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| Research on Surface Roughness Detection Index of Micro-Milling Based on Multi-Objective Optimization |
| LI Wenqin, XU Jinkai, YU Huadong, ZHANG Xianghui, LIU Qimeng, YU Zhanjiang |
| ( Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China ) |
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Abstract Since the fact that the surface topography of micro-milling is complex and difficult to evaluate accurately, a surface roughnessdetection index based on three-dimensional (3D) characterization is proposed. Firstly, on the basis of principal component analysis, the 3D surface roughness parameters Sa, Ssk and Sku are converted into gray correlation degree as a surface roughness detection index based on the gray correlation analysis method. Secondly, the response surface methodology (RSM) is used to establish a gray correlation degree model to analyze the influence of machining parameters on the GRG. Finally, the combination of optimal machining parameters is obtained and verified. The results show that the average relative error of the gray correlation degree model is 6.54%, the fitting accuracy is high and the prediction effect is good, which verifies the feasibility of the model. The GRG corresponding to the obtained optimal process parameter combination is increased by 15.27%, which realizes the purpose of surface roughness minimization and surface abnormal features minimization and proves the feasibility of the detection index.
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2020, 
