Prediction of Cutting Consumption based on Optimization- Making RBF Artificial Neural Network

摘要

Excision rate was fitted and predicted via building on cutting parameters affecting cutting machining process with Optimization-Making RBF (OMRBF) Neural Network. Radial basis function was made to select the best optimized distribution density in order to advance the fitting and forecasting capability of RBF. The result of OMRBF was compared with BP Neural Network's, what showed that the fitting and forecasting accuracy of OMRBF was much higher than BP Neural Network's.