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<p>To investigate the wear behavior of polycrystalline diamond (PCD) tools during the milling of high-volume-fraction SiC particle-reinforced aluminum matrix composites (high-volume-fraction SiC<sub>p</sub>/Al) and to realize tool wear prediction, this study adopts a combination of orthogonal experiments and multiple linear regression analysis. Key factors affecting tool wear were obtained, and a tool wear prediction model was established, which was verified using the F-test-and R² tests. The results show that spindle speed is the primary factor influencing PCD tool wear, feed per tooth is negatively correlated with tool wear, and the effect of milling depth on surface roughness can be neglected. The established multiple linear regression model exhibited a good fitting performance with a coefficient of determination of R²=0.9916, and the average relative error between the predicted and measured tool wear values was within 5%. The optimal milling parameters for minimum tool wear were determined as follows: spindle speed <i>n</i> = 1000 r∙min<sup>− 1</sup>, feed per tooth <i>f</i><sub>z</sub>=0.1&#xa0;mm∙z<sup>− 1</sup>, and milling depth <i>a</i><sub><i>p</i></sub>=0.2&#xa0;mm. Under these parameters, the tool wear was reduced by up to 71.87% compared with the maximum value in the orthogonal test groups. The experimental results demonstrate that a reasonable selection of milling parameters in practical milling can effectively reduce workpiece surface roughness and tool wear, thereby extending the tool service life. This study provides a reference for the high-efficiency and high-precision machining of high-volume-fraction SiC<sub>p</sub>/Al composites.</p>

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Research on milling parameter optimization and wear of PCD tools for SiCp/Al composites

  • 期刊论文

  • 发表日期：

    2026年5月9日

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  • Qingqing Lü,
  • Kun Zhao,
  • Liquan Yang,
  • Erbo Liu,
  • Guangxi Li,
  • Daohui Xiang

摘要

To investigate the wear behavior of polycrystalline diamond (PCD) tools during the milling of high-volume-fraction SiC particle-reinforced aluminum matrix composites (high-volume-fraction SiCp/Al) and to realize tool wear prediction, this study adopts a combination of orthogonal experiments and multiple linear regression analysis. Key factors affecting tool wear were obtained, and a tool wear prediction model was established, which was verified using the F-test-and R² tests. The results show that spindle speed is the primary factor influencing PCD tool wear, feed per tooth is negatively correlated with tool wear, and the effect of milling depth on surface roughness can be neglected. The established multiple linear regression model exhibited a good fitting performance with a coefficient of determination of R²=0.9916, and the average relative error between the predicted and measured tool wear values was within 5%. The optimal milling parameters for minimum tool wear were determined as follows: spindle speed n = 1000 r∙min− 1, feed per tooth fz=0.1 mm∙z− 1, and milling depth ap=0.2 mm. Under these parameters, the tool wear was reduced by up to 71.87% compared with the maximum value in the orthogonal test groups. The experimental results demonstrate that a reasonable selection of milling parameters in practical milling can effectively reduce workpiece surface roughness and tool wear, thereby extending the tool service life. This study provides a reference for the high-efficiency and high-precision machining of high-volume-fraction SiCp/Al composites.