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Experimental investigations on wear and friction behaviour of SiC@r-GO reinforced Mg matrix composites produced through solvent-based powder metallurgy

机译:溶剂基粉末冶金制备SiC @ r-GO增强Mg基复合材料的磨损和摩擦性能的实验研究

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摘要

In the present study, wear and friction behaviour of Magnesium(Mg) Metal Matrix Composite(MMC) reinforced with Silicon carbide(SiC) doped reduced graphene oxide (r-GO) nanosheets is carried over. In addition, a mathematical model is developed to predict the influence of various control factors of the Mg composites fabricated through Solvent-based powder metallurgy process. Herein SiC is doped with varying wt. % (10, 20, 30) into r-GO nanosheets and its effect over dry sliding wear is studied at constant control parameters like that of load (10N), sliding distance (1000m) and sliding velocity (1 m/s). Optimal parameter for specific wear rate (SWR) is attained by Taguchi method and the mathematical model was developed using Artificial Neural Network in order to understand the wear behaviour of developed MMC under varying parametric condition. Analysis of variance result reveals that wt.% of r-GO have major influence on SWR and sliding velocity have least effect. Occurrence of delamination wear could also be notified over the worn out surface.
机译:本文研究了掺有碳化硅(SiC)掺杂的还原氧化石墨烯(r-GO)纳米片增强的镁(Mg)金属基复合材料(MMC)的磨损性能。此外,建立了数学模型来预测通过溶剂基粉末冶金工艺制造的镁复合材料的各种控制因素的影响。在此,SiC掺杂有变化的wt。 (10、20、30)%的纳米复合材料制成r-GO纳米片,并在恒定的控制参数(如载荷(10N),滑动距离(1000m)和滑动速度(1 m / s))下研究了其对干式滑动磨损的影响。通过Taguchi方法获得了特定磨损率(SWR)的最佳参数,并使用人工神经网络建立了数学模型,以便了解已开发的MMC在不同参数条件下的磨损行为。方差结果分析表明,r-GO的重量百分比对SWR的影响最大,而滑动速度的影响最小。还可以在磨损的表面上通知分层磨损的发生。

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