Modeling of machining parameters of Ti-6Al-4V for electric discharge machining: A neural network approach

Abstract

This paper presents the artificial intelligence model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate the peak current, servo voltage, pulse on- and pulse off-time in EDM effects on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFNN) is used to develop the artificial neural network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method by using response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through analysis of variance (ANOVA). The optimum machining conditions are estimated and verified with proposed ANN model. It is observed that the developed model is within the limits of the agreeable error with experimental results. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed ANN models can satisfactorily evaluate the MRR, TWR as well as SR in EDM. Therefore, the proposed model can be considered as valuable tools for the process planning for EDM and leads to economical industrial machining by optimizing the input parameters. Key words: Ti-6AL-4V, material removal rate, tool wear rate, surface roughness, radial basis function neural network, response surface method.