Prediction and Optimization of Assembly Accuracy for Multistage Rotors in Aeroengines

Accurate prediction and optimization of assembly accuracy are critical to ensuring assembly quality and efficiency for multistage connected aero-engine rotors. To mitigate the effects of residual alignment errors induced by repeated component measurements and to avoid the formation of bowed rotors caused by conventional stacking strategies that only minimize parallel misalignment, a harmonic decomposition-based registration method is proposed to unify inconsistent measurement datums among multiple setups. Meanwhile, key assembly process parameters are considered simultaneously, including front-and-rear support concentricity, front-and-rear bearing mounting face end-face runout, rotor blade-tip runout, and rotor unbalance. Taking the discrete assembly phase angles of each rotor stage as independent variables, a multi-objective genetic algorithm is adopted to realize the assembly accuracy prediction and optimization of multistage flange-bolted rotors. The proposed method is validated using a four-stage simulated rotor assembly. Experimental results show that the harmonic decomposition-based registration method improves the average geometric prediction accuracy of rotor assembly by 1.2 percentage points, with the prediction error of geometric assembly parameters for each stage not exceeding 8.4% and the unbalance prediction error not exceeding 29.0%. Compared with random assembly, four-objective comprehensive optimization achieves significant reductions in all objectives: front-and-rear support concentricity is reduced by 66.2%, front-and-rear support shoulder end-face runout by 63.9%, blade-tip runout by 16.7%, and unbalance by 33.8%. The residual alignment error compensation method and stacking optimization strategy proposed in this study provide valuable engineering guidance for improving rotor assembly prediction accuracy and enhancing assembly reliability.