This paper proposes a method for predicting concentricity and perpendicularity based on PSO-BP neural network in order to solve the problem of low accuracy for aero engine multistage rotors assembly. The influence factors of error propagation in the assembly are analyzed based on the characteristics of rotor structure and assembly process. And neural networks for predicting concentricity and perpendicularity of multistage rotors assembly are established. The particle swarm algorithm is used to optimize the hyperparameters of the neural network and the optimal hyperparameters can be obtained. In order to verify the effectiveness of the concentricity and perpendicularity prediction method proposed in this paper, experiments are carried out for four rotors assembly with precision rotary measuring instrument. The results show that for the 30 groups of testing samples, the average deviations of concentricity and perpendicularity by PSO-BP neural network prediction method are 1.0 μm and 0.6 μm, respectively. The prediction accuracy of concentricity and perpendicularity of final assembly are improved by 4.5 μm and 2.6 μm, respectively, compared with the traditional assembly method. The proposed method in this paper can be used not only for the guidance of multistage rotors assembly of aero engine, but also for the tolerance allocation in the design process.

The paper aims to provide an adjustment method to reduce coaxiality and initial unbalance for precision rotors assembly, such as aero engine, gas turbine and so on. The propagation process of actual geometry and mass centers is analyzed in the assembly. The coaxiality and initial unbalance are reduced by controlling the assembly angle of each stage rotor. The effectiveness of the proposed assembly method is verified through experiments. Experimental results show that compared with the direct adjustment strategy, the coaxiality and initial unbalance are reduced by 41% and 50% in four rotors assembly, respectively. The proposed method can be used to improve assembly accuracy, especially for multistage rotors precision assembly.

The accurate measurement of stress at different depths in the end face of a high-pressure compressor rotor is particularly important, as it is directly related to the assembly quality and overall performance of aero-engines. The ultrasonic longitudinal critically refracted (LCR) wave is sensitive to stress and can measure stress at different depths, which has a prominent advantage in stress non-destructive measurements. In order to accurately characterize the propagation depth of LCR waves and improve the spatial resolution of stress measurement, a finite element model suitable for the study of LCR wave propagation depths was established based on a wave equation and Snell law, and the generation and propagation process of LCR waves are analyzed. By analyzing the blocking effect of grooves with different depths on the wave, the propagation depth of the LCR wave at seven specific frequencies was determined in turn. On this basis, the LCR wave propagation depth model is established, and the effects of wedge materials, piezoelectric element diameters, and excitation voltages on the propagation depth of LCR waves are discussed. This study is of great significance to improve the spatial resolution of stress measurements at different depths in the end face of the aero-engine rotor.

To simultaneously achieve a high optical‐transparency and Wi‐Fi bandpass filtering characteristics for optical windows, a novel mesh‐coated frequency selective surface (FSS) is designed and fabricated using an ultraviolet lithography technique. Simulation and experimental results show that the resonant centre frequency of the proposed mesh‐coated FSS is about 5.3 GHz with a transmittance loss of −2 dB, while its optical transmittance is above 87.5% in the infrared (IR) waveband ranging from 1.35 to 3.3 μm. Moreover, such a FSS has the merits of ameliorable electromagnetic shielding effectiveness and improvable optical transmittance by optimising the thickness of the mesh layer. Both the design procedure and experimental measuring results are presented and discussed.

The precision measurement and evaluation of the geometry form error for high-end precision components is one of the most important tasks for metrologists. In this paper, a novel hybrid flatness error evaluation method is proposed to measure and evaluate the flatness error for large-scale point cloud data. This method is based on the convex hull algorithm and the improved particle swarm optimization (IPSO) algorithm. Not only are a large number of redundant measurement data points effectively removed through the convex hull algorithm, but also this operation does not lose any valuable information for flatness error evaluation. Then an accurate mathematical model for flatness error evaluation is established based on the minimum zone criteria recommended by ISO, and then the IPSO algorithm is adopted to solve this complex non-linear optimization problem. In particular, the non-linear dynamic inertia weights w and learning factors c ₁ , c ₂ are introduced to improve the calculation accuracy. Finally, the proposed method is verified through an experiment measuring and evaluating the flatness error of an aero-engine rotor connection surface. The effective measurement data points are reduced from 18 000 to 856, or 4.76%, which is significant compared to the original data points. The flatness error evaluation result is 369 µm according to the IPSO method that we propose. Last but not least, we compare our method with the traditional least squares method and unmodified particle swarm optimization algorithm. The calculation results indicate that the evaluation accuracy is increased by 21 µm and 44 µm, respectively.