摘要
提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法。首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律。利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值。实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个~3个数量级,预测精度高10倍左右。
A novel prediction model based on Least Squares Support Vector Machine （LS-SVM） with application in the milling area was proposed. Firstly, according to the basic of the design theory in orthogonal experiments and features with milling, the effect of milling technological parameters on surface roughness was analyzed. Secondly, through analyzing the experiment data, some laws about the effect of different parameters such as spindle speed, desired rate and cutting depth were found. Finally, LS-SVM regression algorithm was used to set up milling prediction model, by which the roughness could be predicted from technological parameters on the limited test data. The practical experimental results showed that the speed of presented model was 2-3 power level higher, while accuracy predicted was about 10 times, that of Back Propagation （BP） network model under the same circumstances.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2007年第6期1137-1141,共5页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(70272032)~~
关键词
表面粗糙度
最小二乘支持向量机
智能预测
铣削
正交实验
surface roughness
least squares support vector machine
intelligent prediction
milling
orthogonal experiment
