Research on intelligent assembly method of aero-engine multi-stage rotors based on SVM and variable-step AFSA-BP neural network

The quality of the aero-engine rotors assembly determines the overall performance of the engine. Aiming at the problems of rotors assembly with different plane types, we proposes a rotor plane classification method based on SVM by using the profile data of PCA dimension reduction. Meanwhile, for the unilateral-tilt plane rotors, the three-objective rotors assembly method of coaxiality, unbalance amount and perpendicularity based on the rigid rotor model is established. For the hyperbolic paraboloid rotors, an intelligent assembly method based on AFSA-BP neural network for coaxiality, unbalance amount and perpendicularity is established. The experiment is based on the double-column ultra-precision measuring instrument and V4L vertical balancing machine and HL5UB horizontal balancing machine to measure rotors geometry and unbalance data. The experimental results show that the plane type classification accuracy can reach 99 %. The prediction error of the coaxiality of the unilateral-tilt plane rotors assembly is 5.1 μm, the prediction error of the unbalance amount is 196 g·mm, and the prediction error of the perpendicularity is 0.6 μm. The average prediction error of the coaxiality of the hyperbolic paraboloid rotors assembly is 0.9 μm, and the average prediction error of the unbalance amount is 73 g·mm, and the average prediction error of the perpendicularity is 0.2 μm. Our method provides a reliable assembly solution for aero-engine rotors assembly and meets actual assembly requirements.