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Volume 43 Issue 6
Dec.  2023
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Article Navigation >  Diamond & Abrasives Engineering  > 2023  >  43(6): 712-719
ZHANG Zelin, ZHOU Hongming, FENG Ming, ZHANG Xianglei, CHEN Zhuojie. Process parameters optimization of zirconia ceramics polishing with magnetic compound fluid slurry[J]. Diamond & Abrasives Engineering, 2023, 43(6): 712-719. doi: 10.13394/j.cnki.jgszz.2023.0003
Citation: ZHANG Zelin, ZHOU Hongming, FENG Ming, ZHANG Xianglei, CHEN Zhuojie. Process parameters optimization of zirconia ceramics polishing with magnetic compound fluid slurry[J]. Diamond & Abrasives Engineering, 2023, 43(6): 712-719. doi: 10.13394/j.cnki.jgszz.2023.0003
shu

Process parameters optimization of zirconia ceramics polishing with magnetic compound fluid slurry

doi: 10.13394/j.cnki.jgszz.2023.0003

  • College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, Zhejiang, China

More Information
  • Received Date: 2023-01-04
  • Accepted Date: 2023-03-09
  • Rev Recd Date: 2023-02-26
    • Available Online: 2023-11-06
    Abstract
  • In order to improve the surface quality of zirconia ceramic workpieces, the magnetic compound fluid polishing tool were utilized. This was done to lessen the material's surface roughness, minimize surface and subsurface damage. With a focus on the effects of magnet speed, processing gap, and abrasive particle size in the polishing fluid on surface roughness and material removal rate, a 3-factor, 3-level orthogonal test was created using Taguchi's method. The weights of each factor on the two evaluation indices were then analyzed using ANOVA. The best process parameter combination for surface roughness was 300 r/min for the magnet speed, 0.5 mm for the processing gap, and 1.25 μm for the abrasive particle size; the best process parameter combination for material removal rate was 400 r/min for the magnet speed, 0.5 mm for the processing gap, and 2 μm for the abrasive particle size. With these processing parameters, the surface roughness can reach up to 4.5 nm, and the material removal rate can reach up to 0.117 μm/min. The optimization effect is significant. The polishing prediction model was developed using a BP neural network that has been genetic algorithm optimized. The prediction error was 3.948 4%.

     

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