Thermal Error Modeling of CNC Turning Center Using Radial Basis Function Neural Network
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The traditional BP neural network approaches have some drawbacks such as low convergence speed and local minimal point. A neural network based on radial basis function(RBF) was used to predict and compensate the thermal error of a CNC turning center. The initialization and learning approach of RBF neural network was discussed. RBF neural network examples by two modeling ways were demonstrated. The modeling performances of RBF approach and LMS approach were synthetically compared. The validation of the modeling robustness was given at last. The experiment result shows that RBF network model makes more accurate predictions and compensation with less modeling time than the LMS linear models.