材料期刊网

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.

The wear behavior of multi-walled carbon nano-tubes (MWCNTs) reinforced copper metal matrix composites (MMCs) processed through powder metallurgy (PM) route was focused on and further investigated for varying MWCNT quantity via experimental, statistical and artificial neural network (ANN) techniques. Microhardness increases with increment in MWCNT quantity. Wear loss against varying load and sliding distance was analyzed as per L16 orthogonal array using a pin-on-disc tribometer. Process parameter optimization by Taguchi's method revealed that wear loss was affected to a greater extent by the introduction of MWCNT; this wear resistant property of newer composite was further analyzed and confirmed through analysis of variance (ANOVA). MWCNT content (76.48%) is the most influencing factor on wear loss followed by applied load (12.18%) and sliding distance (9.91%). ANN model simulations for varying hidden nodes were tried out and the model yielding lower MAE value with 3-7-1 network topology is identified to be reliable. ANN model predictions withR value of 99.5% which highly correlated with the outcomes of ANOVA were successfully employed to investigate individual parameter's effect on wear loss of Cu?MWCNT MMCs.