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

被引:10
作者
Liu, Ying [1 ,2 ]
Song, Shayu [1 ]
Xiao, Guijian [1 ,2 ]
Huang, Yun [1 ,2 ]
Zhou, Kun [1 ,2 ]
机构
[1] Chongqing Univ, Coll Mech Engn, 174 Shazhengjie, Chongqing 400044, Peoples R China
[2] Chongqing Univ, State Key Lab Mech Transmiss, 174, Shazhengjie St, Chongqing 400044, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Abrasive belt grinding; Surface roughness; Elastic contact; Surface topography; SIMULATION; ROUGHNESS; GRAIN; GENERATION;
D O I
10.1007/s00170-022-10757-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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%.
引用
收藏
页码:777 / 792
页数:16
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