基于ELMAN网络的高速平面铣削表面粗糙度的研究

The Research of Surface Roughness on High-Speed Planar Milling Based on ELMAN Network

本文使用人工神经网络方法建立了高速平面铣削条件下切削参数对加工表面粗糙度影响的模型。通过高速切削实验,利用正交试验组合数据组训练神经网络,研究和预测切削速度、切削深度和每齿进给量对加工表面粗糙度的影响。通过实测数据测试了模型的性能,取得了较好的效果,该方法可以用于预测高速平面铣削表面粗糙度。

This paper establishes the model with the artificial nerve-network on condition of high-speed planar milling, to research the influence of cutting parameter to surface roughness. The network is trained by series of datum, which are obtained through high-speed cutting experiment with the orthogonal experiment method. And based on the network, the influence of cutting speed, cutting depth and feed per tooth to the surface roughness are discussed and forecasted. The performance of the model is tested with experiment datum, and the good result is obtained. It shows that the model can be used to forecast the surface roughness on high-speed planar milling.