Abstract
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.
Highlights
In the field of advanced aero engine manufacturing, the quality of precise multistage rotors assembly has an important impact on the rotation quality [1], [2]
Compared with the traditional assembly strategy, the prediction accuracy of concentricity and perpendicularity average deviations of four rotors assembly using PSO-BP neural network assembly strategy are improved by 4.5 μm and 2.6 μm, respectively
DISCUSSIONS AND CONCLUSIONS A prediction method of concentricity and perpendicularity based on PSO-BP neural network is proposed in this paper to improve the assembly accuracy for concentricity and perpendicularity of multistage rotors assembly
Summary
In the field of advanced aero engine manufacturing, the quality of precise multistage rotors assembly has an important impact on the rotation quality [1], [2]. The prediction method of concentricity and perpendicularity for the multistage rotors assembly has not been established, which takes both tightening torque and geometric errors into account. The proposed PSO-BP neural network method can improve the prediction accuracy of concentricity and perpendicularity for the multistage rotors assembly. The experimental results show that the average deviation and the standard deviation of concentricity of final assembly using PSO-BP neural network are 1.0 μm and 0.7 μm, respectively. Compared with the traditional assembly strategy, the prediction accuracy of concentricity and perpendicularity average deviations of four rotors assembly using PSO-BP neural network assembly strategy are improved by 4.5 μm and 2.6 μm, respectively
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