Using Wavelet Packet Transform for Surface Roughness Evaluation and Texture Extraction

Xiao Wang,Yuan Hong,Yichun Zhang,Guanglan Liao,Kepeng Chen,Tielin Shi

doi:10.3390/s17040933

Xiao Wang, Yuan Hong + Show 4 more

Open Access

https://doi.org/10.3390/s17040933

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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.

Highlights

The real surface has been defined in ISO (International Organization for Standardization) as a set of features that physically exist and separate the entire work piece from the surrounding medium [1]
The relative errors are 5.41%, roughness parameters obtained by Wavelet packet transform (WPT) approximate to those measured by profile meter (PM)
Is used to capture the surface topography, frequency components of the surface topography are analyzed in detail

Summary

Introduction

The real surface has been defined in ISO (International Organization for Standardization) as a set of features that physically exist and separate the entire work piece from the surrounding medium [1]. Kim et al presented an optimal algorithm based on WPT for surface quality characterization, improving the performance of texture classification without recursive calculation [31]. Makieta applied wavelet packet strategy for assessing milled surfaces He provided criteria for choosing the basic wavelet and evaluated the surface roughness and waviness [32]. WPT [33] and one-dimensional (1D) wavelet transform [34] were used to extract surface features for surface roughness evaluation based on an artificial neural network, where the statistical features were correlated with the surface roughness parameter Ra. Wavelet packet neural networks were used for surface texture classification [35,36]. WPT is applied to multi-resolution analysis on the high frequency components of real surface topography.

Theory

Simulations and Experiments

Results and Discussion

Figure are

Conclusions