4.6 Article

Parametric Studies on Finishing of AZ31B Magnesium Alloy with Al2O3 Magnetic Abrasives Prepared by Combining Plasma Molten Metal Powder with Sprayed Abrasive Powder

期刊

MICROMACHINES
卷 13, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/mi13091369

关键词

magnetic abrasive finishing; Al2O3 magnetic abrasive; AZ31B magnesium alloy; RSM

资金

  1. National Natural Science Foundation of China [51875328]
  2. Natural Science Foundation of Shandong Province [ZR2019MEE013]

向作者/读者索取更多资源

This study utilizes high-performance iron-based Al2O3 magnetic abrasive powder for magnetic abrasive finishing of AZ31B magnesium alloy, designed a composite magnetic pole processing device through force analysis, and conducted experiments using Response Surface Methodology to establish a surface roughness model with good predictive ability.
High-performance iron-based Al2O3 magnetic abrasive powder (MAP) prepared by combining plasma molten metal powder with sprayed abrasive powder is used for magnetic abrasive finishing (MAF) of AZ31B magnesium alloy to remove surface defects such as creases, cracks, scratches, and pits generated during the manufacturing process of the workpiece, and to reduce surface roughness and improve its wear and corrosion resistance. In order to solve the problem of magnetic abrasive powder splash in the MAF process, the force analysis of the MAP in the processing area is conducted, and a composite magnetic pole processing device was designed and simulated to compare the effects of both devices on MAF, confirming the feasibility of composite magnetic pole grinding. Then, experiments have been designed using Response Surface Methodology (RSM) to investigate the effect of four factors-magnetic pole rotation speed, grinding gap, magnetic pole feed rate, magnetic abrasive filling quantity-on surface roughness and the interactions between them. The minimum surface roughness value that can be obtained is used as the index for parameter optimization, and the optimized parameters are used for experiments, and the results show that the established surface roughness model has good predictive ability.

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