Artificial neural network prediction of material removal rate in electro discharge machining

Thermal erosion theory is widely accepted as an explanation of the erosion process in electro-discharge machining (EDM). Theoretical models are based on the solution of the transient heat conduction equation, which is modeled considering suitable assumptions with appropriate initial and boundary conditions. The closed form solutions result only after considering too many assumptions, which are far from actual machining conditions. The growth of the plasma channel, energy sharing between electrodes, process of vaporization, formation of recast layer, plasma-flushing efficiency, and temperature sensitivity of thermal properties of the work material are a few physical phenomena that render the machining process highly complex and stochastic. The mathematical consideration of all these complex phenomena is very difficult. Therefore, mathematical prediction of material removal rate when compared with the experimental results shows wide variation. In such circumstances, an attempt has been made to develop an artificial feed forward neural network based on the Levenberg-Marquardt back propagation technique of appropriate architecture of the logistic sigmoid activation function to predict the material removal rate. Such a neural network model is expected to perform well under the stochastic environment of actual machining conditions without understanding the complex physical phenomena exhibited in electro-discharge machining. The validity of the neural network model is checked with the experimental data, and we conclude that the artificial neural network model for EDM provides faster and more accurate results.