Fabrication and characterization of GNPs and CNTs reinforced Al7075 matrix composites through the stir casting process

This study investigated the effects of adding graphene nanoplates(GNPs)and carbon nanotubes(CNTs)into the Al7075 matrix via the stir casting method on the microstructure and mechanical properties of the fabricated composites.By increasing the volume fraction of rein-forcements,the fraction of porosity increased.The X-ray diffraction results showed that the addition of reinforcements into the Al7075 changed the dominant crystal orientation from(002)to(111).Field emission scanning electron microscopy images also showed the distribution of clustered reinforcements in the matrix.Between the two reinforcements,the addition of CNTs generated a lower fraction of porosities.Through the addition of 0.52vol%GNPs into the matrix,the hardness,ultimate tensile strength and uniform elongation increased by 44%,32%,and 180%,respectively.Meanwhile,the presence of 0.71vol%CNTs in the matrix increased the hardness,tensile strength and uniform elongation by 108%,129%,and 260%,respectively.

GNPs含量对Al-Si-Cu合金的摩擦磨损性能的影响

在本研究中,利用金相显微镜(OM),扫描电子显微镜(SEM),能谱分析仪(EDS),显微硬度计和摩擦磨损试验机对GNPs(石墨烯片)/Al-Si-Cu复合材料的显微组织、显微硬度和摩擦磨损性能等进行了分析和表征。研究结果表明:随着GNPs的含量的逐渐增加,磨损率和摩擦系数均呈现出先减小再增大的趋势。当GNPs的含量为12 vol.%时,复合材料中的Si相明显得到了细化,由基体合金的长条状变为复合材料的短棒状。此时复合材料的磨损率和摩擦系数均最小,分别为21.27×10^(-3) mm^(3)/m和0.358,与Al-Si-Cu基体合金相比分别降低了37.07%和15.76%,复合材料的耐磨性能达到最佳。Al-Si-Cu基体合金是以剥层磨损为主要磨损机理,其磨屑是以大尺寸的板块状磨屑为主,而12 vol.%GNPs/Al-Si-Cu复合材料是以磨粒磨损为主要磨损机理,其磨屑则是以小尺寸板块状和细颗粒为主。

Finite Element Prediction of the Thermal Conductivity of GNP/Al Composites

A 3 D multi-scale finite element model was developed to predict the effective thermal conductivity of graphene nanoplatelet(GNP)/Al composites.The factors influencing the effective thermal conductivity of the GNP/Al composites were investigated,including the orientation,shape,aspect ratio,configuration and volume fraction of GNPs.The results show that GNPs shape has a little influence on the thermal conductivity of GNP/Al composites,and composites with elliptic GNPs have the highest thermal conductivity.In addition,with increasing the aspect ratio of GNPs,the thermal conductivity of GNP/Al composites increases and finally tends to be stable.The GNPs configuration strongly influences the thermal conductivity of GNP/Al composites,and the thermal conductivity of the composites with layered GNPs is the highest among the five configurations.The effective thermal conductivity is sensitive to volume fraction of GNPs.Ideally,when the volume fraction of layered GNPs reaches 1.54%,the thermal conductivity of GNP/Al composites is as high as 400 W/m K.The findings of this study could provide a good theoretical basis for designing high thermal conductivity GNP/Al composites.

搅拌摩擦加工制备GNPs/Al复合材料的微观结构与力学性能

以纯铝为基体,利用搅拌摩擦加工(FSP)制备GNPs/Al复合材料,研究了复合材料基体组织、增强相与界面的微观结构与力学性能,探讨了其增强机理。结果表明,添加GNPs并经FSP后复合材料基体晶粒得到明显细化且晶界由小角度为主转变为大角度为主;FSP制备过程致使GNPs片层一定程度剥离的同时,较大片径的GNPs被破碎而形成众多边缘缺陷,使其易发生Al-C原子扩散,结果在GNPs边缘与基体形成界面过渡;GNPs加入量约1.8vol%时,复合材料的屈服强度和抗拉强度达到73和147MPa,较同等条件FSP的基体分别提高了92.1%和79.3%,理论计算界面载荷传递、Orowan和细晶强化依次是复合材料的主要增强机制;随着GNPs加入量的增加,复合材料屈服强度实验值与理论值的增长趋势一致,且偏差也略有提高,但可能因GNPs在复合材料中的杂乱排布,界面载荷传递强化不能充分发挥,实际的复合材料屈服强度与理论值尚有差距。

基于BP神经网络和Arrhenius本构模型的石墨烯/7075复合材料热变形行为

在653~713 K温度范围和0.01~10 s^(-1)应变速率下,进行了w(GNP/7075Al)=0.5%增强7075铝基复合材料的热压缩试验,建立了BP神经网络和应变补偿Arrhenius模型,同时建立了复合材料的热加工图和动态再结晶体积分数预测模型,研究了复合材料的热变形行为,并确定了复合材料的热加工工艺参数。结果表明:BP神经网络模型得到的流变应力预测值与试验结果吻合较好,其相关系数最高为99.9983%,平均相对误差绝对值最小为0.5%,表明神经网络对w(GNP/7075Al)=0.5%复合材料的热变形行为具有较高的预测精度。w(GNP/7075Al)=0.5%复合材料最佳变形温度和应变速率分别为685~705 K和0.01~0.1 s~(-1)。动态再结晶(DRX)倾向于在低应变速率和高变形温度下发生。数值模拟和热挤压试验表明,在挤压温度693 K、挤压速度1 mm/min的工艺参数下可以挤出表面质量良好的型材。