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Prediction of Material Removal Rate in Wire Electrical Discharge Machining of Aluminum Composites for Automotive Components
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 25, 2020 by SAE International in United States
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Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
Wire Electrical Discharge Machining (WEDM) is a contemporary approach of material removal which is conceived from the concept of Electrical Discharge Machining process. Wire Spark Erosion Machining which is known as WEDM, predominantly employed for removing material from hard materials and also especially used for making intricate shapes on any electrically conductive work material with irrespective of the hardness. Composite materials offers improved mechanical properties depends upon the constituents to be added. Graphene is identified as outstanding reinforcing element which provide support to enhance the desired properties of aluminium metal matrix composites in a considerable manner. In this present exploration an analysis has been performed on WEDM of Al-GNP composites. Pulse on time (μs), pulse off time (μs) and servo voltage (V) are deemed as input process parameters in this present exploration. Taguchi’s design approach has been adopted for designing and analyzing the experimental runs. An L27 Orthogonal Arrays was employed adopted to conduct the experimental runs. Material removal rate is deemed as desired performance measure which is need to be improved. The influence of process variables on desired performance measures such as material removal rate were analyzed by Taguchi’s single response analysis. The significance of independent process variables on desired performance measure is examined by ANOVA analysis. Multiple regression analysis has been performed for correlating the relationship among the selected input process variable and desired performance measure. The comparison results proved that the values predicted from the developed regression model were closer with the experimental observations.
CitationNatarajan, M., Joseph Selvi, B., Palampalle, B., and Katta Clement PhD, V., "Prediction of Material Removal Rate in Wire Electrical Discharge Machining of Aluminum Composites for Automotive Components," SAE Technical Paper 2020-28-0399, 2020, https://doi.org/10.4271/2020-28-0399.
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- Aravind Krishnan, S., and Samuel, G.L., “Multi-Objective Optimization of Material Removal Rate and Surface Roughness in Wire Electrical Discharge Turning,” International Journal of Advanced Manufacturing Technology 67(9-12):2021-2032, 2013.
- Raju, R., Manikandan, N., Palanisamy, D., Arulkirubakaran, D. et al., “Optimization of Process Parameters in Electrical Discharge Machining of Haste Alloy C276 Using Taguchi’s Method,” Materials Today: Proceedings 5(6):14432-14439, 2018.
- Binoj, J.S., Manikandan, N., Thejasree, P., Varaprasad, K.C. et al., “Machinability Studies on Wire Electrical Discharge Machining of Nickel Alloys Using Multiple Regression Analysis,” Materials Today: Proceedings, 2020, https://doi.org/10.1016/j.matpr.2020.06.407.
- Palanisamy, D., Devaraju, A., Manikandan, N., Balasubramanian, K. et al., “Experimental Investigation and Optimization of Process Parameters in EDM of Aluminium Metal Matrix Composites,” Materials Today: Proceedings 22:525-530, 2020.
- Dabade, U.A., “Multi-Objective Process Optimization to Improve Surface Integrity on Turned Surface of Al/SiCp Metal Matrix Composites Using Grey Relational Analysis,” Procedia CIRP 7:299-304, 2013.
- El-Hofy, H., Advanced Machining Processes (New York: McGraw-Hill, 2005).
- Ho, K.H., Newman, S.T.Ã., Rahimifard, S., and Allen, R.D., “State of the Art in Wire Electrical Discharge Machining (WEDM),” International Journal of Machine Tools and Manufacture 44:1247-1259, 2004.
- Manikandan, N., Raju, R., Palanisamy, D., and Binoj, J.S., “Optimisation of Spark Erosion Machining Process Parameters Using Hybrid Grey Relational Analysis and Artificial Neural Network Model,” International Journal of Machining and Machinability of Materials 22(1):1-23, 2020.
- Kumar, H.G.P., and Anthony Xavior, M., “Graphene Reinforced Metal Matrix Composite (GRMMC): A Review,” Procedia Engineering 97:1033-1040, 2014, http://dx.doi.org/10.1016/j.proeng.2014.12.381.
- Leone, C., Addona, D., and Teti, R., “Tool Wear Modelling through Regression Analysis and Intelligent Methods for Nickel Base Alloy Machining,” CIRP Journal of Manufacturing Science and Technology 4(3):327-331, 2011, http://dx.doi.org/10.1016/j.cirpj.2011.03.009.
- Mahapatra, S.S., and Patnaik, A., “Optimization of Wire Electrical Discharge Machining (WEDM) Process Parameters Using Taguchi Method,” International Journal of Advanced Man 34:911-925, 2007.
- Ross, P.J., Taguchi, Techniques for Quality Engineering (Singapore: McGraw Hill International Edition, 1996).
- Sahoo, A.K., and Pradhan, S., “Modeling and Optimization of Al/SiCp MMC Machining Using Taguchi Approach,” Measurement 46(9):3064-3072, 2013.
- Selvam, M., Panner, P.R.K., Electric, W., and Machining, D., “Optimization Kerf Width and Surface Roughness in Wirecut Electrical Discharge Machining Using Brass Wire,” 21(1):37-55, 2017.
- Manikandan, N., Binoj, J.S., Varaprasad, K.C., Sabari, S.S. et al., (2019), “Investigations on Wire Spark Erosion Machining of Aluminum-Based Metal Matrix Composites,” in Advances in Manufacturing Technology (Singapore: Springer), 361-369.
- Sivaprakasam, P., Hariharan, P., and Gowri, S., “Modeling and Analysis of Micro-WEDM Process of Titanium Alloy (Tie 6Ale4V) Using Response Surface Approach,” Engineering Science and Technology, an International Journal 17:227-235, 2014.