基于RBF神经网络的花岗岩加工铣削力预测研究

Study of Prediction of Granite Processing Milling Force Based on RBF Neural Network

以花岗岩铣削加工中金刚石铣刀为研究对象,对直接导致刀具断裂影响加工效能的关键因素——铣削力进行了研究。针对花岗岩加工铣削力理论公式在实际应用中的局限性,提出了一种采用实验法和神经网络预测相结合的方法。利用实验获得神经网络铣削力预测的建模样本和验证样本以及刀具的断裂极限,利用RBF神经网络对花岗岩加工的铣削力进行了建模预测,通过实验数据验证了该模型的准确性,通过该模型预测获得优选的加工参数,并进行了应用。结果表明该方法有效地提高了刀具的加工效能。

By taking the diamond granite processing of milling clatter as tile study object, the key factor of milling force of the pro- cessing efficiency directly affected by milling cutler breakage was studied. Aimed at the limitation of granite processing milling force theoretic formula in practical application, a method was proposed which combined the experiment and the neural network iorecast. By using experiment to gel the model samples, testing samples and the tool of fracture limit predicted by milling fierce of neural networks, and using RBF neural network on granite processing milling force to have the modeling and prediction, the accuracy of the model was verified by the experimental data, to obtain the optimal processing parameters by the prediction model which was applied in the actual process. The results show that this is an effective method to improve the processing efficiency of tool.