Advanced Engineering Materials最新文献

Multimodal Solidification Characterization

In their Research Article (10.1002/adem.202501408), Nikhilesh Chawla and co-workers demonstrate the first simultaneous, time-resolved 4D X-ray microtomography and energy-dispersive diffraction analysis of Sn-Bi solder alloy solidification. The multimodal approach captures the nucleation and faceted growth of primary Bi crystals, the formation of Sn dendrites, and eutectic microstructures, while correlating diffraction peak intensities with phase volume fractions. These insights enable a deeper, quantitative understanding of microstructural evolution and strain development during alloy solidification.

Microvibration Suppression

Inspired by the distribution of pores and the morphology of mineralized fibers of goat tibia, in their Research Article (10.1002/adem.202402969), Qing Luo, Chang Liu, and Dongxu Li propose a multi-scale biomimetic structure design method. By irregularly embedding micro-scale biomimetic cells into a macroscopically regular biomimetic layout framework, the micro-vibration suppression performance typical flywheel-cabin plate system in the spacecraft is optimized.

In this study, hybrid polymer composites based on polypropylene (PP) are developed using aluminum nitride (AlN) and super thermal conductive graphite (Gr) as fillers to improve mechanical, structural, thermal, and electrical properties. A series of composites containing varying ratios of AlN and Gr are fabricated via melt mixing and hot-press molding techniques. The synergistic effects of ceramic and carbon-based fillers are systematically investigated through mechanical testing, SEM analysis, DSC/TGA thermal characterization, and electrical resistivity measurements. Results show that low filler concentrations led to improved dispersion, mechanical integrity, and enhanced thermal properties. The Gr addition act as an efficient nucleating agent and significantly increases crystallinity and thermal conductivity, while AlN contributes to mechanical reinforcement. In comparison with neat PP, the 10A20G composite exhibits ≈63% (≈1.6-fold) higher thermal conductivity and about 10⁴ times lower electrical resistivity, confirming its suitability for thermal interface applications. The findings suggest that the hybrid use of AlN and Gr can lead to high-performance PP-based composites for electronic packaging and thermal management applications.

Conventional fabrication of Ti-6Al-4V/Ni-Ti bimetallic structures is both costly and impractical for complex geometries. In this study, to resolve these difficulties, multi-wire arc additive manufacturing (M-WAAM) with in situ alloying is used. The results show that NiTi₂ phase reduces ductility and induces stress concentration due to lattice mismatch with the NiTi phase, further accelerating crack formation. The as-built wall consists of NiTi₂ and NiTi layers, formed through element diffusion, with the interface evolving from a basket-weave microstructure to a eutectic α-Ti + NiTi₂ phase and ultimately a planar NiTi₂ structure. Besides, as-built wall exhibits a peak microhardness of 692.2 HV, a compressive strength of 1137.5 ± 31 MPa, and a fracture strain of 6.7% ± 2%. This study presents an efficient strategy for fabricating complex bimetallic structures, providing insights into integrated manufacturing of dissimilar metals for aerospace and related applications.

TiN coatings deposited via high power impulse magnetron sputtering technology exhibit a dense microstructure, high hardness, and adhesion strength, enhancing the longevity of engineering components. This study systematically investigates the influence of N₂ flow rate on the microstructure and mechanical properties of TiN coatings while maintaining a fixed Ar flow rate of 60 sccm and a substrate bias of −120 V. Results indicate that as the N₂ flow rate increases, the surface morphology transitions from irregular shapes to pyramidal and eventually to tetrahedral shapes, while the cross-sectional structure remains dense. The preferred orientation shifts from TiN (111) to TiN (200) with N₂ flow rate increasing. Notably, the coatings achieve peak hardness (about 28.2 GPa) and elastic modulus (about 269.8 GPa) at an N₂ flow rate of 25 sccm, alongside maximum compressive residual stress (about 5.09 GPa). Adhesion strength ranges from 47.6 to 61.2 N, demonstrating superior adhesion to the substrate. This research provides a theoretical for future investigations into the application of TiN coatings in various industrial contexts, highlighting their exceptional mechanical properties and potential for improved performance.

Lattice structures can enhance mechanical properties while minimizing mass, but often face challenges from inefficient material distribution and stress concentrations. Here, a generative design method is used to create freeform lattices that resemble biological structures. This approach is found to provide less constrained material redistribution, allowing the reduction of stress concentrations embedded in conventional designs. Three lattice types are optimized and compared: the bending-dominated body-centered cubic (BCC) lattice, the stretching-dominated simple cubic lattice, and a directional, water-lily-inspired lattice. Compression testing reveals improvements in stiffness, strength, and energy absorption in all three lattice types at sufficiently high relative density. Interestingly, all optimized structures display a marked reduction in anisotropy, with an optimized BCC lattice exhibiting isotropic elasticity. This study shows how generative design can emulate the organic forms of nature to create lattices with superior properties, offering new pathways for lightweight, high-performance structures.

Magnetoactive metamaterials (MMs) represent a cutting-edge class of smart materials that integrate magnetoactive material with architected mechanical metastructures, enabling dynamic control over their mechanical, acoustic, and elastic properties through the application of external magnetic fields. This review presents an in-depth summary of recent progress in MMs, emphasizing their design strategies, manufacturing methods, and wide-ranging applications in areas like biomedical devices, soft robotics, and adaptive structures. The study particularly explores the integration of magnetoactive soft composite materials with mechanical metamaterials, highlighting their ability to achieve tunable physical and mechanical property changes, shape morphing, and wave manipulation. Key fabrication methods, including 3D/4D printing and conventional molding techniques, are discussed, emphasizing their role in creating complex, functional architectures. Additionally, the influence of embedded hard and soft magnetic particles on the performance of MMs made of soft elastomeric matrix is examined, emphasizing their role in achieving contactless actuation, rapid response, and multifunctionality. The review concludes with future research directions, advocating for the integration of machine learning techniques for optimized metamaterial design. The review may serve as a valuable resource for researchers and engineers aiming to harness the potential of these advanced adaptive materials for next-generation technologies.

High-entropy alloys (HEAs) represent a frontier in materials science, offering many promising features suitable for high-demand applications in nuclear and space sectors, such as exceptional mechanical properties. However, a major challenge in these fields is accurately predicting the behavior of HEAs under extreme conditions, such as radiation exposure or elevated operating temperatures, in order to maintain the integrity of the materials. Machine learning (ML) provides powerful tools to address this challenge. ML techniques, including ML interatomic potentials (MLIP), enable the modeling and prediction of complex behaviors in HEAs. This review focuses on ML to enhance the understanding of phase stability, mechanical properties, and radiation damage prediction in these complex alloys. The potential of ML to accelerate the discovery/optimization of new HEA compositions with good performance under extreme conditions is also discussed. Ultimately, the aim is to highlight the transformative role of ML in the field of HEAs under extreme conditions, in light of developing novel materials suitable for harsh environments.