摘要
建立基于人工神经网络的模具型腔粗加工走刀方式的选择模型,设计了学习样本。采用3层反向传播BP网络模拟模具型腔几何参数与数控加工走刀方式的非线性关系,应用训练好的BP网络为模具型腔选择合理的走刀方式。切削加工表明,应用人工神经网络选择走刀方式可以提高模具型腔的加工效率。
An artificial neural network （ANN） -based model of tool path pattern selection for die cavity roughing is set up, and the training specimens are also designed in this paper. A three-layer BP neural network is adopted to simulate the non-linear relationship between the geometric parameters of the die cavity and NC tool path patterns and then the optimum tool path pattern for cutting layers of the die cavity is chosen. Examples demonstrate the validity and convenience of the method.
出处
《锻压技术》
CAS
CSCD
北大核心
2005年第6期53-57,共5页
Forging & Stamping Technology
