Among various non-contact direct ink writing techniques, aerosol jet printing (AJP) stands out due to its distinct advantages, including a more adaptable working distance (2–5 mm) and higher resolution (~ 10 μm). These characteristics make AJP a promising technology for the precise customization of intricate electrical functional devices. However, complex interactions among the machine, process, and materials result in low controllability over the electrical performance of printed lines. This significantly affects the functionality of printed components, thereby limiting the broad applications of AJP. Therefore, a systematic machine learning approach that integrates experimental design, geometrical features extraction, and non-parametric modeling is proposed to achieve printing quality optimization and electrical resistivity prediction for the printed lines in AJP. Specifically, three classical convolutional neural networks (CNNs) architectures are compared for extracting representative features of printed lines, and an optimal operating window is identified to effectively discriminate better line morphology from inferior printed line patterns within the design space. Subsequently, three representative non-parametric machine learning techniques are employed for resistivity modeling. Following that, the modeling performances of the adopted machine learning methods were systematically compared based on four conventional evaluation metrics. Together, these aspects contribute to optimizing the printed line morphology, while simultaneously identifying the optimal resistivity model for accurate predictions in AJP.

The 3D cementitious material printing method is an extrusion-based additive manufacturing strategy in which cementitious materials are extruded through a dynamic nozzle system to form filaments. Despite its ability to fabricate structures with high complexity and efficiency, the uneven material distribution during the extrusion and deposition process is often encountered when a radial toolpath is introduced. This limits the design freedom and printing parameters that can be utilized during radial toolpath printing. Here, we report a facile strategy to overcome the existing challenges of cementitious material non-homogeneity by rationally developing new nozzle geometries that passively compensate the differential deposition rate encountered in conventional rectangular nozzles. Using two-phase numerical study, we showed that our strategy has the potential of achieving a homogeneous mass distribution even when the nozzle travel speed is unfavorably high, while filament from a rectangular nozzle remains highly non-homogenous. The material distribution unevenness can be reduced from 1.35 to 1.23 and to 0.98 after adopting trapezoid and gaussian nozzles, indicating improvements of 34.3% and 94.2%, respectively. This work not only outlines the methodology for improving the quality of corner/curved features in 3DCMP, but also introduces a new strategy which can be adopted for other extrusion-based fabrication techniques with high material inertia.

Due to the demand of unmanned helicopters for drag reduction and rain-proof, the helicopter nacelle must be sealed. It will lead to a decrease in the heat transfer efficiency of the radiator, and the output power of the engine will drop dramatically. The helicopter's flight safety will be seriously jeopardized, especially when the helicopter is hovering (maximum engine power is needed). Currently, active cooling by equipping the radiator with a fan is the only solution, and the heat transfer efficiency of the radiator could be controlled by fan. Therefore, the performance of the fan directly affects the flight safety of the whole system. In this study, the Airfoils 30, suitable for low Reynolds number flows, is adopted to design the fan blade considering the characteristics of helicopter heat sink miniaturization and high integration. Then, a three-dimensional CFD (computational fluid dynamics) k-omega SST model is developed to investigate the effects of fan blade torsion angle, chord length, mounting angle and the number of blades on the performance of the fan. Furthermore, the constraints of radiator dimension, air flow resistance on the performance of the fan are considered comprehensively to finalize the new fan configuration. The optimised parameters of fan suitable for high flow rate (above 1.17 kg/s) are chord length is 55 mm, torsion angle is 26°, mounting angle is 39° and the blade number is 7. The fan efficiency increases about 13.6%. The power consumption decreases about 9.5% (about 73 W). The fan rotational speed decreases 10.5%. The improvement of fan efficiency is a key measure for energy conservation and carbon reduction in unmanned helicopter systems. The 73 W power consumption of the fan decrease indicates that 1.2 kg green-house gas emission reduces per day. The lower power consumption will result in a 0.14% cruising endurance increase. The fan is then manufactured by additive manufacturing based on CFD optimization results. This deep integration between CFD and additive manufacturing reduces trial and error costs and energy consumption. It also shows the promising future of UAV components autonomous manufacturing. Finally, the experiment is conducted in lab under 40℃. The experimental results indicate that the maximum output power of the engine is over than 90 kW. Based on the helicopter main rotor performance curve, the helicopter could hover indefinitely with 500 kg loading under 40 °C. It is a criterion to identify the designing success.

Three-dimensional cementitious material printing (3DCMP) enables structures with complex geometry to be fabricated. Printed filament quality is significantly affected by the mass distribution at its corners. Although fruitful results have been obtained, a significant gap exists in systematically investigating the impact of comprehensive parameters on this mass distribution. Therefore, the cross-section ratio Φ (Φ = S_o/S_u) of the filament is proposed as a measurement to evaluate the mass distribution at corners. Then, the impacts of printing process parameters, including the tool path radius R, nozzle aspect ratio φ, and relative nozzle travel speed ζ, on the filament mass distribution are investigated using computational fluid dynamics (CFD). The flow mechanism is elaborated using CFD for cementitious material printing at corners. It was found that the material flow mechanism caused by the combined effects of the printing process parameters affects the filament mass distribution significantly. Some material spills out from the overfilled zone to the underfilled zone during the deposition process. Additionally, printing process windows were identified to ensure acceptable printing quality using a support vector machine (SVM). A new printing window is identified using transfer learning, which can save data resources compared to the SVM method. Finally, the experimental results show the feasibility and effectiveness of the proposed methods in printing process window determination.