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Summary

Deep cryogenically treated electrodes improve dimensional accuracy in electric discharge machining of Inconel 617. Modified dielectrics and artificial neural networks further enhance precision and reduce overcut, optimizing the machining process.

Keywords:
CuEDMInconel 617brasscryogenicallyovercut

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Surface Engineering

Background:

  • Inconel 617 (IN617) is a challenging material to machine due to its critical applications requiring high precision.
  • Electric discharge machining (EDM) is a common method for IN617, but suffers from over/undercut issues affecting dimensional accuracy.
  • Achieving tight tolerances in IN617 machining necessitates addressing EDM's inherent limitations.

Purpose of the Study:

  • To investigate the effectiveness of deep cryogenically treated (DCT) copper (Cu) and brass electrodes in improving dimensional accuracy during EDM of IN617.
  • To evaluate the impact of modified dielectric fluids on machining performance, specifically overcut (OC).
  • To model the complex phenomena of OC using artificial neural networks (ANN) for predictive accuracy.

Main Methods:

  • A complete factorial design was employed to machine a 300 μm deep impression on IN617.
  • Deep cryogenically treated (DCT) and non-treated (NT) copper and brass electrodes were utilized.
  • Modified dielectric mediums, including Kerosene-Span-20 and Kerosene-Tween-80, were tested.
  • Artificial neural network (ANN) models were developed to predict overcut.

Main Results:

  • DCT electrodes improved dimensional accuracy by an average of 13.5% compared to non-DCT electrodes across various modified dielectrics.
  • DCT brass electrodes showed a 29.7% overall improvement in reducing overcut (OC) compared to the average performance of DCT electrodes.
  • For non-treated electrodes, copper with Kerosene-Span-20 dielectric resulted in 33.3% less OC than with Kerosene-Tween-80.
  • ANN models accurately predicted the nonlinear and complex phenomena of OC.

Conclusions:

  • Deep cryogenic treatment of electrodes significantly enhances dimensional accuracy in EDM of Inconel 617.
  • Modified dielectrics, particularly Kerosene-Span-20, can further reduce overcut, especially with non-treated copper electrodes.
  • Artificial neural networks provide a reliable method for modeling and predicting overcut, reducing the need for extensive experimentation.