Title: Prediction of process parameters in electrical discharge machining using response surface methodology and ANN: an experimental study
Authors: T.M. Chenthil Jegan; R. Chitra; V.S. Thangarasu
Addresses: Department of Mechanical Engineering, St. Xaviers Catholic College of Engineering, Nagercoil, Kanyakumari, India ' Department of Computer Science and Engineering, Noorul Islam University, Thuckalai, Kanyakumari, 629180, India ' Department of Mechanical Engineering, Nehru Institute of Engineering and Technology, Coimbatore 641105, India
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.
Keywords: electro discharge machining; EDM; design of experiments; response surface methodology; artificial neural network material removal rate; surface roughness.
International Journal of Business Intelligence and Data Mining, 2020 Vol.16 No.2, pp.190 - 203
Received: 10 Nov 2016
Accepted: 04 Jul 2017
Published online: 20 Jan 2020 *