为了定位和减弱由于震动等因素引入检测数据中的奇异性信号,从而更加准确地分离表面粗糙度轮廓以评定表面形貌,提出了一种基于小波的奇异性检测和滤波的方法。首先应用小波变换检测奇异性信号对样品表面轮廓测量数据进行预处理以减弱奇异信号对表面参数评定的影响,然后采用小波滤波分解表面轮廓以获得粗糙度尺度轮廓,在此基础上对其三维典型参数进行计算。验算实例采用两类:在实测数据的基础上外加已知的奇异特征和典型带有奇异信号的实测数据。实验结果表明:第一类数据经过小波奇异性处理后与原始未加奇异特征的粗糙度轮廓参数非常接近,Sa,Sz,Sq,Ssk和Sku的相对变化分别为0.27%、0.78%、0.51%、0.04%和1.04%,第一类数据经过小波奇异性处理轮廓测量数据后与原始外加奇异特征的实测数据未经奇异性处理的小波滤波表面粗糙度参数Sa,Sz,Sq,Ssk和Sku的相对变化分别为1.14%、20.63%、1.95%、3.81%、8.86%;第二类实测数据小波奇异性处理前后的小波滤波表面粗糙度参数Sa,Sz,Sq,Ssk和Sku的相对变化分别为6.2％、27.47％、9.54％、131.62％、53.77％。实验结果表明,采用精度较高的小波滤波提取表面粗糙度轮廓之前通过奇异性算法抑制奇异特征,可得到更加合理的表面参数评定结果。

In order to locate and exclude the singularity induced by vibration and to evaluate the surface texture exactly,a wavelet singularity detection and filtering method is presented. Firstly,the wavelet singularity analysis is used to preprocess the sampled surface and then the 3-dimensional roughness profile is extracted by using the wavelet filter. Then, the typical evaluation parameters of roughness profile are computed and the results are compared with two kinds of samples: measured data manually added singularity and measured data with singularity originally. Experimental results show that for the first kind of data roughness parameters after singularity processing for the first kind of data with added singularity are similar to those of original surface,and the relative changes of Sa,Sz,Sq,Ssk and Sku are 0.27%、0.78%、0.51%、0.04%、1.04% respectively. The relative changes of Sa,Sz,Sq,Ssk and Sku before and after singularity processing for the first kind of data with added singularity are 1.14%、20.63%、1.95%、3.81%、8.86%. For the second kind of data the relative changes of Sa,Sz,Sq,Ssk and Sku before and after singularity processing are 6.2％、27.47％、9.54％、131.62％、53.77％respectively. The experiments prove that more reasonable evaluation results can be obtained if the singularity is excluded from the surface before the roughness profile is extracted by using the wavelet filtering.