A Prediction Model of Multi-stage Rotors Coaxiality Based on Neural Network

Abstract

For the assembly of multi-stage rotors, this paper proposes the coaxiality predicting model of multi-stage rotors based on neural network. The model takes the complicated operation of centering and tilting during the measurement of single-stage rotor machining error and the indeterminacy of saddle surface error transmission mechanism during the process of multi-stage rotors assembling into consideration. First of all, the paper proposes the depolarization and declination model of single-stage saddle surface rotor based on deep confidence neural network. And then, the single-stage rotor machining error is taken as the input amount into the BP neural network to establish the coaxiality predicting model of multi-stage saddle surface rotors. Finally, experimental measurements of the level-four core engine rotor are performed to verify the accuracy of multi-stage rotors coaxiality prediction model. The result shows that the coaxiality of multi-stage rotors can be effectively predicted by the neural network. The average error of coaxiality prediction is 1.0μm, the standard deviation is 0.7μm, compared to the traditional method, the mean error and standard deviation decreases by 81.8% and 73.1%, respectively, which can reflect the advantages of the coaxiality prediction model of BP neural network.

Analysis of force and wear mechanism of shield hob under conventional working conditions

“…During hob excavation, wear occurs between the hob and the geotechnical surface, which leads to a decrease in the diameter of the hob and an increase in the width of the cutting edge [12]. According to the empirical formula ( 6), the amount of radial wear is related to parameters such as the driving distance, penetration, and cutting edge width.…”