Multi-Source Error Coupling and Tolerance Optimization for Improving the Precision of Automated Assembly of Aircraft Components

In automated aircraft assembly, achieving high-precision alignment is essential due to the presence of multiple coupled error sources that significantly affect final product quality. This study proposes an integrated framework to model multi-source errors via a directed coupling network and to quantify their impact using Monte Carlo simulations. To reduce the complexity of tolerance allocation, Sobol-based global sensitivity analysis is applied to identify dominant contributors to assembly deviations. The most influential parameters are retained for multi-objective optimization using the non-dominated sorting genetic algorithm II (NSGA-II). This framework enables the minimization of key assembly deviations while maintaining computational efficiency. Experimental validation on a typical helicopter ring assembly demonstrates that the proposed optimization approach increases the position pass rate from 67.4% to 100.0% and the coaxiality pass rate from 93.5% to 100.0%. The corresponding process capability indices (CPK) also improve significantly, from 0.31 to 2.19 for position and from 0.62 to 1.06 for coaxiality. These improvements not only satisfy high-precision assembly requirements but also exceed common industry benchmarks, demonstrating the method’s practical effectiveness under multi-source uncertainty.