To solve the problem of difficult machining, the near-net shaped Al/SiCP composites with high volume fraction of SiC particles were fabricated by vacuum-pressure infiltration. The SiCP preform with a complex shape was...To solve the problem of difficult machining, the near-net shaped Al/SiCP composites with high volume fraction of SiC particles were fabricated by vacuum-pressure infiltration. The SiCP preform with a complex shape was prepared by gelcasting. Pure Al, Al4Mg, and Al4Mg2Si were used as the matrices, respectively. The results indicate that the optimal parameters of SiCP suspension in gelcasting process are pH value of 10, TMAH content of 0.5 wt.%, and solid loading of 52 vol.%. The Al matrix alloyed with Mg contributes to improving the interfacial wettability of the matrix and SiC particles, which increases the relative density of the composite. The Al matrix alloyed with Si is beneficial to inhibiting the formation of the detrimental Al4C3 phases. The Al4Mg2Si/SiCP composite exhibits high relative density of 99.2%, good thermal conductivity of 150 W·m^-1·K^-1, low coefficient of thermal expansion of 10.1×10^-6 K^-1, and excellent bending strength of 489 MPa.展开更多
The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such ...The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.展开更多
基金Project (CXZZ20140506150310438) supported by the Science and Technology Program of Shenzhen City, ChinaProject (2017GK2261) supported by the Science and Technology Program of Hunan Province, ChinaProject (2017zzts111) supported by the Fundamental Research Funds for the Central Universities, China。
文摘To solve the problem of difficult machining, the near-net shaped Al/SiCP composites with high volume fraction of SiC particles were fabricated by vacuum-pressure infiltration. The SiCP preform with a complex shape was prepared by gelcasting. Pure Al, Al4Mg, and Al4Mg2Si were used as the matrices, respectively. The results indicate that the optimal parameters of SiCP suspension in gelcasting process are pH value of 10, TMAH content of 0.5 wt.%, and solid loading of 52 vol.%. The Al matrix alloyed with Mg contributes to improving the interfacial wettability of the matrix and SiC particles, which increases the relative density of the composite. The Al matrix alloyed with Si is beneficial to inhibiting the formation of the detrimental Al4C3 phases. The Al4Mg2Si/SiCP composite exhibits high relative density of 99.2%, good thermal conductivity of 150 W·m^-1·K^-1, low coefficient of thermal expansion of 10.1×10^-6 K^-1, and excellent bending strength of 489 MPa.
文摘The present work is focused on optimization of machining characteristics of AI/SiCp composites. The machining characteristics such as specific energy, tool wear and surface roughness were studied. The parameters such as volume fraction of SiC, cutting speed and feed rate were considered. Artificial neural networks (ANN) was used to train and simulate the experimental data. Genetic algorithms (GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics. Validation of optimized results was also performed by confirmation experiments.
