摘要
铣削变形问题一直是影响薄壁件加工精度的瓶颈问题。由于引起铣削变形的因素较多以及铣削过程本身的复杂性,使得铣削参数与变形量之间的关系很难用准确的解析式来表示。因此借助神经网络的非线性映射能力,建立了变形量与铣削参数之间的非线性映射模型。结果显示:所建立的神经网络模型具有较高的精度和良好的泛化能力。该研究为进一步的铣削参数优化提供了保障。
Processing deformation in the course of numerical control milling is a key problem of influencing the thin -walled workpiece＇s machining precision. Because of the complicacy of milling processing and many factors of causing deformation, it is difficult to obtain an exact analytical formula to express the intricate relationship of milling parameters and deformation. So with the help of ANN＇s powerful non - linear mapping ability, this paper has established a non - linear mapping model between milling parameters and deformation. Results show that the model is feasible and effective. This research will provide foundation for further optimization of milling parameters.
出处
《制造技术与机床》
CSCD
北大核心
2007年第8期48-50,共3页
Manufacturing Technology & Machine Tool
关键词
铣削变形
BP神经网络
变形预测
Milling Deformation
BP Neural Networks
Deformation Forecast
