Prediction of the Parallelism Error and Unbalance of Aero-Engine Rotors Based on Intelligent Algorithm
Ruirui Li, Chuanzhi Sun, Yongmeng Liu, Yingjie Mei, Jiubin Tan
Abstract
Accurate measurement of geometric errors and mass properties is essential to aero-engine manufacturing. The measurement results can provide a reference for assembly. To address the problem of poor parallelism and unbalance prediction accuracy of hyperbolic paraboloid rotors, this paper designs a classifier. It achieves precise differentiation of the rotor surface by combining the Principal Component Analysis (PCA) and Support Vector Machine (SVM). The results show that the surface classifier accuracy is 99%. Then this paper combines the Genetic Algorithm (GA) and Back Propagation Neural Network (BPNN) to predict the parallelism and unbalance of the hyperbolic parabolic rotors. Compared with the traditional prediction model, the GA-BP neural network is more accurate in predicting the parallelism and unbalance of the multi-stage assembled rotors. The trend of the predicted results is more consistent with the experimental data. The prediction error of parallelism is reduced by 7.5 μm, and the maximum deviation of unbalance is reduced by 250.6 g·mm through the GA-BP neural network. The surface classifier and intelligent algorithm designed in this paper can provide a reference for rotor manufacturing and assembly.