Architecture and training algorithm of feed forward artificial neural network to predict material removal rate of electrical discharge machining process

This paper presents a model of a feed forward articial neural network to predict the material removal rate of an electrical discharge machine process. a new modified architecture and training algorithm is proposed by segmenting the roughing and finishing machining parameters of the process. the segmentation is performed in order to obtain a lower difference between the actual and predicted material removal rates. through comparative analysis and results obtained between the two architectures, it is found that the new modified feed forward articial neural network produces lower error between the experimental and predicted material removal rates, thus, improving the accuracy of the prediction model.