A high-precision prediction model for surface topography of abrasive belt grinding considering elastic contact

Abrasive belt grinding is a commonly used machining technique for many key components, especially on aerospace equipment. The surface roughness results from a belt grinding process are essential for the fatigue performance of workpieces; however, little research was reported on the surface topograph...

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Veröffentlicht in:International journal of advanced manufacturing technology 2023-03, Vol.125 (1-2), p.777-792
Hauptverfasser: Liu, Ying, Song, Shayu, Xiao, Guijian, Huang, Yun, Zhou, Kun
Format: Artikel
Sprache:eng
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Zusammenfassung:Abrasive belt grinding is a commonly used machining technique for many key components, especially on aerospace equipment. The surface roughness results from a belt grinding process are essential for the fatigue performance of workpieces; however, little research was reported on the surface topography generation of abrasive belt grinding. This study proposed a modeling and simulation method for the surface topography of abrasive belt grinding considering its elastic contact. Abrasive grain cloud forming tool was established combining standard grains number with Gaussian distribution. Workpiece surface and tool trajectory were discretized based on Brinell theory, which considered the microscopic cutting effects. Besides, Hertz theory is used to calculate the elastic deformation of rubber contact wheel with the selection of active abrasive grains and redistribution of forces in contact area; as a result, the interaction between multiple abrasive grains and workpiece could be simulated. Moreover, to verify the proposed method, numerical simulations and abrasive belt grinding experiments were conducted and compared. The results show that the minimum profile gap and error can reach 0.003 mm and 1.03% in terms of material removal depth and surface profile, and the error calculation is deeply affected by the denominator (simulation results). The grinding surface texture simulated by the model agrees well with the experimental observations, and the average predicting error of surface roughness is 3.25%.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-022-10757-4