CAJ | 학술논문

微铣削表面粗糙度是衡量加工过程的一个重要性能指标，建立预测精度较高的微铣削表面粗糙度预测模型，准确地预测和控制零件微铣削加工后的表面粗糙度，对于合理选择工艺参数指导零件的实际加工意义重大。本文结合目前微铣削表面粗糙度预测模型的研究现状，针对黄铜件分别采用响应曲面法（RSM）和支持向量机（SVM）回归建立关于刀具悬伸量、主轴转速、每齿进给量、轴向切深四个切削参数的微铣削表面粗糙度预测模型，并通过微铣削加工试验对两种方法建立的预测模型进行对比验证，结果表明SVM预测模型预测均方误差仅为RSM预测模型的17.9%，预测精度较高，能够较好的预测微铣削表面粗糙度的大小和变化规律。因此，SVM预测模型更适合于微铣削表面粗糙度的预测。
미선삭표면조조도시형량가공과정적일개중요성능지표，건립예측정도교고적미선삭표면조조도예측모형，준학지예측화공제령건미선삭가공후적표면조조도，대우합리선택공예삼수지도령건적실제가공의의중대。본문결합목전미선삭표면조조도예측모형적연구현상，침대황동건분별채용향응곡면법（RSM）화지지향량궤（SVM）회귀건립관우도구현신량、주축전속、매치진급량、축향절심사개절삭삼수적미선삭표면조조도예측모형，병통과미선삭가공시험대량충방법건립적예측모형진행대비험증，결과표명SVM예측모형예측균방오차부위RSM예측모형적17.9%，예측정도교고，능구교호적예측미선삭표면조조도적대소화변화규률。인차，SVM예측모형경괄합우미선삭표면조조도적예측。
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.