中文摘要：

螺线的表面粗糙的预兆的模型倾斜齿轮(SBG ) 牙齿基于最少的方形的支持向量机器(LSSVM ) 被建议。有光线的基础功能(RBF ) 的一个非线性的 LSSVM 模型内核被介绍然后 PECF 系统的试验性的安装被建立。Taguchi 方法被介绍估计在齿轮牙齿表面粗糙上完成参数的效果，并且训练数据也通过实验被获得。在在一样的条件下面的预言的价值和试验性的价值之间的比较被执行。结果证明预言的价值被发现与试验性的价值近似一致。吝啬的绝对百分比错误(MAPE ) 为表面粗糙是 2.43% 并且 2.61% 为应用电压。

英文摘要：

The predictive model of surface roughness of the spiral bevel gear （SBG） tooth based on the least square support vector machine （LSSVM） was proposed. A nonlinear LSSVM model with radial basis function （RBF） kernel was presented and then the experimental setup of PECF system was established. The Taguchi method was introduced to assess the effect of finishing parameters on the gear tooth surface roughness, and the training data was also obtained through experiments. The comparison between the predicted values and the experimental values under the same conditions was carried out. The results show that the predicted values are found to be approximately consistent with the experimental values. The mean absolute percent error （MAPE） is 2.43% for the surface roughness and 2.61% for the applied voltage.