The electrode is one of the most important components of tubular direct methanol fuel cells (DMFC), and the coating process directly determines its performance. In the present research, a tubular electrode coating device was designed based on planetary gear structures, and the influence of the coating process parameters on the electrode structure’s performance was studied. The experimental results show that: the coating layer on the electrode surface prepared by the self-made device is uniform and dense, and the coating surface quality is better than a manual coating. The best coating environment temperature is 30–40 °C, and the coating spindle speed is 6.67 r/min. Under the condition in which Nafion 117 is used as the proton exchange membrane, the fuel cell is placed in 1 mol/L H2SO4 + 0.5 mol/L CH3OH electrolyte, and high-purity oxygen is fed at a rate of 100 mL/min, the power density of the electrode coated by the self-made device can reach 20.50 mW/cm2, which is about 2.4 times that of the electrode coated manually.

Due to variable cross-sections and a thin-walled structure, gas turbine blades have stringent dimensional, and geometrical tolerance requirements. Single-crystal hollow blades are manufactured using the following investment casting processes: ceramic core preparation, wax injection, ceramic coating, wax removal, metal casting, and finishing. The main causes of the final casting deformation are wax pattern deformation, core deflection, and metal solidification warpage. This paper proposes a numerical simulation method to predict the deformation of the wax pattern, core deflection, and the directional solidification (DS) process of large single-crystal blades. Additionally, it investigates the displacement field and the influence of casting process parameters on dimensional accuracy. Three groups of DS process parameters were selected for experiments, and the deformation prediction was in agreement with the experimental results. The selected blade section deformation is the smallest when the pouring temperature is 1530°C and the withdrawal rate is 5 mm/min. The proposed finite element model is efficient to predict the deformation in all the investment casting processes, providing geometric guidance for the control of the dimensional accuracy of the turbine blade.

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  mm2 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.