摘要
The dry sliding wear behavior of AA6061 matrix composite reinforced with aluminium nitride particles（AlN） produced by stir casting process was investigated. A regression model was developed to predict the wear rate of the prepared composite. A four-factor, five-level central composite rotatable design matrix was used to minimize the number of experimental runs. The factors considered in this study were sliding velocity, sliding distance, normal load and mass fraction of AlN reinforcement in the matrix. The developed regression model was validated by statistical software SYSTAT 12 and statistical tools such as analysis of variance（ANOVA） and student＇s t test. It was found that the developed regression model could be effectively used to predict the wear rate at 95% confidence level. The influence of these factors on wear rate of AA6061/AlNp composite was analyzed using the developed regression model and predicted trends were discussed with the aid of worn surface morphologies. The regression model indicated that the wear rate of cast AA6061/AlNp composite decreased with an increase in the mass fraction of AlN and increased with an increase of the sliding velocity, sliding distance and normal load acting on the composite specimen.
以AA6061为基体、AlN颗粒为增强体,采用搅拌铸造工艺得到AA6061-T6/AlNp复合材料,研究了AA6061-T6/AlNp复合材料的干滑动磨损行为。开发回归模型来预测复合材料的磨损率。采用四因素、五水平的正交实验进行优化。实验因素包括滑动速度、滑动距离、荷载、增强体AlN颗粒的质量分数。采用SYSTAT 12软件和统计工具,如方差分析(方差分析)和t实验,验证回归模型。结果表明,开发的回归模型可以有效预测复合材料的磨损率,置信度达95%。采用回归模型,并依据磨损表面形貌分析,预测实验因素对AA6061-T6/AlNp复合材料磨损率的影响。回归模型预测结果表明,复合材料的磨损率随着增强体AlN质量分数的增加而降低,随着滑动速度、滑动距离、荷载的增加而增加。
