Prediction and Optimization of EDM Process Parameters Using Artificial Neural Network and Genetic Algorithm
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 6
Abstract
The objective of the paper is to develop empirical models and prediction of machining quality for Electrical Discharge Machining of Precipitation Hardened Stainless Steel (PH Steel) with copper tungsten electrode. The important process input parameters such as peak current, pulse on time, pulse off time and tool lift time are selected to predict the machining qualities of Material Removal Rate and Surface Roughness . Taguchi experimental design L27 orthogonal array was used to formulate the experimental design. The empirical models have been developed to predict the Material Removal Rate and Surface Roughness using Regression Analysis and Artificial Neural Network (ANN). Back propagation algorithm with experimental data used to train ANN. Prediction capability of ANN model and regression models are verified with experimental data. According to results, the ANN model is better performed as compared to the regression model to predict the MRR and SR for a given range of process parameters of EDM. Finally non-dominated sorting genetic algorithm (NSGA-II) applied to obtain non-dominated solution set to achieve the maximum material removal rate and minimum surface roughness.
Authors and Affiliations
Chandramouli S, Eswaraiah .
Keywords
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