Iterative modeling of grain size and force during ultrasonic vibratory–assisted grinding SiCp/Al composites.
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| Title: | Iterative modeling of grain size and force during ultrasonic vibratory–assisted grinding SiCp/Al composites. |
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| Authors: | jin, Jinghao1 (AUTHOR), Zhao, Man1,2,3 (AUTHOR) zhaoman@sues.edu.cn, Mao, Jian1,2,3 (AUTHOR), Liu, Gang1,2 (AUTHOR), Zhang, Liqiang1,2 (AUTHOR), Feng, Yixuan4 (AUTHOR), Liang, Steven Y.4 (AUTHOR) |
| Source: | International Journal of Advanced Manufacturing Technology. Jun2025, Vol. 138 Issue 7, p3559-3574. 16p. |
| Subjects: | Grain size, Tangential force, Thermal conductivity, Silicon carbide, Model validation |
| Abstract: | Aluminum matrix composites reinforced with silicon carbide particles (SiCp/Al) are widely used in aerospace fields with excellent properties such as high specific strength, high specific stiffness, and high thermal conductivity. The SiCp/Al composite, characterized by its multiphase architecture comprising dissimilar constituent phases, presents significant machining challenges that stem from intrinsic heterogeneous deformation behavior, and the microstructure of the material is one of the determining factors of the life of the workpiece, so the grinding mechanism considering microstructure evolution should be investigated. Therefore, the grinding force model and grain size evolution model of ultrasonic vibration–assisted grinding (UVAG) SiCp/Al composites are constructed in this paper. On the basis, the grinding force-heat model and the grain size evolution model are dynamically iterated to obtain the grain size evolution trend and the relationship of process parameters–grain size evolution–grinding force. Then the orthogonal grinding experiments were systematically designed and executed, with model validation conducted through ultrasonic vibration–assisted grinding tests under controlled conditions. The results showed that the error of the grinding force model considering microstructure evolution is less than 10%. Parametric sensitivity analysis identified depth of cut as the predominant influencing factor, contributing variance in tangential forces and normal forces. Finally, EBSD detection was performed to analyze the effect of grinding parameters on grain size and verify the accuracy of the grain size model of this material, and the model error is about 6.37%. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Aluminum matrix composites reinforced with silicon carbide particles (SiCp/Al) are widely used in aerospace fields with excellent properties such as high specific strength, high specific stiffness, and high thermal conductivity. The SiCp/Al composite, characterized by its multiphase architecture comprising dissimilar constituent phases, presents significant machining challenges that stem from intrinsic heterogeneous deformation behavior, and the microstructure of the material is one of the determining factors of the life of the workpiece, so the grinding mechanism considering microstructure evolution should be investigated. Therefore, the grinding force model and grain size evolution model of ultrasonic vibration–assisted grinding (UVAG) SiCp/Al composites are constructed in this paper. On the basis, the grinding force-heat model and the grain size evolution model are dynamically iterated to obtain the grain size evolution trend and the relationship of process parameters–grain size evolution–grinding force. Then the orthogonal grinding experiments were systematically designed and executed, with model validation conducted through ultrasonic vibration–assisted grinding tests under controlled conditions. The results showed that the error of the grinding force model considering microstructure evolution is less than 10%. Parametric sensitivity analysis identified depth of cut as the predominant influencing factor, contributing variance in tangential forces and normal forces. Finally, EBSD detection was performed to analyze the effect of grinding parameters on grain size and verify the accuracy of the grain size model of this material, and the model error is about 6.37%. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02683768 |
| DOI: | 10.1007/s00170-025-15629-1 |
