Selecting an artificial neural network for efficient modeling and accurate simulation of the milling process

In this paper. two supervised neural networks are used to estimate the forces developed during milling. These two Artificial Neural Networks (ANNs) are compared based on a cost function that relates the size of the training data to the accuracy of the model. Training experiments are screened based on design of experiments. Verification experiments are conducted to evaluate these two models. It is shown that the Radial Basis Network model is superior in this particular case. Orthogonal design and specifically equally spaced dimensioning showed to be a good way to select the training experiments. (C) 2002 Elsevier Science Ltd. All rights reserved.