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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：Industrial Lubrication and Tribology

收录刊物：EI、SCI

卷号：68

期号：2

页面范围：206-211

关键字：micro-milling;surface roughness;prediction model;response surface method;RSM;support vector machine;SVM

摘要：Surface roughness is an important performance indication for micro-milling processing. Establishing a roughness-prediction model with high-precision is helpful to select the cutting parameters for micro-milling. Two prediction models are established by response surface method (RSM) and support vector machine regression (SVM) in this paper. Four cutting parameters are involved in the models (extended length of micro-milling tool, spindle speed, feed per tooth, and cutting depth in the axial direction). The models are established for material of brass. Experiments are carried out to verify the accuracy of the models. The results show that SVM prediction model has higher prediction accuracy, predict the variation law of micro-milling surface roughness better than RSM.