An empirical study on statistical analysis and optimization of EDM process parameters for inconel 718 super alloy using D-optimal approach and genetic algorithm

Document Type : Research Paper


1 Ph.D. Candidate, Ferdowsi University of Mashhad, Department of Mechanical Engineering, Mashhad, Iran

2 Associate Professor, Ferdowsi University of Mashhad, Department of Mechanical Engineering, Mashhad, Iran


Among the several non-conventional processes, electrical discharge machining (EDM) is the most widely and successfully applied for the machining of conductive parts. In this technique, the tool has no mechanical contact with the work piece and also the hardness of work piece has no effect on the machining pace. Hence, this technique could be employed to machine hard materials such as super alloys. Inconel 718 super alloy is a nickel based alloy that is mostly used in oil and gas, power stations and aerospace industries. In this study the effect of input EDM process parameters on Inconel 718 super alloy, is modeled and optimized. The process input parameters considered here include voltage (V), peak current (I), pulse on time (Ton) and duty factor (η). The process quality measures are surface roughness (SR) and material removal rate (MRR). The objective is to determine a combination of process parameters to minimize SR and maximize MRR. The experimental data are gathered based on D-optimal design of experiments. Then, statistical analyses and validation experiments have been carried out to select the best and most fitted regression models. In the last section of this research, genetic algorithm (GA) has been employed for optimization of the performance characteristics. Using the proposed optimization procedure, proper levels of input parameters for any desirable group of process outputs can be identified. A set of verification tests is also performed to verify the accuracy of optimization procedure in determining the optimal levels of machining parameters. The results indicate that the proposed modeling technique and genetic algorithm are quite efficient in modeling and optimization of EDM process parameters.


Main Subjects

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  • Receive Date: 10 August 2015
  • Revise Date: 04 November 2015
  • Accept Date: 04 November 2015