Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM

Volume 8 Issue 3 May - July 2018

Research Paper

Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM

Dinesh Kumar*, Naveen Beri**

* Dean Academics (Associate Professor), Tawi Engineering College, Pathankot- IKGujral Punjab Technical University, Punjab, India.

** Associate Professor, Department of Mechanical Engineering, Beant College of Engineering Technology, Gurdaspur, Punjab, India.

Kumar,D., and Beri, N. (2018). Optimization of Process Parameters by ANN and Taguchi Analysis on M2 Tool Steel With Versatile Electrodes during EDM. i-manager’s Journal on Mechanical Engineering, 8(3), 53-58. https://doi.org/10.26634/jme.8.3.14738

Abstract

Artificial Neural Network (ANN) analysis is used for the prediction of Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness (SR) in Electrical Discharge Machining(EDM) process of M2 tool steel, followed by a Matlab program.Taguchi's L36 mixed orthogonal array was used to optimize the input parameters, such as polarity, electrode type, and concentration of abrasive powder, discharge current, voltage and duty cycle. Taguchi technique individually is not effective for multiple performance characteristics, so ANN is applied for this experimental study for converting process parameters into single performance characteristics which allows for a potential economical experimentation. The outcome analysis shows that the suggested ANN models can predict the acceptable optimum machining parameters for Electrical Discharge Machining, and has been verified experimentally as well as graphically by adopting Back-Propagation Neural Networks (BPNN) model.