performance of the process parameter on the response variable, the experiment was carried out on EN-24 steel with a copper electrode. For analysis, process parameters like current, pulse on time, voltage are considered. The Matlab ANN Toolbox is used for modeling purpose. ANN Model is developed with Traingdx, Learngdx, MSE, Logsig as training, learning, performance, and transfer functions, using a Feed-forward back-propagation as an algorithm with three nodes in the input layer and one node in the output layer for material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR), using various nodes in hidden layers. Eight networks are tried 3-1-3, 3-3-3, 3-6-3, 3-7-3, 3-1-3, 3-3-3-3-3, 3-6-6-3 and 3-7-7-3 structure. To predict the value of the response variable, a 3-7-3 network structure is found as best fit for the proposed model.

Keywords

Artificial Neural Network, Electro Discharge Machining, Material Removal Rate, Electrode Wear Rate, Surface Roughness.
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  • Andromeda, T., Yahya, A., Khamis, N., Khalil, K., & Erawan, A. (2011). Predicting Material Removal Rate of Electrical Discharge Machining (EDM) using Artificial Neural Network for High Igap current. InECCE 2011 - International Conference on Electrical, Control and Computer Engineering. 10.1109/INECCE.2011.5953887.

  • Beravala, H. & Pandey, P. (2018). Experimental investigations to evaluate the effect of magnetic field on the performance of air and argon gas-assisted EDM processes. Journal of Manufacturing Processes, 34, 356–373.

  • Chandramouli, S. & Eswaraiah, K. (2016). Modeling of EDM Process Parameters in Machining of 17-4 PH Steel using Artificial Neural Network. Indian Journal of Science and Technology, 9(1), 1-7. DOI: 10.17485/ijst/2016/v9iS1/103307.

  • Chekuri, R. B. R., Kalluri, R., Siriyala, R., & Palakollu, J. K. (2018). A study on die-sinking EDM of Nimonic C-263 superalloy: an intelligent approach to predict the process parameters using ANN. International Journal of Engineering & Technology, 7(1.1), 651-654.

  • Gangil, M. & Pradhan, M. (2017). Modeling & optimization of electrical discharge machining process using RSM: A Review. Materials Today Proceedings, 4(2), 1752-1761.

  • Govindan, P. & Joshi, S. (2010). Experimental characterization of material removal in dry electrical discharge drilling. International Journal of Machine Tools & Manufacture, 50(5), 431–443.

  • Joshi, S., Govindan, P., Malshe, A. & Rajurkar, K. (2011). Experimental characterization of dry EDM performed in a pulsating magnetic field. CIRP Annals, 60(1), 239-242.

  • Mhatre, M. S., Pawade, R. S., Sapkal, S. U., & Siddiqui, F. (2014). Prediction of EDM process parameters by using Artificial Neural Network (ANN) - A prediction technique. International Journal of Scientific & Engineering Research, 5(12), 29-33.

  • Parveen Kumar and Pooja Sharma (2014). Artificial neural networks-a study. International Journal of Emerging Engineering Research and Technology, 2(2), 143-148.

  • Zain, A. M., Haron, H., & Sharif, S. (2010). Prediction of surface roughness in the end milling machining using Artificial Neural Network. Expert Systems with Applications, 37(2), 1755-1768.


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  • Modeling of Magnetic Assisted EDM of EN24 Steel Using Artificial Neural Network

Abstract Views: 327  |  PDF Views: 1

Authors

Utkarsha U. Dabade
Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India
Prajakta R. Nalawade
Walchand College of Engineering, Sangli, Maharashtra, India
Amar S. Bhandare
Walchand College of Engineering, Sangli, Maharashtra, India

Abstract


EDM is a non-conventional method of electro-thermal machining process where electrical energy produces electrical sparks. To learn the performance of the process parameter on the response variable, the experiment was carried out on EN-24 steel with a copper electrode. For analysis, process parameters like current, pulse on time, voltage are considered. The Matlab ANN Toolbox is used for modeling purpose. ANN Model is developed with Traingdx, Learngdx, MSE, Logsig as training, learning, performance, and transfer functions, using a Feed-forward back-propagation as an algorithm with three nodes in the input layer and one node in the output layer for material removal rate (MRR), electrode wear rate (EWR), surface roughness (SR), using various nodes in hidden layers. Eight networks are tried 3-1-3, 3-3-3, 3-6-3, 3-7-3, 3-1-3, 3-3-3-3-3, 3-6-6-3 and 3-7-7-3 structure. To predict the value of the response variable, a 3-7-3 network structure is found as best fit for the proposed model.

Keywords


Artificial Neural Network, Electro Discharge Machining, Material Removal Rate, Electrode Wear Rate, Surface Roughness.

References