Abstract
Curved surface parts have been widely used in aerospace, automobile, and other fields due to their excellent features in aerodynamics, hydrodynamics, and thermodynamics. Compared with surface modeling technology, the development of surface processing technology is slightly behind. In order to improve the processing quality and efficiency of curved surface, a five-axis CNC polishing machine tool is developed, and the pre-processing and post-processing of machining the curved surface are studied. Taking a metal shell of the mobile phone as an example, three different paths are planned for the flat surface and the curved surface respectively, and a method of generating the tool path is proposed. The kinematics model of the five-axis CNC machine tool is established, which is used to obtain the calculation formula of the movement amount of each axis when polishing on the flat surface and the curved surface of the metal shell. The polishing effects of the different paths on the surface quality and the polishing efficiency of the metal shell are studied through the polishing experiments, and the polishing path with the best surface quality and the highest polishing efficiency is found.
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The datasets analyzed during the current study are available from the corresponding author on reasonable request.
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Funding
Supported by the National Natural Science Foundation of China (Grant No. 51975392 and 51775360), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 19KJA220001), the Postdoctoral Science Foundation of China (Grant No. 2015M571800), and the Natural Science Foundation of Jiangsu Province (Grant No. BK20201412).
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Investigation: CD and CF. Project administration: CF and LZ. Validation: CF. Writing — original draft: CF and CD. Writing — review and editing: LZ. All authors read and approved the final manuscript.
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Zhang, L., Ding, C., Fan, C. et al. Process planning of the automatic polishing of the curved surface using a five-axis machine tool. Int J Adv Manuf Technol 120, 7205–7218 (2022). https://doi.org/10.1007/s00170-022-09226-9
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DOI: https://doi.org/10.1007/s00170-022-09226-9