基于最小二乘支持向量机的铣削加工表面粗糙度预测模型

在分析以往所建立的表面粗糙度预测模型方法不足的基础上，将一种基于最小二乘支持向量机的预测模型引入铣削加工领域，并给出了相应的步骤和算法。该模型能方便地预测铣削加工参数对加工表面粗糙度的影响，并能利用有限的试验数据得出整个工作范围内的表面粗糙度预测值，有助于准确认识已加工表面质量随铣削参数的变化规律。通过具体实例及与其他几种预测方法的对比表明，在相同样本条件下，其模型构造速度比标准支持向量机方法高1～2个数量级，模型预测误差约为支持向量机方法的40％，预测精度比常规BP模型高1个数量级。因此，基于最小二乘支持向量机方法建模速度快、预测精度高、适合加工表面粗糙度预测。

A Prediction Model for Surface Roughness in Milling Based on Least Square Support Vector Machine

A novel prediction model based on least square support vector machine(LS-SVM) was proposed.Based on the new model,the design steps and learning algorithm were given.The practical experimental results show that the construction speed of this LS-SVM model is 10 or 100 times less than that of the SVM model,while the prediction errors are 60%.Moreover,compared with BP model,the prediction accuracy is about 10 times higher than that of the former.The effects of milling parameters on surface roughness in milling can be predicted with the limited test data,thus the variation law of quality of machined surface following milling parameters can be obtained.