铝硅合金变质条件下凝固过程及模拟的研究
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摘要
铸造Al-Si合金广泛应用于铸造形状复杂的薄壁零件、航空工业承受高静载荷零件、气缸体、气缸盖等,是一种重要的工业合金。Al-Si合金的凝固过程是影响铸件质量的关键环节之一,因此有必要对其进行深入了解。本文通过试验获取数据并对试验数据进行统计分析,建立了Al-Si合金在变质条件下的过冷度预测模型和微观组织的定量模型,并研究了Al-Si合金的潜热释放规律。通过计算机数值模拟技术研究Al-Si合金的凝固过程及其微观组织的形成过程,能够起到辅助优化生产工艺,改善铸件质量,缩短研发周期的作用。
过冷度是凝固过程中备受关注的主要问题之一。论文针对金属型条件下经过变质的Al-Si合金的凝固特点,建立了其在不同凝固阶段(初生相阶段、共晶相阶段)的过冷度预测性模型,该模型与冷却速度密切相关的。本文选取了si的质量分数在7%-17%的Al-Si合金作为试验合金,并通过设计具有不同厚度阶梯的阶梯型铸件模具,实现了Al-Si合金在不同冷却速度条件下的凝固过程。在Al-Si合金的凝固过程中,本文使用温度采集模块对各个阶梯中心部位的温度进行了测量和记录。通过计算机辅助分析冷却曲线,分析Al-Si合金凝固过程中形核过冷度与冷却速度的关系,进而建立了相关的过冷度预测性模型。
当前合金的力学性能预测仍然基于合金的微观组织。本文通过定量分析冷却速度和金相组织,考察了在Al-Sr中间合金变质条件下亚共晶Al-Si合金和共晶Al-Si合金的初生α相的二次枝晶间距(SDAS)的变化规律,对已有的SDAS模型进行了改进,建立了Sr变质条件下的SDAS预测模型。此外,本文还考察了A1-P中间合金变质条件下过共晶Al-Si合金的初生si的形核数量,分析了不同冷速条件下的初生Si的面密度和体密度。
所有的Al-Si合金凝固过程都要经历共晶阶段,因此,建立基于共晶团形核生长的确定性模型是十分必要的。为了使数值模拟计算既能保证一定的计算效率,又能通过微观组织模拟较好地反映凝固过程,本文在计算过程中,对初生相凝固阶段仍然采用宏观的计算方法,而对共晶相凝固阶段则建立了微观组织的确定性模型,采用宏微观结合的计算方法进行模拟计算。通过分析试验数据,确定了形核模型和生长模型需要的基本参数范围,并在模拟计算中对参数进行验证,确定了共晶组织微观模拟计算需要的较为准确的参数数据。
凝固潜热是影响传热过程数值模拟结果准确性的重要因素。本文在分析Al-Si合金潜热释放特点的基础上,建立了根据Si含量计算Al-Si合金潜热的计算模型。本文通过两种方法研究Al-Si合金的潜热释放规律。第一种方法是计算机辅助冷却曲线分析法(CA-CCA).通过分析符合牛顿冷却条件的CA-CCA试验数据,发现CA-CCA方法虽然可以计算其潜热,然而该潜热的误差较大,因此,本文建议不采用由此计算的潜热数值进行模拟计算；但是该方法提供了固相率变化等相关信息,有助于辅助认识和分析实际的凝固过程。另一种方法是差示扫描量热法(DSC)。首先,对高纯Al-Si合金进行了大量的DSC试验研究,经过分析发现Al-Si合金的潜热值在共晶成分两侧分别呈现线性规律,由此建立了Al-Si合金的潜热计算公式。其次,对试验铸件的测温部位取样进行DSC试验,经过分析发现获得的潜热数值与高纯Al-Si合金的潜热数值存在一定差距,但其差值方面仍然具有规律性,因此,可以根据高纯Al-Si合金的潜热公式对其进行估算。
为了解决目前宏微观模拟耦合计算中存在的耗时长的问题,本文基于上述的过冷度预测模型,建立了初生相采用宏观模拟方法和共晶相采用宏微观相结合的模拟方法,即对Al-Si合金的初生相凝固过程采用Scheil方程处理固相率的变化,而对共晶相的凝固过程则建立了相应的确定性计算模型处理相应的形核和生长问题。基于Windows XP的系统平台,本文通过Visual C++6.0编程实现了该模型对试验铸件的模拟计算。其中,共晶Al-Si合金的计算结果与试验结果具有较好的一致性；而对于拥有初生相的Al-Si合金的凝固计算,虽然其冷却速度变化与试验结果有一定差距,但总体的凝固时间上仍然具有准确性。本文根据计算的凝固时间和建立的SDAS预测模型,实现了对Al-Si合金微观组织的预测。本文采用的的部分宏观计算,部分宏微观耦合的计算方法忽略了微观组织形貌等相关问题,但其计算效率相对于宏微观完全耦合的计算模拟却得到了大幅度的提高,基于建立的SDAS预测模型对微观组织的预测结果也较为准确,从而可以更好的面向工程实际。通过计算结果与实验结果的对比,也分析了当前计算模型中存在的不足之处,提出以后工作可能的发展方向。
Aluminum silicon alloy is one of the important commercial alloys, and its castings are widely used for different purposes, especially for castings with complicated shapes, ranging from thin wall components and high static loads parts used for aviation industry to cylinder blocks and cylinder head. In order to get better casting products, it has warranted a more thorough understanding of the solidification process of aluminum silicon alloys. Based on the statistical analysis of the experimental data, the undercooling prediction models and the micro structure quantitative models of the modified aluminum silicon alloys are developed. The solidification process and micro structure evolution are studied by numerical simulation so as to optimize processing technique, improve casting quality and shorten design cycles.
The nucleation undercooling is one of the problems paid more attention by people in the solidification process. With the study of the solidification characteristics of aluminum silicon alloys, which are modified and solidified in permanent mold, the undercooling prediction models during different solidification stages have been developed and these models are related to cooling rate. To investigate this question, the aluminum silicon alloys with silicon content from7wt%to17wt%were selected for experiment. The different cooling rates are realized by a step casting mold whose steps are in different thickness. The temperature of the center of each step was measured and recorded by a data acquisition system. The relationship between nucleation undercooling and cooling rate during the melting solidification has been analyzed with the help of computer aided cooling curve analysis technique, then the prediction models have been achieved.
Because of the fact that the alloy performance prediction was based on the microstructure, the rules of the secondary dendrite arm spacings of hypoeutectic and eutectic aluminum silicon alloys modified by Al-Sr master alloy were studied by means of cooling rate data and metallographic quantitative analysis techniques. Meanwhile, the nucleation numbers of primary silicon phase of hypereutectic aluminum silicon alloys modified by Al-P master alloy were measured and the area density and body density of primary silicon phase with different cooling rates have been determined.
The deterministic models for the nucleation and growth of eutectic cells have been founded for the eutectic solidification stage of aluminum silicon alloys. For the purposes of both computation efficiency and microstructure evolution during solidification process, the macro method was used for the primary phase stages and the macro method coupled with the micro deterministic method was used for the eutectic phase stage in this study. The parameters for the nucleation models and the growth models were determined by the experimental data and validated by numerical simulation.
Latent heat has an impact on the simulation accuracy of the thermal field. A latent heat computation model of aluminum silicon alloy has been created based on the silicon content in the alloys. Two different methods were used for studying the latent heat release rules of aluminum silicon alloys. One was Computer Aided-Cooling Curve Analysis method (CA-CCA). The results of the CA-CCA experiment showed that this method gave users a latent heat value with larger relative error, which could not be used for numerical simulation. However, this method provided information about fraction of solid and so on that can help us to better understand the solidification process. The other method is Differential Scanning Calorimeter method (DSC). The high purity aluminum silicon alloys were first used for DSC experiment. It is found that there is a linear relationship for the latent heat of hypoeutectic or hypereutectic alloy and a mathematical formula has been obtained. The experimental castings were also carried out the DSC experiment for each step center. The values of them are smaller than the ones of high purity alloys but show regular variations so that it could also be estimated by the latent heat formula mentioned above.
In order to shorten the computation time of the macro-micro coupled method, the macro method for the primary phase and the macro-micro coupled method for the eutectic phase were used for computation based on the undercooling prediction models developed in this study. Then the Scheil equation was used to deal with the fraction of solid of the primary phase solidification and the micro models of nucleation and growth developed in this study were employed for the eutectic phase solidification. The program was developed on Windows XP and the programming language was C++. The simulation results for the eutectic alloys have been verified by good consistence of calculated results with experimental ones. For the alloys with primary phase, there is a difference on the cooling rate variations but the solidification time also shows a certain degree of accuracy. With the SDAS models the values of SDAS could also be good predicted. This method neglected the problems such as micro structure patterns and improved the computation efficiency. The good prediction results could also be applied for industry application. According to the comparison of the results, the disadvantage of the models was also be analyzed and some of the future research directions have been pointed out.
过冷度是凝固过程中备受关注的主要问题之一。论文针对金属型条件下经过变质的Al-Si合金的凝固特点,建立了其在不同凝固阶段(初生相阶段、共晶相阶段)的过冷度预测性模型,该模型与冷却速度密切相关的。本文选取了si的质量分数在7%-17%的Al-Si合金作为试验合金,并通过设计具有不同厚度阶梯的阶梯型铸件模具,实现了Al-Si合金在不同冷却速度条件下的凝固过程。在Al-Si合金的凝固过程中,本文使用温度采集模块对各个阶梯中心部位的温度进行了测量和记录。通过计算机辅助分析冷却曲线,分析Al-Si合金凝固过程中形核过冷度与冷却速度的关系,进而建立了相关的过冷度预测性模型。
当前合金的力学性能预测仍然基于合金的微观组织。本文通过定量分析冷却速度和金相组织,考察了在Al-Sr中间合金变质条件下亚共晶Al-Si合金和共晶Al-Si合金的初生α相的二次枝晶间距(SDAS)的变化规律,对已有的SDAS模型进行了改进,建立了Sr变质条件下的SDAS预测模型。此外,本文还考察了A1-P中间合金变质条件下过共晶Al-Si合金的初生si的形核数量,分析了不同冷速条件下的初生Si的面密度和体密度。
所有的Al-Si合金凝固过程都要经历共晶阶段,因此,建立基于共晶团形核生长的确定性模型是十分必要的。为了使数值模拟计算既能保证一定的计算效率,又能通过微观组织模拟较好地反映凝固过程,本文在计算过程中,对初生相凝固阶段仍然采用宏观的计算方法,而对共晶相凝固阶段则建立了微观组织的确定性模型,采用宏微观结合的计算方法进行模拟计算。通过分析试验数据,确定了形核模型和生长模型需要的基本参数范围,并在模拟计算中对参数进行验证,确定了共晶组织微观模拟计算需要的较为准确的参数数据。
凝固潜热是影响传热过程数值模拟结果准确性的重要因素。本文在分析Al-Si合金潜热释放特点的基础上,建立了根据Si含量计算Al-Si合金潜热的计算模型。本文通过两种方法研究Al-Si合金的潜热释放规律。第一种方法是计算机辅助冷却曲线分析法(CA-CCA).通过分析符合牛顿冷却条件的CA-CCA试验数据,发现CA-CCA方法虽然可以计算其潜热,然而该潜热的误差较大,因此,本文建议不采用由此计算的潜热数值进行模拟计算；但是该方法提供了固相率变化等相关信息,有助于辅助认识和分析实际的凝固过程。另一种方法是差示扫描量热法(DSC)。首先,对高纯Al-Si合金进行了大量的DSC试验研究,经过分析发现Al-Si合金的潜热值在共晶成分两侧分别呈现线性规律,由此建立了Al-Si合金的潜热计算公式。其次,对试验铸件的测温部位取样进行DSC试验,经过分析发现获得的潜热数值与高纯Al-Si合金的潜热数值存在一定差距,但其差值方面仍然具有规律性,因此,可以根据高纯Al-Si合金的潜热公式对其进行估算。
为了解决目前宏微观模拟耦合计算中存在的耗时长的问题,本文基于上述的过冷度预测模型,建立了初生相采用宏观模拟方法和共晶相采用宏微观相结合的模拟方法,即对Al-Si合金的初生相凝固过程采用Scheil方程处理固相率的变化,而对共晶相的凝固过程则建立了相应的确定性计算模型处理相应的形核和生长问题。基于Windows XP的系统平台,本文通过Visual C++6.0编程实现了该模型对试验铸件的模拟计算。其中,共晶Al-Si合金的计算结果与试验结果具有较好的一致性；而对于拥有初生相的Al-Si合金的凝固计算,虽然其冷却速度变化与试验结果有一定差距,但总体的凝固时间上仍然具有准确性。本文根据计算的凝固时间和建立的SDAS预测模型,实现了对Al-Si合金微观组织的预测。本文采用的的部分宏观计算,部分宏微观耦合的计算方法忽略了微观组织形貌等相关问题,但其计算效率相对于宏微观完全耦合的计算模拟却得到了大幅度的提高,基于建立的SDAS预测模型对微观组织的预测结果也较为准确,从而可以更好的面向工程实际。通过计算结果与实验结果的对比,也分析了当前计算模型中存在的不足之处,提出以后工作可能的发展方向。
Aluminum silicon alloy is one of the important commercial alloys, and its castings are widely used for different purposes, especially for castings with complicated shapes, ranging from thin wall components and high static loads parts used for aviation industry to cylinder blocks and cylinder head. In order to get better casting products, it has warranted a more thorough understanding of the solidification process of aluminum silicon alloys. Based on the statistical analysis of the experimental data, the undercooling prediction models and the micro structure quantitative models of the modified aluminum silicon alloys are developed. The solidification process and micro structure evolution are studied by numerical simulation so as to optimize processing technique, improve casting quality and shorten design cycles.
The nucleation undercooling is one of the problems paid more attention by people in the solidification process. With the study of the solidification characteristics of aluminum silicon alloys, which are modified and solidified in permanent mold, the undercooling prediction models during different solidification stages have been developed and these models are related to cooling rate. To investigate this question, the aluminum silicon alloys with silicon content from7wt%to17wt%were selected for experiment. The different cooling rates are realized by a step casting mold whose steps are in different thickness. The temperature of the center of each step was measured and recorded by a data acquisition system. The relationship between nucleation undercooling and cooling rate during the melting solidification has been analyzed with the help of computer aided cooling curve analysis technique, then the prediction models have been achieved.
Because of the fact that the alloy performance prediction was based on the microstructure, the rules of the secondary dendrite arm spacings of hypoeutectic and eutectic aluminum silicon alloys modified by Al-Sr master alloy were studied by means of cooling rate data and metallographic quantitative analysis techniques. Meanwhile, the nucleation numbers of primary silicon phase of hypereutectic aluminum silicon alloys modified by Al-P master alloy were measured and the area density and body density of primary silicon phase with different cooling rates have been determined.
The deterministic models for the nucleation and growth of eutectic cells have been founded for the eutectic solidification stage of aluminum silicon alloys. For the purposes of both computation efficiency and microstructure evolution during solidification process, the macro method was used for the primary phase stages and the macro method coupled with the micro deterministic method was used for the eutectic phase stage in this study. The parameters for the nucleation models and the growth models were determined by the experimental data and validated by numerical simulation.
Latent heat has an impact on the simulation accuracy of the thermal field. A latent heat computation model of aluminum silicon alloy has been created based on the silicon content in the alloys. Two different methods were used for studying the latent heat release rules of aluminum silicon alloys. One was Computer Aided-Cooling Curve Analysis method (CA-CCA). The results of the CA-CCA experiment showed that this method gave users a latent heat value with larger relative error, which could not be used for numerical simulation. However, this method provided information about fraction of solid and so on that can help us to better understand the solidification process. The other method is Differential Scanning Calorimeter method (DSC). The high purity aluminum silicon alloys were first used for DSC experiment. It is found that there is a linear relationship for the latent heat of hypoeutectic or hypereutectic alloy and a mathematical formula has been obtained. The experimental castings were also carried out the DSC experiment for each step center. The values of them are smaller than the ones of high purity alloys but show regular variations so that it could also be estimated by the latent heat formula mentioned above.
In order to shorten the computation time of the macro-micro coupled method, the macro method for the primary phase and the macro-micro coupled method for the eutectic phase were used for computation based on the undercooling prediction models developed in this study. Then the Scheil equation was used to deal with the fraction of solid of the primary phase solidification and the micro models of nucleation and growth developed in this study were employed for the eutectic phase solidification. The program was developed on Windows XP and the programming language was C++. The simulation results for the eutectic alloys have been verified by good consistence of calculated results with experimental ones. For the alloys with primary phase, there is a difference on the cooling rate variations but the solidification time also shows a certain degree of accuracy. With the SDAS models the values of SDAS could also be good predicted. This method neglected the problems such as micro structure patterns and improved the computation efficiency. The good prediction results could also be applied for industry application. According to the comparison of the results, the disadvantage of the models was also be analyzed and some of the future research directions have been pointed out.
引文
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