Enhancing Mechanical Properties of Graphene/Aluminum Nanocomposites via Microstructure Design Using Molecular Dynamics Simulations.

This study explores the mechanical properties of graphene/aluminum (Gr/Al) nanocomposites through nanoindentation testing performed via molecular dynamics simulations in a large-scale atomic/molecular massively parallel simulator (LAMMPS). The simulation model was initially subjected to energy minimization at 300 K, followed by relaxation for 50 ps under the NPT ensemble, wherein the number of atoms (N), simulation temperature (T), and pressure (P) were conserved. After the model was fully relaxed, loading and unloading simulations were performed. This study focused on the effects of the Gr arrangement with a brick-and-mortar structure and incorporation of high-entropy alloy (HEA) coatings on mechanical properties. The findings revealed that Gr sheets (GSs) significantly impeded dislocation propagation, preventing the dislocation network from penetrating the Gr layer within the plastic zone. However, interactions between dislocations and GSs in the Gr/Al nanocomposites resulted in reduced hardness compared with that of pure aluminum. After modifying the arrangement of GSs and introducing HEA (FeNiCrCoAl) coatings, the elastic modulus and hardness of the Gr/Al nanocomposites were 83 and 9.5 GPa, respectively, representing increases of 21.5% and 17.3% compared with those of pure aluminum. This study demonstrates that vertically oriented GSs in combination with HEA coatings at a mass fraction of 3.4% significantly enhance the mechanical properties of the Gr/Al nanocomposites.

Research on an Online Monitoring Device for the Powder Laying Process of Laser Powder Bed Fusion.

Improving the quality of metal additive manufacturing parts requires online monitoring of the powder bed laying procedure during laser powder bed fusion production. In this article, a visual online monitoring tool for flaws in the powder laying process is examined, and machine vision technology is applied to LPBF manufacture. A multiscale improvement and model channel pruning optimization method based on convolutional neural networks is proposed, which makes up for the deficiencies of the defect recognition method of small-scale powder laying, reduces the redundant parameters of the model, and enhances the processing speed of the model under the premise of guaranteeing the accuracy of the model. Finally, we developed an LPBF manufacturing process laying powder defect recognition algorithm. Test experiments show the performance of the method: the minimum size of the detected defects is 0.54 mm, the accuracy rate of the feedback results is 98.63%, and the single-layer laying powder detection time is 3.516 s, which can realize the effective detection and control of common laying powder defects in the additive manufacturing process, avoids the breakage of the scraper, and ensures the safe operation of the LPBF equipment.