An investigation of material removal rate and kerf on WEDM through grey relational analysis

Authors

  • J.M. Pujara RK University, Rajkot and Assistant Professor Government Engineering College, Rajkot - 360005
  • K.D. Kothari Mechanical Engineering Department, RK University, Rajkot – 360020
  • A.V. Gohil Production Engineering Department, Shantilal Shah Engineering College, 364060, Bhavnagar

DOI:

https://doi.org/10.15282/jmes.12.2.2018.10.0322

Keywords:

Wire electrical discharge machining; metal matrix composites; material removal rate; kerf; response surface methodology; optimization.

Abstract

This paper describes an investigation on the optimization of process parameters on the Material Removal Rate (MRR) and kerf in Wire Electrical Discharge Machining (WEDM) operations. The experiments were carried out under varying pulse ON/OFF time, peak current, and wire feed by using Taguchi L9 orthogonal array. Grey Relational Analysis (GRA) optimization technique is used to find the optimal selection of process parameters for improvement in WEDM performance. The deviation of the kerf and material removal rate with process parameters is mathematically modeled by using Response Surface Methodology (RSM). The adequacy of the developed mathematical models is checked by Analysis of Variance (ANOVA). The problem is formulated as a multi-objective optimization as the influence of process input variables on material removal rate and kerf is conflicting in nature. Based on statistical analysis, it has been found that pulse on time, peak current is adequately influential parameters for MRR while pulse off time, peak current, and wire feed adequately influential for the kerf. Confirmation test result shows the application of the optimization technique for predicting optimal combinations of input variables for better output responses.

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Published

2018-06-30

How to Cite

[1]
J. Pujara, K. Kothari, and A. Gohil, “An investigation of material removal rate and kerf on WEDM through grey relational analysis ”, J. Mech. Eng. Sci., vol. 12, no. 2, pp. 3633–3644, Jun. 2018.

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Section

Article