首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Modeling and analysis of the effects of processing parameters on the performance characteristics in the high pressure die casting process of Al–SI alloys
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Modeling and analysis of the effects of processing parameters on the performance characteristics in the high pressure die casting process of Al–SI alloys

机译:Al-SI合金高压压铸工艺中工艺参数对性能特征影响的建模与分析

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摘要

The high pressure die casting (HPDC) process has achieved remarkable success in the manufacture of aluminum–silicon (Al–SI) alloy components for the modern metal industry. Mathematical models are proposed for the modeling and analysis of the effects of machining parameters on the performance characteristics in the HPDC process of Al–SI alloys which are developed using the response surface methodology (RSM) to explain the influences of three processing parameters (die temperature, injection pressure and cooling time) on the performance characteristics of the mean particle size (MPS) of primary silicon and material hardness (HBN) value. The experiment plan adopts the centered central composite design (CCD). The separable influence of individual machining parameters and the interaction between these parameters are also investigated by using analysis of variance (ANOVA). With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of both the mean particle size of primary silicon and its hardness value. Two main significant factors involved in the mean particle size of primary silicon are the die temperature and the cooling time. The injection pressure and die temperature also have statistically significant effect on microstructure and hardness.
机译:高压压铸(HPDC)工艺在制造用于现代金属工业的铝硅(Al-SI)合金组件中取得了显著成功。提出了数学模型,用于建模和分析加工参数对Al-SI合金HPDC工艺中的性能特征的影响,该模型是使用响应面方法(RSM)开发的,用于解释三个加工参数(模具温度)的影响,注射压力和冷却时间)对初生硅的平均粒径(MPS)和材料硬度(HBN)值的性能特性的影响。实验计划采用中心中央复合设计(CCD)。通过使用方差分析(ANOVA),还研究了各个加工参数的可分离影响以及这些参数之间的相互作用。在高达95%置信区间的实验值的情况下,对于实验结果而言,可以很好地给出原始硅的平均粒径及其硬度值的数学模型。原始硅的平均粒度涉及的两个主要重要因素是芯片温度和冷却时间。注射压力和模具温度对微观结构和硬度也有统计学上显着的影响。

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