An improved decomposition algorithm of surface topography of machining

Abstract

In order to accurately decompose the surface morphology of machined surface and trace the potential errors of the machine, a comprehensive improved algorithm is proposed, which combines wavelet packet decomposition (WPD) and improved complete ensemble empirical modal decomposition of adaptive noise (Improve CEEMDAN). Firstly, the cost function is used to find the optimal wavelet packet base and the optimal decomposition tree is obtained. Secondly, under semi-hard threshold denoising, the wavelet coefficients obtained by the optimal decomposition tree can generate the denoised signal. Finally, the white noise is preprocessed to obtain the upper limit frequency and the band white noise, and the improvement of CEEMDAN is completed. The improved CEEMDAN is used to decompose the denoised signal to obtain a series of intrinsic mode functions (IMFs). The merit of this comprehensive improved algorithm is that it can improve the calculation efficiency and decomposition accuracy by adaptively finding the optimal wavelet packet base and adding band-limited white noise. Simulations and experiments results show the feasibility, effectiveness and higher accuracy of the comprehensive algorithm in decomposing surface topography.

Keywords:

• Empirical mode decomposition
• Feature decomposition
• Surface topography
• Wavelet packet decomposition

Additional information

Funding

This work was supported by the [National Natural Science Foundation of China] under Grant [No.51675378]; and the [National Science and Technology Major Project of China] under Grant [No. 2014ZX04014-031].