CN 41-1243/TG ISSN 1006-852X
Volume 45 Issue 3
Jun.  2025
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Article Contents
LIU Bingyang, DING Yunlong, SHAO Wenjie, HAN Bing, CHEN Yan. Magnetic particle grinding and finishing test of mixed particle size abrasives[J]. Diamond & Abrasives Engineering, 2025, 45(3): 377-384. doi: 10.13394/j.cnki.jgszz.2024.0078
Citation: LIU Bingyang, DING Yunlong, SHAO Wenjie, HAN Bing, CHEN Yan. Magnetic particle grinding and finishing test of mixed particle size abrasives[J]. Diamond & Abrasives Engineering, 2025, 45(3): 377-384. doi: 10.13394/j.cnki.jgszz.2024.0078

Magnetic particle grinding and finishing test of mixed particle size abrasives

doi: 10.13394/j.cnki.jgszz.2024.0078
More Information
  • Received Date: 2024-04-25
  • Accepted Date: 2024-06-24
  • Rev Recd Date: 2024-06-07
  •   Objectives  Magnetic particle grinding finishing technology as an advanced processing technology can achieve high precision surface treatment. In order to simplify the test device, reduce the test cost and improve the processing effect, the single particle size abrasive is changed to mixed particle size abrasive without changing the test device, so as to improve the grinding effect of magnetic particles.  Methods  Finite element analysis software is used to simulate the magnetic field in the machining area, and the magnetic field data is imported into the discrete element simulation software through an API interface to obtain the magnetic field during the simulation process, in order to simulate the force situation of mixed particle size abrasive and single particle size abrasive during machining. Taking the spindle speed of the machine tool (A), the abrasive mass ratio (B) and the abrasive particle size ratio (C) as the research objects, the experimental parameters are analyzed and optimized using response surface methodology. To prevent abrasive splashing and reduce the grinding effect, the selected experimental parameters ranges are 400 to 600 r/min for A, 0.50 to 2.00 for B, and 1.5 to 2.5 for C. Using the surface roughness Ra of the workpiece as the response value, the Box Behnken method is used for response surface test design.  Results  The P-value of the variance analysis of the model experiment results is less than 0.000 1, indicating that the experimental model is highly significant. The mismatch term refers to the unexplained error in the model, with a P-value of 0.458 1, much greater than 0.050 0, indicating that the mismatch term is not significant and the regression equation fitted by the software is valid. At the same time, the multiple correlation coefficient R2 is 0.996 2, and R2Adj is 0.989 4 after verification, which is very close to 1.000 0, indicating a good fit of the model. Moreover, the surface roughness is affected by the spindle speed, abrasive mass ratio and abrasive particle size ratio to 98.94%. The results of the single factor experiment indicate that the order of influence on surface roughness Ra is the spindle speed, followed by the abrasive mass ratio and the abrasive particle size ratio. When the spindle speed is 500 r/min and the abrasive mass ratio is 1.25, the workpiece surface roughness Ra reaches the minimum value. In the case of a certain abrasive mass ratio, when the abrasive particle size ratio is 2.0 and the spindle speed is 500 r/min, the workpiece surface roughness Ra reaches its minimum value. Under the condition of constant spindle speed, when the abrasive mass ratio is 0.50 and the abrasive particle size ratio is 2.5, the workpiece surface roughness Ra reaches the maximum. However, when the abrasive particle size ratio is 2.0 and the appropriate mass ratio is 1.25, the surface roughness Ra of the workpiece can reach the minimum value. Aiming at the minimum surface roughness Ra of the workpiece, the response surface software is used to optimize the data, and the optimal process parameter combination for workpiece processing is obtained, that is, the spindle speed is 511 r/min, the abrasive mass ratio is 1.67, the abrasive particle size ratio is 1.9, and the predicted surface roughness Ra value after processing is 0.038 µm. When the workpiece is machined under the optimal process parameters, the surface roughness Ra of the workpiece decreases from the original value of 0.244 μm to the test value of 0.036 μm, and the absolute value of relative error between the two is 5.26%.  Conclusions  The experimental results show that the established model is effective, and the process parameters that affect the surface roughness Ra of the workpiece are in the order of spindle speed, followed by abrasive mass ratio and abrasive particle size ratio. Compared to single-abrasive magnetic particle grinding, the use of mixed abrasives can further reduce the surface roughness of the workpiece and improve its machining effect.

     

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