A Hybrid Radial Basis Function Neural Network for Dimensional Error Prediction in End Milling

Abstract

AbstractThis paper presents an approach to predict dimensional errors in end milling by using a hybrid radial basis function (RBF) neural network. First, the results of end milling experiments are discussed and the effects of the cutting parameters on dimensional errors of machined surfaces are analyzed. The results showed the dimensional errors are affected by the spindle speed, the feed rate, the radial and axial depths of cut. Then, a hybrid RBF neural network is applied. This neural network combines regression tree and an RBF neural network to rapidly determine the center values and its number, and the radial values of the radial basis function. Finally, the prediction models of dimensional errors are established by using the RBF neural network, the ANFIS (adaptive-network-based fuzzy inference system), and the hybrid RBF neural network for end milling. Compared with the predicted results of the above three models, the performance of the hybrid RBF neural network-based method is shown to be the best.KeywordsFeed RateRadial Basis FunctionFuzzy Inference SystemSpindle SpeedRadial Basis Function Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.