Multi-Objective Optimization in Electrical Discharge Machining of 6061 Al/SiC_p Using RSM and NSGA-II

Abstract:

In this paper, response surface methodology (RSM) and non sorting genetic algorithm (NSGA-II) are used to optimize the multi-responses of electrical discharge machining of Aluminum Alloy 6061/10%SiC_p composite. Experiment is performed to evaluates the effects of process parameters namely peak current, pulse on time, pulses off time and gap voltage on the responses material removal rate (MRR) and tool wear rate (TWR). The central composite rotatable design (CCRD) is utilized to design the experiment using RSM. Analysis of Variance (ANOVA) test is performed to validate model and to further establish the mathematical relation between process parameters and responses. Results are analyzed using ANOVA models. NSGA-II is used to optimize two conflicting responses i.e MRR and TWR. Finally results are validated by confirmatory experiment.

• Periodical: Key Engineering Materials (Volume 748)
• Pages: 207-211
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.748.207
• Online since: August 2017
• Authors: Balbir Singh*, Sudhir Kumar, Jatinder Kumar
• Keywords: Composite, Electrical Discharge Machining, Electrode, Material Removal Rate, Non Sorting Genetic Algorithm-II, Response Surface Methodology (RSM), Tool Wear Rate

References

[1] P.C. Pandey and H.S. Shan H.S., Modern Machining Processes", Tata McGraw Hill India, (2008) 84-113.

[2] B.J. Ranganath, Thermal Metal Cutting Processes, IK International Publishing House Pvt. Ltd, New Delhi, India, (2008) 4-19.

[3] M.K. Surappa, Aluminium matrix composites: Challenges and Opportunities, Sadhana, vol. 28 ( 1 & 2), (2003) 319-334.

DOI: 10.1007/bf02717141

[4] N. Tomac N. and K. Tonnessen, Machinability of particulate aluminum matrix composites, Annal CIRP, 42(1), 55-58.

[5] A. Manna and B. Bhattacharya B., A study on different tooling system during machining of Al/SiC-MMC, Journal of Materials Processing Technology, 123 (2000) 476-482.

DOI: 10.1016/s0924-0136(02)00127-9

[6] N. Singh, K. Raghukandan, M. Rathinasabapathi and B.C. Pai, Electric discharge machining of Al–10%SiCP as-cast metal matrix composites, Journal of Materials Processing Technology, (2004) 155–156 and 1653–1657.

DOI: 10.1016/j.jmatprotec.2004.04.321

[7] R.K. Bhushan, S. Kumar and S. Das, GA approach for optimization of surface roughness parameters in machining of Al alloy SiC particle composite, JMEPEG, vol. 21, pp.1676-1686, (2012).

DOI: 10.1007/s11665-011-0066-2

[8] P.K. Shrivastava and A.K. Dubey, (2013), Intelligent modeling and multi-objective optimization of electric discharge diamond grinding, Materials and Manufacturing Processes, 28, (2013) 1036-104.

[9] M.P. Garg, A. Jain and G. Bhushan, Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm, Proc IMechE Part B: J Engineering Manufacture 226(12), (2012).

DOI: 10.1177/0954405412462778

[10] D.C. Montgomery, Design and Analysis of Experiments, 5th edition; Wiley India: New Delhi, (2010).

[11] N. Srinivas and D. Kalyanmoy, Multiobjective Optimization using Non-dominated Sorting in Genetic Algorithms, Journal Evolutionary Computation, 2(3), (1994) 221-248.

DOI: 10.1162/evco.1994.2.3.221

[12] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6 (2), (2002) 182-19.

DOI: 10.1109/4235.996017