CN 41-1243/TG ISSN 1006-852X
Volume 40 Issue 3
Jun.  2020
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QI Junde, CHEN Bing. Mechanism model of belt grinding considering single abrasive action[J]. Diamond & Abrasives Engineering, 2020, 40(3): 13-20. doi: 10.13394/j.cnki.jgszz.2020.3.0002
Citation: QI Junde, CHEN Bing. Mechanism model of belt grinding considering single abrasive action[J]. Diamond & Abrasives Engineering, 2020, 40(3): 13-20. doi: 10.13394/j.cnki.jgszz.2020.3.0002

Mechanism model of belt grinding considering single abrasive action

doi: 10.13394/j.cnki.jgszz.2020.3.0002
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  • Rev Recd Date: 2020-05-07
  • Available Online: 2022-04-06
  • To solve the problem of quantitative material removal in the grinding process, based on the theories and methods of elastic-plastic deformation, Hertz contact and probability statistics, the material removal mechanism model of belt grinding was constructed from the perspective of micro single abrasive. Firstly, the material removal mechanism of a single abrasive at different deformation stages of workpiece during grinding was analyzed, the grinding process was simplified according to the experimental calculation results, and the calculation method of force on a single abrasive particle was given. On this basis, based on the distribution function of the number of abrasive particles and the blade heights, the equilibrium equation of micro abrasive particles and macro grinding pressure was established by using the theory of probability and statistics, which was used to obtain the cutting depth distribution function of the particles. Combined with the material removal volume of a single abrasive particle, the total material removal model was constructed by integral operation. Finally, the maximum material removal depth was obtained through the TC4 alloy grinding experiment on the robotic grinding platform. The results show that the maximum material removal depth increases with the increase of grinding pressure and belt linear velocity, and decreases with the increase of belt feeding speed. The maximum relative error is 17.66% and the average relative error is 10.55% between the theoretical predicted value and the experimental value, which verifies the validity of the model.

     

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