Multi-parametric Optimization of Wired Electrical Discharge Machining Process to Minimize Damage Cause in Steel - A Soft Computing-Based Taguchi-Grey Relation Analysis Approach

Kusumlata Jain, Vani Agrawal, Sayed Sayeed Ahmad, Smaranika Mohapatra, Prabhat Kumar Srivastava, Dhanaraj Bharathi Narasimha, Ritesh Bhat

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The automotive industry makes crucial components from a wide array of materials. Euro Norm (EN) 31 steel is a highly regarded engineering functional material that meets the industry's requirements. However, conventional machining is not cost effective due to EN 31's high hardness and strength. Thus, the current research focuses on the effect of wired electrical discharge machining (WEDM) process parameters on the material removal rate (MRR) and average arithmatic mean of surface roughness (Ra) of EN 31 steel, as WEDM is a highly sustainable and cost-effective alternative to the conventional machining processes. Experiments are conducted utilizing a Taguchi L27 orthogonal array with the following input parameters: servo voltage, pulse width, pulse interval, and cutting speed. Grey relational analysis (GRA) has been used to optimize the multiple responses. The analysis of variance (ANOVA) of the grey relational grade (GRG) demonstrated that the most influential element in simultaneously improving performance measures is the speed, S (rpm) as it contributes 62.39 % to the variance in the response.

Original languageEnglish
Pages (from-to)267-274
Number of pages8
JournalEngineered Science
Volume20
DOIs
Publication statusPublished - 12-2022

All Science Journal Classification (ASJC) codes

  • Chemistry (miscellaneous)
  • Materials Science(all)
  • Energy Engineering and Power Technology
  • Engineering(all)
  • Physical and Theoretical Chemistry
  • Artificial Intelligence
  • Applied Mathematics

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