A Study on the Machinability of Wire Electrical Discharge Machining of Nickel Alloy Using Taguchi Grey Approach

Abstract:

Superalloys, referred to as nickel alloys, have several uses in engineering and are widely used in industries that are as diverse as chemical processing and food processing. The high thermal conductivity and high strength of these materials make them hard to remove material from with traditional processing techniques. The majority of modern techniques for machining harder materials are alternatives to older methods. The present study is focusing on Wire Electrical Discharge Machining (WEDM), a modern machining technique used for the processing of tougher materials. The aspiration of this work is to present a Taguchi-based Grey technique that can be used to optimize a number of different performance indicators. The connection between the input and output variables has been analyzed using a regression model. Taguchi's design approach has been applied to the design of trials, with the Pulse on/off time and the applied current serving as independent variables. For enhancing the multiple machining performance of nickel alloy during Wire Electrical Discharge Machining (WEDM), this experimental effort seeks to pinpoint the most effective variables. This is accomplished using the Taguchi-Grey method. The performance analysis offers producers with concrete proof of the efficiency of evolved systems, allowing them to make well-informed and effective choices.

• Periodical: Key Engineering Materials (Volume 971)
• Pages: 33-43
• DOI: https://doi.org/10.4028/p-2zAe5p
• Online since: December 2023
• Authors: Duraiswamy Palanisamy, P. Thejasree, Umapathi Durai*, Natarajan Manikandan
• Keywords: Grey Relational Analysis, Haste Alloy C-276, Nickel Alloy, Regression Model, Response Analysis, Taguchi’s Method, WEDM

References

[1] Prayogo, G. S.; Lusi, N. Application of Taguchi Technique Coupled with Grey Relational Analysis for Multiple Performance Characteristics Optimization of EDM Parameters on ST 42 Steel. In AIP Conference Proceedings; Author(s), 2016.

DOI: 10.1063/1.4945515

[2] Roy, S.; Kumar, R.; Anurag; Panda, A.; Das, R. K. A Brief Review on Machining of Inconel 718. Mater. Today 2018, 5 (9), 18664–18673.

DOI: 10.1016/j.matpr.2018.06.212

[3] Abhilash, P. M.; Chakradhar, D. Prediction and Analysis of Process Failures by ANN Classification during Wire-EDM of Inconel 718. Adv. Manuf. 2020, 8 (4), 519–536.

DOI: 10.1007/s40436-020-00327-w

[4] Jadam, T.; Sahu, S. K.; Datta, S.; Masanta, M. EDM Performance of Inconel 718 Superalloy: Application of Multi-Walled Carbon Nanotube (MWCNT) Added Dielectric Media. J. Braz. Soc. Mech. Sci. Eng. 2019, 41 (8).

DOI: 10.1007/s40430-019-1813-9

[5] Singh, A.; Ghadai, R. K.; Kalita, K.; Chatterjee, P.; Pamučar, D. Edm Process Parameter Optimization for Efficient Machining of Inconel-718. Facta Univ. Ser. Mech. Eng. 2020, 18 (3), 473.

DOI: 10.22190/fume200406035s

[6] Ho, K. H.; Newman, S. T. State of the Art Electrical Discharge Machining (EDM). Int. J. Mach. Tools Manuf. 2003, 43 (13), 1287–1300.

DOI: 10.1016/s0890-6955(03)00162-7

[7] Nguyen, H.-P.; Ngo, N.-V.; Nguyen, Q.-T. Optimizing Process Parameters in Edm Using Low Frequency Vibration for Material Removal Rate and Surface Roughness. J. King Saud Univ. - Eng. Sci. 2021, 33 (4), 284–291.

DOI: 10.1016/j.jksues.2020.05.002

[8] Kliuev, M.; Florio, K.; Akbari, M.; Wegener, K. Influence of Energy Fraction in EDM Drilling of Inconel 718 by Statistical Analysis and Finite Element Crater-Modelling. J. Manuf. Process. 2019, 40, 84–93.

DOI: 10.1016/j.jmapro.2019.03.002

[9] El-Hofy, H. A. Advanced Machining Processes; McGraw-Hill Education, 2005.

[10] Li, X. K.; Yan, F. H.; Ma, J.; Chen, Z. Z.; Wen, X. Y.; Cao, Y. RBF and NSGA-II Based EDM Process Parameters Optimization with Multiple Constraints. Math. Biosci. Eng. 2019, 16 (5), 5788–5803.

DOI: 10.3934/mbe.2019289

[11] Buschaiah, K.; JagadeeswaraRao, M.; Krishnaiah, A. Investigation on the Influence of Edm Parameters on Machining Characteristics for Aisi 304. Mater. Today 2018, 5 (2), 3648–3656.

DOI: 10.1016/j.matpr.2017.11.615

[12] Palanisamy, D.; Devaraju, A.; Manikandan, N.; Balasubramanian, K.; Arulkirubakaran, D. Experimental Investigation and Optimization of Process Parameters in EDM of Aluminium Metal Matrix Composites. Mater. Today 2020, 22, 525–530.

DOI: 10.1016/j.matpr.2019.08.145

[13] Caiazzo, F.; Cuccaro, L.; Fierro, I.; Petrone, G.; Alfieri, V. Electrical Discharge Machining of René 108 DS Nickel Superalloy for Aerospace Turbine Blades. Procedia CIRP 2015, 33, 382–387.

DOI: 10.1016/j.procir.2015.06.086

[14] Manikandan, N.; Thejasree, P.; Raju, R.; Palanisamy, D.; Varaprasad, K. C.; Sagai Francis Britto, A.; Deeraj Chengalva Sai, A. Investigations on Wire Electrical Discharge Machining of Titanium Alloys by Taguchi—Grey Approach. In Lecture Notes in Mechanical Engineering; Springer Nature Singapore: Singapore, 2022; p.359–368.

DOI: 10.1007/978-981-19-0244-4_35

[15] Liu, S.; Liu, Y. An Introduction to Grey Systems: Foundations, Methodology, and Applications; Iigss Academic Publisher, 1998.

[16] Manikandan, N.; Varaprasad, K. C.; Thejasree, P.; Palanisamy, D.; Arulkirubakaran, D.; Raju, R.; Badrinath, K. Prediction of Performance Measures Using Multiple Regression Analysis for Wire Electrical Discharge Machining of Titanium Alloy. In Lecture Notes in Mechanical Engineering; Springer Nature Singapore: Singapore, 2022; p.601–612.

DOI: 10.1007/978-981-19-0244-4_57

[17] Palanisamy, D.; Senthil, P. Optimization on Turning Parameters of 15-5PH Stainless Steel Using Taguchi Based Grey Approach and Topsis. Arch. Mech. Eng. 2016, 63 (3), 397–412.

DOI: 10.1515/meceng-2016-0023

[18] Srinivasan, D., N. Ganesh, H. Ramakrishnan, R. Balasundaram, R. Sanjeevi, and Mohanraj Chandran. "Investigation of surface roughness and material removal rate of WEDM of SS304 using ANOVA and regression models." Surface Topography: Metrology and Properties 10, no. 2 (2022): 025014.

DOI: 10.1088/2051-672x/ac6c9e

[19] Majumder, H., and K. P. Maity. "Predictive analysis on responses in WEDM of titanium grade 6 using general regression neural network (GRNN) and multiple regression analysis (MRA)." Silicon 10 (2018): 1763-1776.

DOI: 10.1007/s12633-017-9667-1

[20] Sadeghi, Mohammad, Hamideh Razavi, Amin Esmaeilzadeh, and Farhad Kolahan. "Optimization of cutting conditions in WEDM process using regression modelling and Tabu-search algorithm." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 225, no. 10 (2011): 1825-1834.

DOI: 10.1177/0954405411406639

[21] Kulekci, Mustafa Kemal, Adnan Akkurt, Ugur Esme, and Iskender Ozkul. "Multiple regression modeling and prediction of the surface roughness in the WEDM process." Materiali in tehnologije 48 (2014): 9-14.

[22] Kumar, Harmesh, Alakesh Manna, and Rajesh Kumar. "Modeling of process parameters for surface roughness and analysis of machined surface in WEDM of Al/SiC-MMC." Transactions of the Indian Institute of Metals 71 (2018): 231-244.

DOI: 10.1007/s12666-017-1159-x

[23] Nain, Somvir Singh, Dixit Garg, and Sanjeev Kumar. "Investigation for obtaining the optimal solution for improving the performance of WEDM of super alloy Udimet-L605 using particle swarm optimization." Engineering science and technology, an international journal 21, no. 2 (2018): 261-273.

DOI: 10.1016/j.jestch.2018.03.005