斜齿微线段齿轮刚度研究

Research on Stiffness of Helical Gears with Micro-segments Profile

介绍了斜齿微线段齿轮刚度的定义,并首次使用有限元法进行了斜齿微线段齿轮刚度计算,初步得出了斜齿微线段齿轮的刚度要好于相应的渐开线齿轮的结论。提出了利用人工神经网络的非线性映射能力,训练一个可以预测不同参数斜齿微线段齿轮刚度的人工神经网络,使其可以方便地代入到有关的计算程序中,其中重点介绍的是人工神经网络的教师样本构造问题。

The definition of stiffness of helical gears with micro-segments profile was introduced, and the stiffness has been calculated by using finite element methods (FEM) for the first time. The conclusion was preliminary drawn that helical gears with micro-segments profile have better stiffness than involute gear. An artificial neural network (ANN) for predicting stiffness was trained, which can be used by other relevant programmer. The problem of how to construct teacher samples was introduced emphatically. The results showed that the method is effective.