Shi Wei-Chao, Zheng Jian-Ming, Wang Qi-Long, Wang Li-Jie, Li Qi
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Micromachines (Basel). 2021 Feb 24;12(3):228. doi: 10.3390/mi12030228.
It is important to characterize surface topography in order to study machined surface characteristics. Due to the features of periodicity and randomness of machined surface topography, the existing topographical parameters may not describe its features accurately. A novel characterization method called the normal declination angle of microfacet-based surface topography is thus proposed for this task. The topography of machined surfaces is measured and the data on the normal declination angle are obtained. Then, surface topography is analyzed via the distribution of the normal declination angle. The lognormal distribution characterization model of machined surface topography is established, and the accuracy of the model is verified by error analysis. The results show that the calculated results of the present characterization model are generally consistent with the distribution of the normal declination angle, where the maximal root mean square errors (RMSE) is 4.5%. Therefore, this study may serve as an effective and novel way to describe the characteristics of the machined surface topography.
为了研究加工表面特性,表征表面形貌很重要。由于加工表面形貌具有周期性和随机性的特点,现有的形貌参数可能无法准确描述其特征。因此,针对此任务提出了一种名为基于微平面的表面形貌法向偏角的新型表征方法。测量加工表面的形貌并获取法向偏角的数据。然后,通过法向偏角的分布来分析表面形貌。建立了加工表面形貌的对数正态分布表征模型,并通过误差分析验证了模型的准确性。结果表明,当前表征模型的计算结果与法向偏角的分布总体一致,其中最大均方根误差(RMSE)为4.5%。因此,本研究可为描述加工表面形貌特征提供一种有效且新颖的方法。