多壁碳纳米管增强铜基金属复合材料干滑动磨损行为的参数优化（英文）

发布时间：2018-06-02 07:06

本文选题：铜 + 多壁碳纳米管(MWCNT)　；参考：《Transactions of Nonferrous Metals Society of China》2017年03期

【摘要】：利用实验、统计学及人工神经网络方法研究粉末冶金法制备的多壁碳纳米管增强铜基金属复合材料的磨损行为,并探讨多壁碳纳米管含量的影响。测定和分析复合材料样品的显微硬度,设计L16正交实验,采用销盘式摩擦计测定样品的磨损量随载荷和滑动距离的变化。结果表明:铜基金属复合材料的硬度随多壁碳纳米管含量的增加而增加。Taguchi法工艺参数优化结果表明多壁碳纳米管的引入对复合材料磨损量产生较大影响。利用ANOV统计学方法分析和验证了复合材料的抗磨损性能。多壁碳纳米管含量对复合材料磨损量的影响最大(贡献率为76.48%),其次为所加载荷(贡献率为12.18%),最后为滑动距离(贡献率为9.91%)。采用具有可变隐含节点的人工神经网络模型对复合材料的磨损过程进行模拟,所得结果的平均误差(MAE)值较低,3-7-1网络拓扑结构的适应性强,所得数据可靠。人工神经网络预测值(相关系数R值为99.5%)与ANOVA统计结果吻合良好,且能用于研究各参数对多壁碳纳米管增强的铜基金属复合材料磨损行为的影响。
[Abstract]:The wear behavior of multi-wall carbon nanotubes reinforced copper matrix metal composites prepared by powder metallurgy was studied by means of experiment, statistics and artificial neural network. The effect of the content of multi-wall carbon nanotubes on the wear behavior of copper matrix metal composites was also discussed. The microhardness of the composite materials was measured and analyzed. The L16 orthogonal experiment was designed. The wear rate of the samples was measured by using a pin-disc friction meter with load and sliding distance. The results show that the hardness of copper matrix metal composites increases with the increase of multi-wall carbon nanotubes content. The optimization results show that the introduction of multi-walled carbon nanotubes has a great influence on the wear of the composites. The wear resistance of the composites was analyzed and verified by ANOV statistical method. The content of multi-walled carbon nanotubes had the greatest effect on the wear capacity of composites (the contribution rate was 76.48%), followed by the loading (the contribution rate was 12.18%) and the sliding distance (the contribution rate was 9.91%). An artificial neural network model with variable hidden nodes is used to simulate the wear process of composite materials. The average error (MAE) value of the obtained results is lower than that of the 3-7-1 network topology, and the obtained data are reliable. The predicted value of artificial neural network (correlation coefficient R = 99.5) is in good agreement with the ANOVA statistical results, and it can be used to study the effects of various parameters on the wear behavior of copper matrix metal composites reinforced by multi-walled carbon nanotubes.
【作者单位】： Department
【分类号】：TB333

【相似文献】