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Bhandare, Amar S.
- Investigation and Optimization of Inconel-718 during Dry EDM
Abstract Views :221 |
PDF Views:0
Authors
Affiliations
1 Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra, IN
1 Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra, IN
Source
Manufacturing Technology Today, Vol 18, No 9 (2019), Pagination: 52-58Abstract
Dry electric discharge machining (EDM) is a new process in which the liquid dielectric is replaced by a gaseous as a medium. The flow of high velocity gas through hollow pipe tool electrode into the gap provides removal of debris and prevents excessive heating of the tool and work piece at the discharge spots. Keeping literature review into consideration and trial experiment in this paper, an attempt has been made by selecting compressed air as a dielectric medium, with Inconel – 718 as a work piece material and copper as a tool electrode. Experiments are performed using Taguchi DoE L27 orthogonal array to observe and analyze the effects of different process parameters to optimize the response variables such as material removal rate (MRR), tool wear rate (TWR) and surface roughness (Ra). In the current work, a unit has been developed to implement dry EDM process on existing oil based EDM machine.Keywords
Dry Electrical Discharge Machining (Dry EDM), Material Removal Rate (MRR), Surface Roughness (Ra), Tool Wear Rate (TWR), Taguchi Method, Analysis of Variance.
References
- Leao, Fabio, N; Pashby, Ian R: A review on the use of environmentally friendly dielectric fluids in electrical discharge machining, ‘Journal of Materials Processing Technology’, 149 (1-3), 2004, 341-346.
- Kunieda, M and Yoshida, M: Electrical Discharge Machining in Gas Tokyo University of Agriculture and Technology, Department of Mechanical Systems Engineering Tokyo Japan’, 1996.
- Khundrakpam, Nimo Singh; Brar, Gurinder Singh; Deepak, Dharmpal: Grey-Taguchi Optimization of Near Dry EDM Process Parameters on the Surface Roughness, 'ICMPC 2017', vol. 5, no. 2, 2018, 4445-4451.
- Macedoa F T B; Wiessner, M; Hollenstein, C; Kuster, F; Wegener, K: Investigation of the Fundamentals of Tool Electrode Wear in Dry EDM, ‘7th CIRP Conference on High Performance Cutting (HPC VII)’, vol. 46, 2016, 55-58.
- Saha, S K & Choudry, S K: Multi–objective optimization of the dry electric discharge machining process, hal-00396875, version 1- 4, 2009.
- Uhlmann, Eckart; Schimmelpfennig, Tassilo-Maria; Perfilov, Ivan; Streckenbach, Jan; Schweitzer, Luiz: Comparative Analysis of Dry-EDM and Conventional EDM for the Manufacturing of Micro holes in Si3N4-TiN, ‘18th CIRP Conference on Electro Physical and Chemical Machining (ISEM XVIII)’, vol. 42, 2016, 173-178.
- Shirguppikar, Shailesh S; Dabade, Uday A: Experimental Investigation of Dry Electric Discharge Machining (Dry EDM) Process on Bright Mild Steel, 'IMME17', vol. 5, 2018, 7595-7603.
- Govindan, P and Joshi, S: Investigations into performance of dry EDM using slotted electrodes, ‘International Journal of Precision Engineering and Manufacturing', vol. 12, no. 6, 2011, 957-963.
- Govindan, P and Joshi, S: Experimental characterization of material removal in dry electrical discharge drilling, ‘International Journal of Machine Tools & Manufacture’, vol. 50, no. 5, 2010, 431–443.
- Tao, J; Shih, AJ; and Ni, Jun: Experimental Study of the Dry and Near-Dry Electrical Discharge Milling Processes, ‘Journal of Manufacturing Science and Engineering’, vol. 130, 2008, DOI:10.1115/1.2784276.
- Phadke M S : Quality engineering using robust design, Prentice Hall, Eaglewood Cliffs, 1989
- Modeling of Magnetic Assisted EDM of EN24 Steel Using Artificial Neural Network
Abstract Views :255 |
PDF Views:1
Authors
Affiliations
1 Veermata Jijabai Technological Institute, Mumbai, Maharashtra, IN
2 Walchand College of Engineering, Sangli, Maharashtra, IN
1 Veermata Jijabai Technological Institute, Mumbai, Maharashtra, IN
2 Walchand College of Engineering, Sangli, Maharashtra, IN
Source
Manufacturing Technology Today, Vol 20, No 5-6 (2021), Pagination: 31-37Abstract
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
- 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.