Hits:

Indexed by:Journal Papers

Date of Publication:2016-01-01

Journal:Industrial Lubrication and Tribology

Included Journals:EI、SCI

Volume:68

Issue:2

Page Number:206-211

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

Abstract: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.