This study proposed a new magnetic abrasive finishing (MAF) method, in which a 6-axis robot with a magnetic machining tool was used to polish the inner surfaces of curved tubes. We have also developed a magnetic machining tool jig, which can be fixed at the front of the 6-axis robot, rotating freely and suitable for polishing the inner surfaces of curved tubes. In this study, we focused on investigating the machining parameters in the initial machining stage and precision finishing stage. Based on the characteristics of machining parameters, a multi-stage MAF process was conducted to obtain an inner surface with high quality and high efficiency. The experimental results showed that both the roughness Ra and Rz of inner surface in the initial machining stage significantly decreased with the increase in the mixed magnetic abrasives, to as low as less than 20 nm Ra in the precision finishing stage when the machining parameters were appropriately adjusted. In addition, the roughness Ra of inner surface could be further reduced to less than 10 nm Ra in the multi-stage MAF process. Finally, the magnetic flux density cloud map and the magnetic field line distribution map were analyzed in Ansys Maxwell.

A novel polishing method is proposed to increase material removal rates through the acceleration of abrasive movements using micro-jets formed by spontaneous collapses of bubbles due to the cavitation in a special-shaped Venturi tube. The Venturi structure is optimized by numerical simulations. Process-related parameters for the optimal cavitation ratio are investigated for achieving maximum adaptation to polishing flat workpieces. Furthermore, this novel approach enhances processing efficiency by approximately 60% compared to traditional abrasive flow polishing. The processing method that employs cavitation bubbles within a special-shaped Venturi tube to augment the flow of abrasive particles holds significant potential for material polishing applications.

Unmanned aerial systems (UASs) find diverse applications across military, civilian, and commercial sectors, including military reconnaissance, aerial photography, environmental monitoring, precision agriculture, logistics, and rescue operations, offering efficient, safe, and cost-effective solutions to various industries. To ensure the stable and reliable operation of UASs, fault diagnosis is essential, which can enhance safety, and minimize potential risks and losses. However, most existing fault diagnosis methods rely on a single physical quantity as the primary information source or solely consider fault data at a single moment, leading to challenges of low diagnostic accuracy and limited reliability. Aimed at this problem, this paper presents a fault diagnosis method based on time–space domain weighted information fusion for UASs. First, the Gaussian fault model is constructed for the data with different fault features in the space domain. Next, the weighted coefficient method is used to generate the basic probability assignment (BPA) by matching the fault data with the Gaussian fault model. Then, the Dempster’s combination rule, which enables the Dempster–Shafer (D-S) evidence theory, is adopted to fuse the generated BPAs. Based on this, the pignistic probability transformation is performed to determine the fault type. Finally, numerical results demonstrate the effectiveness of the proposed fault diagnosis method in accurately identifying the fault types of UASs.

The dual-pole magnetic abrasive finishing (DMAF) method was proposed to achieve a smooth surface on TC4 titanium alloy. Firstly, both the distribution of the magnetic field and the intensity of magnetic induction produced by nine combinations of magnetic poles of different shapes were simulated using Ansys Maxwell software (2024R2). According to the results of the simulation, the optimal combination of magnetic poles was determined. Then, the machining parameters of multi-stage DMAF were optimized through comparative experiments on major single factors. Finally, combinations of the mixed magnetic abrasive in three polishing stages were obtained as follows: #100 electrolytic iron powder (Fe₃O₄) + #2000 white abrasive (WA), #200 Fe₃O₄ + #8000 WA, and #450Fe₃O₄ + #w1 diamond (DMD). The gap between the upper and lower magnetic poles was set to 5 mm, the rotational speed of the magnetic pole was set to 300 rpm, and the quality ratio of the abrasive was 2:1. The experiments indicated that the average surface roughness Ra was reduced from an initial value of 0.433 μm to 8 nm after 30 min of multi-stage DMAF, and a nano-level mirror polishing effect was essentially achieved in the polishing zone.

The integration of unmanned aerial vehicles (UAVs) into vehicular networks offers numerous advantages in enhancing communication and coverage performance. With the ability to move flexibly in three-dimensional space, UAVs can effectively bridge the communication gap between intelligent connected vehicles (ICVs) and infrastructure. However, the rapid movement of UAVs and ICVs poses significant challenges to the stability and reliability of communication links. Motivated by these challenges, integrated UAV–ICV networks can be viewed as vehicular delay-tolerant networks (VDTNs), where data delivery is accomplished through the “store-carry-forward” transmission mechanism. Since VDTNs exhibit social attributes, this paper first investigates the opportunistic transmission problem in the presence of selfish nodes. Then, by enabling node cooperation, this paper proposes an opportunistic transmission scheme for integrated UAV–ICV networks. To address the issue of node selfishness in practical scenarios, the proposed scheme classifies the degree of cooperation and analyzes the encounter probability between nodes. Based on this, information is initially flooded, and the UAV is selected for data distribution by jointly considering the node centrality, energy consumption, and cache size. Finally, simulation results demonstrate that the proposed scheme can effectively improve the delivery ratio and reduce the average delivery delay compared to state-of-the-art schemes.

With the continuous increase in power demand in aerospace, shipping, electricity, and other industries, a series of manufacturing requirements such as high precision, complex structure, and thin wall have been put forward for gas turbines. Gas turbine blades are the key parts of the gas turbine. Their manufacturing accuracy directly affects the fuel economy of the gas turbine. Thus, how to improve the manufacturing accuracy of gas turbine blades has always been a hot research topic. In this study, we perform a quantitative study on the correlation between process parameters and the overall wax pattern shrinkage of gas turbine blades in the wax injection process. A prediction model based on a generalized regression neural network (GRNN) is developed with the newly defined cross-sectional features consisting of area, area ratio, and some discrete point deviations. In the qualitative analysis of the cross-sectional features, it is concluded that the highest accuracy of the wax pattern is obtained for the fourth group of experiments, which corresponds to a holding pressure of 18 bars, a holding time of 180 s, and an injection temperature of 62 °C. The prediction model is trained and tested based on small experimental data, resulting in an average RE of 1.5% for the area, an average RE of 0.58% for the area ratio, and a maximum MSE of less than 0.06 ${\mathrm{mm}}^2$for discrete point deviations. Experiments show that the GRNN prediction model constructed in this study is relatively accurate, which means that the shrinkage of the remaining major investment casting procedures can also be modeled and controlled separately to obtain turbine blades with higher accuracy.