基于RBF神经网络的磨削表面粗糙度预测模型

Grinding Surface Roughness Prediction Model Based on RBF Neural Network

工件表面粗糙度是反映表面完整性指标中极为重要的一个参数,也是衡量磨削加工质量的重要因素之一,准确地预测磨削表面粗糙度对于快速合理地选择磨削加工工艺参数具有重要意义。通过开展实际磨削实验获得磨削加工数据,对获取的样本数据进行归一化处理以适应RBF神经网络的学习。同时采用循环算法比较得出隐层的最优神经元个数,最终建立了基于径向基函数神经网络的磨削表面粗糙度预测模型,并利用MATLAB进行仿真预测。仿真结果表明:该预测模型准确率很高,能为表面粗糙度预测研究提供可靠数据。

Surface roughness of workpiece is a crucial parameter among integrity indexes,and it is also one of the important fac-tors to measure the grinding quality. Predicting the grinding surface roughness accurately has great significance for selection of the grinding process parameters in a more rapid and reasonable way. The grinding processing data were acquired through actual grinding experiment. In order to adapt to the RBF neural network learning,the data were normalized. At the same time,cyclic algorithm was used for choosing optimal number of neurons in the hidden layers. Eventually,the grinding surface roughness prediction model was es-tablished based on RBF neural network. The MATLAB simulation results show that the prediction model has high accuracy,and can provide reliable data for surface roughness prediction research.