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 ...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.展开更多
基金Project(90923022) supported by the National Natural Science Foundation of ChinaProject(2009220022) supported by Liaoning Science and Technology Foundation,China
文摘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.
