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Indexed by:Journal Papers

Date of Publication:2013-10-20

Journal:The Journal of New Industrialization

Issue:10

Page Number:39-47

Key Words:Micro-milling;Surface roughness;Prediction model;RSM;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 RSM (Response surface method) and SVM (Support Vector Machine Regression) 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.