Analysis and Optimization of Process Parameters of Powder Mixed Near Dry- EDM by using Grey Relational Optimization

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Authors

  • Ajit .
     School of Mechanical Engineering, Lingaya's Vidyapeeth, Faridabad - 121001, Haryana ,IN
  • Sanjay Sundriyal
     School of Mechanical Engineering, Lingaya's Vidyapeeth, Faridabad - 121001, Haryana ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/35504

Keywords:

Dry Air, EDM, Metallic Powder, MRR, Residual Stress, Surface Finish

Abstract

The hybrid Electric Discharge Machine (PM-EDM) is the latest advancement in the removal of hard materials from workpieces while enhancing their performance and characteristics. This research project is focused on optimizing key process parameters, including material removal rate, tool wear, residual stresses, and surface finish. The approach utilized in this study involves employing the standard deviation-based objective weighting method in conjunction with GRA (Grey Relational Analysis) optimization to improve the hybrid PM-EDM process when applied to EN-31 material. Experimental runs were conducted to assess the impact of various input factors such as pulse on time, duty cycle, discharge current, and concentration of metallic powder. The GRA (Grey Relational Analysis)-based Taguchi method was employed for this purpose, and the experimental design followed an L-27 orthogonal array within the framework, facilitated by the use of minitab-20 software. A total of 27 experiments were conducted, encompassing diverse combinations of process parameters. Subsequently, an ANOVA (Analysis of Variance) was performed to scrutinize the impact of various inputs of discharge current, pulse on time, duty cycle, and powder concentration on MRR (Material Removal), TWR (Tool Wear), Ra (surface finish), and Rs (Residual stress).

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Published

2023-12-28

How to Cite

., A., & Sundriyal, S. (2023). Analysis and Optimization of Process Parameters of Powder Mixed Near Dry- EDM by using Grey Relational Optimization. Journal of Mines, Metals and Fuels, 71(11), 1837–1857. https://doi.org/10.18311/jmmf/2023/35504

Issue

Volume 71, Issue 11, November 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Europe PMC
Received 2023-10-31
Accepted 2023-11-13
Published 2023-12-28

 

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