| Based on the achievement that previous scholars make, the conception and development of chip control technology are systematically introduced in the dissertation. The significance of the prediction of the breaking area of 3-D chip former is clarified. The weakness of the prediction of the breaking area of 3-D chip former is also analyzed. Finally the new method of the prediction of the breaking area using neural networks for 3-D chip former is presented. The Radius Basis Function (RBF) networks, featuring a quick learning speed, is one of the feedforward neural networks. It has attracted much attention from automatic control recently for it implementing the nonlinearity mapping through adjusting the parameters of the nonlinearity transfer function. One of the on-line adaptive learning algorithm—the nearest neighbor-clustering algorithm has been implemented in the MATLAB programming language in this dissertation, laying the foundation for the prediction model. During the course of designing the learning samples, we make a lot of cutting experiments, after penetratingly studying the effect of the machining condition and the geometry feature of the chip former on chip control, the dissertation presents an improving method. First, classifying the chip-breaking grooves according to their sectional feature; then selecting the typical features from several important sections; lastly, expressing these features in the form of numberical value. This method is useful for effectively selecting the geometry feature of the chip former. Based on the above, this dissertation come true the prediction of the breaking area of 3-D chip former based on RBF neural networks and the result is good. After studying the effect of the network parameters, we get the conclusion that optimizing the learning samples and improving the network structure are benefit to training and advancing generalization capability of neural network, this way farther develops the research method of predicting the chip control.
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