摘要
Abstract
The work aims to introduce surface skewness Ssk and surface kurtosis Sku to jointly characterize surface topogra-phy in a more precise and reliable manner when the symmetric degree and profile peak sharpness of surface texture structures and surface profile amplitude values tend to be subject to large difference, even though the surfaces obtained through various processing methods sometimes have the same Sa value regarding the situation that the surface topography is characterized main-ly with the surface arithmetic average deviation Sa in the actual production. Orthogonal experiment and range analysis were ap-plied to study the influence of grinding parameters on the change in Ssk and Sku. On this basis, BP neural network was introduced in the predictive modeling of Ssk and Sku. The complex problem of multi-input and nonlinearity for surface roughness modeling was effectively solved due to the property of self-learning. The effect laws of grinding parameters on Ss kand Sku were achieved. Ssk would reach the minimum whenvs=20 m/s,vf=27 m/min,f=5 mm/min andap=0.005 mm, and Sku was the minimum when vs=29 m/s,vf=23 m/min,f=25 mm/min andap=0.002 mm. And then, the accurate neural network prediction models for Ssk and Sku based on grinding parameters were built respectively.vf andf have a significant impact on Ssk. Similarly, f andvs impact Sku the most. It is necessary to select suitable grinding parameters to obtain the surface with more valleys and less acute profile peaks. Moreover, the prediction models built can guide the optimization of grinding process effectively.关键词
表面偏斜度/表面峰度/磨削参数/神经网络`/预测建模Key words
surface skewness/surface kurtosis/grinding parameters/neural network/predictive modeling分类
机械制造
