Evaluation of Three-Dimensional Surface Roughness in Microgroove Based on Bidimensional Empirical Mode Decomposition

Micromachines (Basel). 2022 Nov 18;13(11):2011. doi: 10.3390/mi13112011.

Abstract

Micromilling is an extremely important advanced manufacturing technology in the micromanufacturing industry. Compared with the traditional milling process, micromilling has stricter requirements on the surface roughness of the workpiece, and the roughness of the microcurved surface is not easy to measure. In order to more accurately characterize the curved surface morphology of the microgrooves obtained by micromilling, this paper proposes a method to extract the reference plane of the curved surface based on the bidimensional empirical mode decomposition algorithm and characterize the three-dimensional surface roughness of the curved surface. First, we synthesize the morphologies of the microgrooves by simulated non-Gaussian rough surfaces and models of textures. Second, the bidimensional empirical mode decomposition algorithm was used to extract the reference planes of the simulated microgrooves. Third, the three-dimensional roughness parameters suitable for the curved surfaces of microgrooves were selected to establish an evaluation system. The results show that the mean squared errors of the reference planes are below 1%, so bidimensional empirical mode decomposition can effectively extract reference planes, and the evaluation system of three-dimensional surface roughness proposed in this paper reflects morphological characteristics of the curved surfaces of microgrooves more thoroughly than that of two-dimensional surface roughness parameters.

Keywords: bidimensional empirical mode decomposition; curved surface of the microgroove; extraction of the reference plane; non-Gaussian surface; three-dimensional surface roughness.

Grants and funding

This research funders are Huadong Yu and Jinkai Xu, and is supported by ten projects, they are: the Jilin Key Research and Development Project: 20210201112GX, and the Science Fund for Youth Scholar of Changchun University of Science Technology (No. XQNJJ-2018-09), and Jilin innovation and entrepreneurship talent funding project (No. 2021Z002), and the National Natural Science Foundation of China (grant no. U19A20103), and the National Key Research and Development Program of China with Grant Number 2018YFB1107400, and the National Key Research and Development Plan Project (No. 2018YFB1107403), and the “111” Project of China (No. D17017), and Jilin Province Scientific and Technological Development Program (No. 20190101005JH, No. 20180201057GX, No. 20190302076GX).