Measurement-informed optimization of aero-engine blade arrangement considering manufacturing-assembly integrated errors

Residual unbalance in aero-engine blade-disk systems is strongly influenced by the coupled transmission of manufacturing and assembly errors. This study proposes a measurement-informed blade arrangement optimization method that integrates blade mass moments, disk eccentricity, and assembly-angle deviations into a unified unbalance model. Coordinate measurements, blade mass-moment measurements, and assembly-position error identification are used to establish an integrated error transmission model, in which multiple error sources are represented as resultant unbalance vectors. The blade arrangement is formulated as a permutation optimization problem that minimizes resultant unbalance. A robust hybrid genetic algorithm with variable-neighborhood search is developed, combining population-based global exploration with local refinement through swap, insertion, and reversal operations. Unlike conventional strategies mainly relying on blade mass moments or empirical heavy-light alternation, the proposed method incorporates disk eccentricity and assembly-angle deviations into the unbalance-vector calculation, enabling phase compensation among measured error-induced unbalance components. Dynamic balancing experiments using actual machined blades and a disk show that the optimized arrangement reduces vibration amplitude from 0.0114 m/s 2 to 0.0069 m/s 2 , a 39.43 % reduction, demonstrating improved arrangement effectiveness and dynamic balancing performance.