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.

中文翻译：

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一种改进的加工表面形貌分解算法

摘要 为了准确分解加工表面的表面形貌并追踪机器的潜在误差，提出了一种综合改进算法，将小波包分解（WPD）和自适应噪声的改进完全集成经验模态分解（Improve CEEMDAN）相结合。 . 首先利用代价函数寻找最优小波包基，得到最优分解树。其次，在半硬阈值去噪下，最优分解树得到的小波系数可以生成去噪信号。最后对白噪声进行预处理，得到上限频率和频带白噪声，完成CEEMDAN的改进。改进后的CEEMDAN用于分解去噪后的信号，得到一系列固有模式函数（IMF）。这种综合改进算法的优点是通过自适应地寻找最优小波包基并加入带限白噪声来提高计算效率和分解精度。仿真和实验结果表明，该综合算法在地表地形分解中具有可行性、有效性和更高的精度。