基于改进型BP网络的油管接箍加工圆度误差预测  

Prediction of the Roundness Error of Tubing Coupling with Improved BP Neural Network Model

作　　者：唐喜强[1] 时轮[1] 庄光法[1] 许开州[1] 

机构地区：[1]上海交通大学机械与动力工程学院,上海200030

出　　处：《机械设计与研究》2011年第2期91-93,106,共4页Machine Design And Research

摘　　要：基于人工神经网络所体现出的良好的非线性逼近特性,运用遗传算法优化BP网络的权值和阈值,避免BP网络陷入局部极小点,同时通过灰色关联分析优化网络的结构,使网络具有更好的泛化性和收敛速度。运用改进的BP网络发掘油管接箍加工中各个加工参数对最终加工圆度误差的潜在关系,从而实现对油管接箍加工圆度误差的预测。结果表明改进型BP神经网络具有较快的收敛速度和较好的泛化性能够准确预测油管接箍加工圆度误差。Neural network has good non-linear approach characteristic.The weight value and the threshold value of BP neural network improved by genetic algorithm,so the BP neural network can prevent itself from getting into local minimum point,and the use of grey correlation analysis in optimizing the structure of network can improve generalization performance and increase convergence speed.The improved network can be used in the prediction of the roundness error of tubing coupling by digging the hidden relation between processing parameters and roundness error.The results showed that the improved BP neural network has fast convergence rate,strong generalization capability and,accurately predicting the roundness error of tubing coupling.

关 键 词：圆度误差 BP网络 遗传算法 灰色关联分析 

分 类 号：TG506[金属学及工艺—金属切削加工及机床]