Abstract
In this paper, a Back Propagation (BP) neural network was proposed for forging die design of automobile steering knuckles. It was used to predict the appropriate die cavity geometry data by training from sample data of practically successful cases according to the correlations between various factors that influence the forging process. In particular, a Radial Basis Function (RBF) neural network was also developed to compare its prediction accuracy with that of the BP neural network. The results show that the neural network is suitable for actual forging die design as long as the number of learning samples is enough.
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