基于RSM的SiC单晶片表面粗糙度预测及参数优化

通过响应面分析法(RSM)对超声振动辅助金刚石线锯切割SiC单晶体的工艺参数进行分析和优化。采用中心组合设计实验,考察线锯速度、工件进给速度、工件转速和超声波振幅这4个因素对SiC单晶片表面粗糙度值的影响,建立了SiC单晶片表面粗糙度的响应模型,进行响应面分析,采用满意度函数(DFM)确定了切割SiC单晶体的最佳工艺参数,验证试验表明该模型能实现相应的硬脆材料切割过程的表面粗糙度预测。

Predicting Surface Roughness of SiC Monocrystal Wafer and Optimizing Its Parameters Using Response Surface Method

The response surface method(RSM) is used to study the influence of the process parameters on the surface roughness of SiC monocrystal wafer under wire saw with the ultrasonic vibration machining process.The central composite design(CCD) is used to design the experimental scheme.The wire saw′s velocity,part feed rate,part speed and ultrasonic amplitude are the factors that influence the SiC surface roughness.An empirical model has been developed for predicting the surface roughness for machining the SiC monocrystal wafer.The response surface regression and variance analysis are used to study the effects of process parameters.The optimum machining condition for minimizing the surface roughness is determined by using the desirability function approach.The influence of different parameters on machining the SiC monocrystal wafer has been analyzed in detail.The verification experimental results show that this model can well predict the surface roughness for machining monocrystal materials.