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Optimization of Process Variables in Sinking EDM Using Artificial Neural Network (ANN) Method

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
S. Kumar
Sanjoy K. Ghoshal
Pawan K. Arora
Leeladhar Nagdeve
Published in
Advances in Modern Machining Processes
Publisher
Springer Nature Singapore

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Abstract

The chapter delves into the optimization of process variables in Sinking EDM using Artificial Neural Networks (ANN) for machining AISI 420 martensitic stainless steel with a copper electrode. It highlights the advantages of ANN in modeling and determining optimal parameter combinations for Electrode Wear Rate (EWR) and Surface Roughness (SR). The study involves a thorough literature review, experimental setup, and the application of the Taguchi L16 orthogonal array to investigate the effects of various machining parameters. The results demonstrate the superior predictive capabilities of ANN, with high correlation coefficients for both EWR and SR, making it a reliable and accurate method for optimizing machining parameters. The chapter concludes by emphasizing the cost-effective, time-saving, and efficient nature of the ANN approach, setting it apart from traditional methods.
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previous chapter Multi-objective Parametric Optimization During WEDM of Silicon Through MOGWO
next chapter Optimization of Wire-EDM Process Parameters for Ti6Al4V Alloy Cutting Using Mayfly Algorithm
Title
Optimization of Process Variables in Sinking EDM Using Artificial Neural Network (ANN) Method
Authors
S. Kumar
Sanjoy K. Ghoshal
Pawan K. Arora
Leeladhar Nagdeve
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Advances in Modern Machining Processes

Print ISBN: 978-981-19-7149-5

Electronic ISBN: 978-981-19-7150-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-7150-1_19
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