Regulation of geometric assembly accuracy for aero-engine multi-stage rotors based on multi-population particle swarm-genetic hybrid algorithm

Abstract The modeling and intelligent regulation of geometric assembly accuracy in multi-stage rotors are crucial to the precision and stability of aero-engine rotor systems. To overcome the low computational efficiency of existing error models and the challenge of determining optimal assembly phases, this paper proposes an efficient geometric error propagation model based on geometric algebra. Additionally, an improved multi-population particle swarm-genetic hybrid algorithm (MPSOGA) that employs a combinatorial optimization mechanism to better simulate natural evolutionary principles is introduced. By introducing a secondary optimization concept to holistically consider assembly errors, the regulation of the coaxiality of the multi-stage rotors is integrated with the regulation of the concentricity of the critical-stage rotor to further improve the geometric assembly accuracy. The simulation experiment results of the five-stages rotors show the method reduces the coaxiality of multi-stage rotors by 63.26% and the concentricity of critical-stage rotor by 99.66%, proving the geometric error propagation model’s superiority and the hybrid algorithm’s effectiveness. This provides an effective solution to the problem of unqualified geometric assembly accuracy in aero-engine multi-stage rotors, improving assembly quality and production efficiency.