Yuchao Bai

Poor consolidations and the strength–conductivity trade-off limit the performance of copper alloys fabricated by laser powder bed fusion (LPBF). To address this, this study developed a strategy combining the response surface methodology (RSM) with direct ageing treatment (DAT) to achieve a favorable strength–conductivity synergy. The results showed that under the optimal process parameters, a high relative density of 99.25% (8.95 g/cm³ for theoretical density) was obtained. After direct ageing treatment at 490 °C for 60 min, the CuCrZr exhibited an ultimate tensile strength of 399.31 MPa and a thermal conductivity of 326.53 W/(m·K). To reveal the underlying mechanisms, this study employed a combination of systematic characterization via high-resolution transmission electron microscopy (HRTEM) and quantitative modeling. HRTEM characterized the uniformly dispersed nanoscale body-centered cubic (BCC) Cr precipitates that form semi-coherent interfaces with the face-centered cubic (FCC) Cu matrix, showing a crystallographic misorientation of approximately 10.5° intermediate between the classic Nishiyama–Wassermann and Kurdjumov–Sachs orientation relationships. Quantitative modeling indicates that the high strength arises from a synergistic effect: coherent strain fields exerted by the precipitates effectively pin retained dislocations, coupling Orowan and dislocation strengthening. Meanwhile, solute precipitation reduces lattice distortion, restoring notable thermal conductivity.

To develop a metamaterial calcaneal bone plate with integrated mechanical and biofunctional properties, this study investigated its design and manufacturing methodologies. A solid calcaneal bone plate was first designed via a hybrid reverse-forward engineering approach. Subsequently, the metamaterial plate was realized through topology optimization combined with porous structure filling, utilizing scalar field-driven fusion to integrate distinct porous structures. Finally, the plate was directly fabricated by 3D printing and postprocessed. Results show the hybrid-designed solid plate exhibits favorable overall performance. For topology-optimized plates under different mass targets, maximum stress increases moderately but distributes more uniformly, while maximum displacement rises slightly yet remains small, ensuring safety and weight reduction. Plates filled with Kelvin cell and Diamond surface porous structures via scalar field-driven fusion demonstrate excellent filling effects, good pore connectivity, and smooth transitions for reliable bond strength. The plate's textured surface with raised ripples facilitates cell adhesion. Both 3D-printed calcaneal prostheses and plates feature high quality with minimal adhered material and no obvious defects. Assembly results confirm a close fit, properly positioned screw holes, and no significant conflicts, laying a solid foundation for the direct application of metamaterial calcaneal bone plates.

The powder-laying arm of a metal 3D printer is heavy, which can easily cause long-term damage to the powder-laying servomotor or belt, so it is necessary to design a lightweight powder-laying arm. To this end, we first use 3D modeling Rhino software to rebuild the powder-laying arm, and then, we carry out topology optimization design on the rebuilt powder-laying arm in Altair Inspire software. Finally, we use the Aurora Elva 3D printer to complete manufacturing and assembly to verify compatibility. The results show that the maximum displacement of the original powder-spreading arm is concentrated in the lower right corner at 4.319 × 10⁻⁵ mm; the maximum stress is concentrated in the middle transition part, decreasing toward the ends; the maximum stress is 3.843 × 10⁻² MPa; the stress concentration and deformation of the powder-spreading arm when spreading powder is small, which provides a large optimization space. The topology-optimized powder-spreading arm, with a 25% quality objective, maintains the integrity of the connection with the fixing hole while having a large mass reduction. The surface of the parts of the completed 3D-printed powder arm is bright, with low roughness, and there is no obvious warping and deformation or other defects; the completed 3D-printed powder-spreading arm and the assembly of the wall are closely coordinated with each other, and the location of the screw holes is appropriate, having no obvious assembly conflicts between the parts, which lays the foundation for the mass production of the powder-spreading arm of high-performance metal 3D printers.

Laser-based powder bed fusion of metals (PBF-LB/M) is one of the most promising additive manufacturing technologies to fabricate complex-structured metal parts. However, its corresponding applications have been limited by technical bottlenecks and increasingly strict industrial requirements. Process optimization, a scientific issue, urgently needs to be solved. In this paper, a three-phase transient model based on the level-set method is established to examine the heat transfer and melt pool behavior in PBF-LB/M. Surface tension, the Marangoni effect, and recoil pressure are implemented in the model, and evaporation-induced mass and thermal loss are fully considered in the computing element. The results show that the surface roughness and density of metal parts induced by heat transfer and melt pool behavior are closely related to process parameters such as laser power, layer thickness, scanning speed, etc. When the volumetric energy density is low, the insufficient fusion of metal particles leads to pore defects. When the line energy density is high, the melt track is smooth with low porosity, resulting in the high density of the products. Additionally, the partial melting of powder particles at the beginning and end of the melting track usually contributes to pore formation. These findings provide valuable insights for improving the quality and reliability of metal additive manufacturing.

Additive manufacturing (AM) provides a promising method to fabricate advanced functional parts with different mechanical and material performances from their traditional counterparts. However, the poor surface quality makes the subsequent post-processing necessary for precision application. Hybrid manufacturing combining additive and subtractive manufacturing processes is an effective method to improve the surface quality of additive manufacturing (AMed) metal parts rapidly by using a subtractive process, in which surface roughness is an important technical indicator. Therefore, accurate surface roughness prediction is crucial for process and quality control in the subtractive machining of additively manufactured parts. In this study, a prediction method utilizing a broad learning system (BLS) is developed to predict the surface roughness of machined AMed maraging steel parts considering aging heat treatment. First, feature extraction was performed on the force signal during the cutting process in the time domain, frequency domain, and time–frequency domain. Then, the maximum information coefficient was used to select important features from high to low feature by feature. Furthermore, the important features and cutting parameters were fused as the input of BLS. Finally, the corresponding prediction results were compared with those based only on cutting parameters. The results show that the prediction accuracy of machined surface roughness is higher when fusing force signal features and cutting parameters. The prediction errors (mean absolute percentage error) were reduced by 67.28% and 16.39% to 0.53% and 0.51%, respectively, for the AMed maraging steels with and without heat treatment.