Multi-variable Analysis and Optimization of Electrical Discharge Machining Process Using a PCA-ANN Based Approach

Authors

  • Kaushal Pratap Singh
  • Brij Kishor Singh
  • Manoj Kumar Gaur

Abstract

AbstractThe optimum selection of process parameters has played a crucial role in electrical discharge machining (EDM) for improving the material removal rate, reducing the tool wear rate and radial overcut. In this paper, optimum parameters while machining 202 stainless steel using copper electrode as a tool has been investigated. For optimization of process parameters along with multiple quality characteristics, principal component analysis coupled with artificial neural network method has been adopted in this work. Here artificial neural network (ANN) is used to predict the combined objective function (COF). Comparison between experimental COF with ANN predicted COF to find the percentage error.  Keywords: Response parameter (MMR, TWR, SR), EDM, PCA, ANN, COF      Cite this Article          Kaushal Pratap Singh, Brij Kishor Singh, Manoj Kumar Gaur. Multi-variable Analysis                   and Optimization of Electrical Discharge Machining Process Using a PCA-ANN Based                  Approach. Trends in Opto-Electro & Optical Communications. 2016; 6(3): 39–45p.

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Published

2017-02-06

Issue

Vol. 6 No. 3 (2016)

Section

Research Articles

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