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镁合金及其复合材料作为重要的轻质材料在汽车和航空工业中的应用一直引人注目。由于镁不能形成任何稳定的碳化物,SiC颗粒被大量用于增强镁基复合材料。AZ91/SiC复合材料凝固后固态晶粒密度NV是合金液态最大过冷度ΔT的函数。这类函数决定于异质形核位置特征与合金中存在的SiC数量。本文的目的是为描述初生相晶粒密度与过冷度关系的模型选择参数。这一模型与基于化学元素扩散和晶界动力学的结晶模式有关,能够用于预测铸件质量及其显微组织。形核模型通常有未知确切值的参数,甚至其物理意义都是待讨论的,对试验数据进行分析之后可以获得这些参数。制备了SiC强化的AZ91合金基复合材料,其中SiC颗粒含量分别为0、1%、2%、3%和4%(质量分数)。把这种复合材料铸造成具有4种厚度的试验板。从热电偶测温点附近获取数据,分析不同复合材料与不同厚度试板的过冷度及其对晶粒尺寸的影响。显微组织和热分析给出的一套数据建立了晶粒尺寸与SiC颗粒质量分数及过冷度联系,这些数据用于近似形核模型的参数调节。获得的模型在模拟复合材料显微组织方面是非常有用的。
The use of magnesium alloys and their composites as important lightweight materials in the automotive and aerospace industries has drawn attention. Due to the inability of magnesium to form any stable carbides, SiC particles are widely used to reinforce magnesium-based composites. The solid state grain density NV after solidification of AZ91 / SiC composite is a function of the maximum undercooling ΔT of the alloy liquid. Such functions depend on the location of the heterogeneous nucleation sites and the amount of SiC present in the alloy. The purpose of this paper is to model selection parameters that describe the relationship between primary grain density and undercooling. This model is related to the crystallization pattern based on chemical element diffusion and grain boundary kinetics and can be used to predict the quality of castings and their microstructure. The nucleation model usually has parameters of unknown exact value, and even its physical meaning is to be discussed. After the experimental data are analyzed, these parameters can be obtained. SiC reinforced AZ91 alloy matrix composites were prepared, in which SiC particles content were 0,1%, 2%, 3% and 4% (mass fraction) respectively. This composite material was cast into a test plate having 4 thicknesses. Obtained data from the thermocouple near the temperature measurement point, the analysis of different composite materials and different thickness test plate undercooling and its impact on grain size. A set of data given by microstructure and thermal analysis establishes the relationship between grain size and SiC particle fraction and undercooling, and these data are used for parameter adjustment of the approximate nucleation model. The model obtained is very useful in simulating the microstructure of composite materials.