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Experimental Investigation and Optimization of Electric Discharge Machining Process Parameters Using Grey-Fuzzy-Based

Ankit Sharma1, Vidyapati Kumar2, Atul Babbar3

  • 1Chitkara College of Applied Engineering, Chitkara University, Punjab 140401, India.

Materials (Basel, Switzerland)
|October 13, 2021
PubMed
Summary

This study optimized electrical discharge machining (EDM) for hexagonal hole formation in SG iron. Grey-fuzzy logic identified optimal parameters for material removal rate and overcut, enhancing manufacturing efficiency.

Keywords:
fuzzy logicgrey theoryoptimizationprocess parameterresponse

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Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Tribology

Background:

  • Electrical Discharge Machining (EDM) is a key unconventional method for complex geometries in advanced materials.
  • Pearlitic Spheroidal Graphite (SG) iron 450/12 grade is a critical engineering material requiring precise machining.
  • Optimizing EDM parameters is crucial for efficient material removal and dimensional accuracy.

Purpose of the Study:

  • To investigate the machining efficiency of EDM for hexagonal hole formation in SG iron 450/12.
  • To optimize the multi-objective EDM process using Grey Relational Analysis (GRA) and fuzzy logic.
  • To develop predictor regression models and validate them through confirmation tests.

Main Methods:

  • Experimental trials varying peak current (I), pulse-on time (Ton), and inter-electrode gap (IEG).
  • Grey Relational Analysis (GRA) interwoven with fuzzy logic for multi-objective optimization.
  • Statistical analysis, response graphs, response tables, and regression modeling.

Main Results:

  • Determined the optimal parametric settings for EDM based on the Grey-Fuzzy Reasoning Grade (GFRG).
  • Established predictor regression models for material removal rate (MRR), overcut, and GFRG.
  • Validated the models through confirmation tests, demonstrating their predictive capability.

Conclusions:

  • The integrated grey-fuzzy logic approach effectively optimizes the EDM process for SG iron.
  • The developed models accurately predict EDM responses, aiding in process control and efficiency.
  • This research provides a robust methodology for optimizing complex machining operations in engineering materials.