In this work, ZrB2-based ultra-high temperature ceramics were fabricated by Direct Ink Writing (DIW) using two different ink formulations containing 10% vol. SiC (ZS10) or 10% vol. MoSi2 (ZM10) as sintering aids. Rheological tests confirmed shear-thinning and viscoelastic behaviour for both inks, with ZM10 exhibiting higher viscosity and modulus values, indicative of a stronger network structure. During sintering, both compositions underwent homogeneous shrinkage (≈20%) and mass loss (≈8%) due to the removal of organic additives, resulting in defect-free samples. ZM10 achieved higher densification (3.0% porosity) compared to ZS10 (5.5% porosity), confirming the effectiveness of MoSi2 in promoting liquid-phase sintering. Mechanical characterization showed brittle fracture for both compositions, with ZM10 reaching a flexural strength of ≈270 MPa and ZS10 of ͏ ≈170 MPa. Moreover, oxidation tests showed minimal oxidation for both materials, with oxide scales of 260–360 µm after 30 min exposure in air at 1650 °C. Overall, the results show that DIW combined with pressure-less sintering enables the production of dense, complex-shaped ZrB2-based ceramics with tunable mechanical and oxidation properties.
Femtosecond laser ablation offers exceptional precision for titanium processing, yet the atomistic origins of crystallographic anisotropy in ultrafast material removal remain poorly understood. Here, molecular dynamics simulations coupled with a two-temperature model were used to determine how crystallographic orientation and microstructure govern the thermal response, defect evolution, and ablation behaviour of hexagonal close-packed (HCP) titanium as a reference material. Basal (0001), prismatic (10–10 and 11–20), first-order pyramidal (10–11), second-order pyramidal (11–22), and polycrystalline configurations of HCP titanium were subjected to two representative laser fluences (0.109 and 0.545 J cm−2) with temperature-dependent electronic properties. The second-order pyramidal orientation consistently generated the highest average temperature within the laser-interaction region. At absorbed fluences of 0.109 and 0.545 J cm−2, its temperature was approximately 30–36% higher than those of the basal and prismatic orientations, but only approximately 12% higher than that of the first-order pyramidal orientation. Single crystals developed comparatively organised dislocation networks that followed the evolving melt-pool geometry, whereas the polycrystalline model exhibited heterogeneous defect nucleation at grain boundaries, which acted simultaneously as dislocation sources and barriers to propagation. Laser-induced defects also developed a characteristic sixfold arrangement reflecting the underlying symmetry of the titanium lattice. These findings establish a direct atomistic link between crystallographic structure, local temperature evolution and defect-mediated material removal, providing a mechanistic basis for controlling crater morphology, thermal damage and surface integrity during precision femtosecond-laser processing.
The direct nitridation of binder jet additively manufactured silicon to produce porous reaction-bonded silicon nitride was explored to determine the maximum thickness of the sample that could be converted to >99% silicon nitride. The maximum thickness that could be nitrided was investigated by nitriding cubes of increasing physical size on the millimeter scale. Highly porous samples with high conversion ( > 99%) to silicon nitride were achieved in cubes with 35 mm edge length. It was shown that the silicon nitride phase content changes with increasing sample size.
This study investigates the manufacturing of carbon-fiber-reinforced composite structures performed during the flight of a suborbital rocket. The authors adapted the Direct Robotic Extrusion of Photopolymers process to allow the manufacturing of more complex and carbon-fiber-reinforced structures. A dedicated degassing procedure was developed to effectively limit void formation caused by vacuum conditions: For circular specimens, no statistically significant difference in void volume fraction was observed between ambient pressure (2.2%) and vacuum conditions (2.1%), demonstrating the effectiveness of the implemented degassing approach. A thermogravimetric analysis showed a mass loss of the carbon-fiber filament of 4.4%, supporting the conclusion that its outgassing was responsible for void formation. Under reduced gravity, the void volume fraction increased to 16.5%. Furthermore, adhesion forces dominated the extrusion process in the absence of a gravitational vector, leading to incomplete extrusion of two of the four structures. These results demonstrate that reduced gravity and reduced atmospheric pressure constitute distinct challenges for viscous liquid-based in-space manufacturing processes, and that ground-based vacuum tests alone are insufficient to qualify such processes for spaceflight. A systematic review identified zero eligible studies investigating the manufacturing of carbon-fiber-reinforced structures under simultaneous reduced gravity and reduced atmospheric pressure conditions, indicating a significant research gap.
In-situ assistance plays a vital role in enhancing operational performance and process reliability in manufacturing. Conventional operator training systems often rely on manual supervision from experienced workers, which can lead to inefficiency, inconsistent guidance, and high labor costs. To address these limitations, this work introduces AssemblyMate, an intelligent XR-based assembly assistant designed to support human operators during complex assembly procedures. The system integrates two core components: a multimodal reasoning agent and a spatial-temporal reasoning agent, enabling dynamic guidance, real-time feedback and systematic evaluation for operators. Specifically, the multimodal reasoning agent provides context-aware instructions, demonstration videos, and interaction with operators in augmented reality through the learning process. Meanwhile, the spatial-temporal reasoning agent continuously analyzes action sequences and task states, enabling dynamic performance evaluation and coordinating with a collaborative robot to manage product transfer and assembly flow. The proposed framework is validated on two representative industrial scenarios—calculator assembly and vehicle assembly. It demonstrates that the integrated reasoning architecture significantly improves task efficiency, accuracy, and human-robot collaboration. These results highlight the potential of intelligent assistant to transform in-situ operations into adaptive, data-informed processes that advance workforce augmentation in smart manufacturing.
