基于BP神经网络陶瓷素坯铣削加工边缘破损预测模型的研究

Prediction Model of Edge Damage in Ceramic Blank Milling based on BP Neural Network

针对陶瓷坯体铣削加工中边缘破损的控制和预测问题,通过边缘破损尺寸与铣削加工参数映射关系,构建了一种基于BP神经网络的陶瓷坯体加工边缘破损预测模型。设计了铣削速度、进给速度、刀具锥角、铣削深度和铣削宽度五个因素的正交实验。实验结果表明,预测模型具有较高的预测精度,其预测误差在10%以内,且铣削加工中铣削宽度和深度对边缘破损影响最大。

Aiming at the problem of control and prediction of edge damage in ceramic body milling process,prediction model of ceramic blank edge damage based on BP neural network is established through the mapping relationship between edge damage size and milling parameters.Orthogonal experiments of milling speed,feed speed,cutter cone angle,milling depth and milling width are designed.It is demonstrated that the prediction model has high accuracy,with a prediction error of less than 10%.Also,the milling width and depth have been found to show the greatest impact on edge damage in milling.