Prediction of process parameters in electrical discharge machining using response surface methodology and ANN: an experimental study
by T.M. Chenthil Jegan; R. Chitra; V.S. Thangarasu
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 16, No. 2, 2020

Abstract: In the present work, the process parameters of electro discharge machining are predicted by response surface methodology and artificial neural network in AA6061. AA6061 is extensively used in aircraft and aerospace applications. In order to reduce the depletion of the material during machining, prediction of appropriate machining parameter is essential. Current, pulse on, pulse off and flushing pressure are considered as input parameters for prediction. Experiments were conducted with those parameters in five different levels and data collected related to process responses for optimisation. Material removal rate and surface roughness measured for each experimental run were compared, utilised to fit a quadratic mathematical model in response surface methodology. In ANN model, artificial neural network with back propagation algorithm was used to develop the relationship between input parameters and predominant output responses. The performance of the developed model is analysed ANOVA and regression plot. The results proved that artificial neural network model is better for empirical modelling.

Online publication date: Thu, 30-Jan-2020

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