高速铣削工件表面粗糙度预测建模

Prediction Modeling of Surface Roughness for High Speed Milling Workpiece

高速铣削工件表面粗糙度是衡量高速铣削工件表面质量的重要指标.利用广义回归神经网络对工况1、工况3、工况5、工况7、工况9、工况11、工况13和工况15下的高速铣削试验数据进行分析,得到用于预测高速铣削工件表面粗糙度的数学模型.对工况2和工况6下的高速铣削工件表面粗糙度进行预测,将高速铣削工件表面粗糙度预测结果和试验结果进行对比.结果表明,高速铣削工件表面粗糙度预测数学模型具有良好的泛化性能,广义回归神经网络可为高速铣削工件表面粗糙度预测建模提供一种有效工具.

Surface roughness is an important index in evaluating the surface qualit workpiece. The generalized regression neural network（GRNN） was applied to ana of high speed mi ze high speed mi test data of working condition 1, condition 3, condition 5, condition 7, condition 9, condition 13 and condition 15 ,and the prediction model of surface roughness for high speed milling w established. Predictions of surface roughnesses for high speed milling workpiece under work 11, cond orkpiece ng ng on was ing condition 2 and condition 6 were conducted, and the predicted results were compared with test data. The result shows that the prediction model of surface roughness has a good generalization performance, and the GRNN can provide an effective method for establishing the prediction model of surface roughness for high speed milling workpiece.