Enhancement of Physical Properties and Corrosion Resistance of Al-Cu-Al2O3/Graphene Nanocomposites by Powder Metallurgy Technique

Manufacturing of Aluminum Nano-Composites Reinforced with Nano-Copper and High Graphene Ratios Using Hot Pressing Technique

In this study, the nano-aluminum powder was reinforced with a hybrid of copper and graphene nanoplatelets (GNPs). The ratios of GNPs were 0 wt%, 0.4 wt%, 0.6 wt%, 1.2 wt% and 1.8 wt%. To avoid the reaction between aluminum and graphene and, consequently, the formation of aluminum carbide, the GNP was first metalized with 5 wt% Ag and then coated with the predetermined 15 wt% Cu by the electroless coating process. In addition, the coating process was performed to improve the poor wettability between metal and ceramic. The Al/(GNPs-Ag)Cu nanocomposites with a high relative density of 99.9% were successfully prepared by the powder hot-pressing techniques. The effects of (GNPs/Ag) and Cu on the microstructure, density, hardness, and compressive strength of the Al-Cu nanocomposite were studied. As a result of agitating the GNPs during the cleaning and silver and Cu-plating, a homogeneous distribution was achieved. Some layers formed nano-tubes. The Al4C3 phase was not detected due to coating GNPs with Cu. The Cu9Al4 intermetallic was formed during the sintering process. The homogeneous dispersion of Cu and different ratios of GNs, good adhesion, and the formation of the new Cu9Al4 intermetallic improved in hardness. The pure aluminum sample recorded 216.2 HV, whereas Al/Cu reinforced with 1.8 GNs recorded 328.42 HV with a 51.9% increment. The compressive stress of graphene samples was improved upon increasing the GNPs contents. The Al-Cu/1.8 GNs sample recorded 266.99 MPa.

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al₂O₃ nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al₂O₃ nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.