Comparative modeling of wire electrical discharge machining (WEDM) process using back propagation (BPN) and general regression neural networks (GRNN)

Abstract

The use of two neural networks techniques to model wire electrical discharge machining process (WEDM) is explored in this paper. Both the back-propagation (BPN) and General Regression Neural Networks (GRNN) are used to determine and compare the WEDM parameters with the features of the surface roughness. A comparison between the back-propagation and general regression neural networks in the modeling of the WEDM process is given. It is shown that both the back-propagation and general regression neural networks can model the WEDM process with reasonable accuracy. However, back propagation neural network has better learning ability for the wire electrical discharge machining process than the general regression neural network. Also, the back-propagation network has better generalization ability for the wire electrical discharge machining process than does the general regression neural network.