Using Wavelet Packet Transform for Surface Roughness Evaluation and Texture Extraction

Abstract

Surface characterization plays a significant role in evaluating surface functional performance. In this paper, we introduce wavelet packet transform for surface roughness characterization and surface texture extraction. Surface topography is acquired by a confocal laser scanning microscope. Smooth border padding and de-noise process are implemented to generate a roughness surface precisely. By analyzing the high frequency components of a simulated profile, surface textures are separated by using wavelet packet transform, and the reconstructed roughness and waviness coincide well with the original ones. Wavelet packet transform is then used as a smooth filter for texture extraction. A roughness specimen and three real engineering surfaces are also analyzed in detail. Profile and areal roughness parameters are calculated to quantify the characterization results and compared with those measured by a profile meter. Most obtained roughness parameters agree well with the measurement results, and the largest deviation occurs in the skewness. The relations between the roughness parameters and noise are analyzed by simulation for explaining the relatively large deviations. The extracted textures reflect the surface structure and indicate the manufacturing conditions well, which is helpful for further feature recognition and matching. By using wavelet packet transform, engineering surfaces are comprehensively characterized including evaluating surface roughness and extracting surface texture.

Research and Modelling of Surface Roughness, Cutting Forces and I-kaz Coefficients for S42C in Turning using Response Surface Methodology

“…In (5) shows the third central moment, skewness (S) which identifies a distribution's degree of asymmetry around its mean. Wang et al (2017) used skew, mean and kurtosis to assess surface roughness and used wavelet packet transform to extract surface texture [13]. They found that the mean, skewness and kurtosis values increased with the rise in noise, meaning that these statistical moments are influenced by noise much stronger than surface roughness values.…”

Research and Modeling of Photovoltaic Array Channel Noise Characteristics

“…It can represent the local characteristics of signals in the time-frequency domain and has strong analysis ability for non-stationary signals. The decomposition process is to make the signal through a series of filters with different center frequencies but the same bandwidth, simultaneously decomposing the high and low frequency parts of the signal to improve the frequency resolution [30,31]. The discretized signal sequence is decomposed into layer wavelet packets to obtain sub-signals.…”

Analysis of the applicability of filtration methods for coordinate measurements

“…Known methods for nonlinear low-pass filtering are wavelet transforms [12][13][14], bilateral filter [15][16][17], and smoothing spline [20]. The most promising one should be considered a bilateral spline, which is a further development of the linear Gaussian filter and having two optimization parameters.…”

“…These methods can be used to filter the measured signal on the CMM. Among the known methods, the following directions can be distinguished: wavelet transforms [12][13][14], bilateral filter [15][16][17] and smoothing spline [18][19][20].…”