本文用双隐层BP人工神经网络建立了丝杆螺母副表面边界膜温度特性的磨损自补偿数学模型。
The BP neural network used in the temperature characteristic of the boundary film on the screw-nut pairs surface in the wear-self-compensation system was established.
在两种不同的运动副表面粗糙度下，分别讨论了配流盘静压支承的主要结构参数对最小水膜厚度的影响。
The influence of the primary structural parameter on the minimal water film thickness will be respectively discussed in two different rough degree.
通过这个过程，当工具电极(阳级？)只有极小的损耗时材料便获得了了迁移速度，并且并不危害副表面。
During the process, high material removal rates are obtained while the tool electrode wear is extremely small and the sub-surface is not damaged.
应用推荐