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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2022, Vol. 43 ›› Issue (3): 425278-425278.doi: 10.7527/S1000-6893.2021.25278

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Multi-objective optimization method for aircraft tolerance allocation based on Monte Carlo-adaptive differential evolution algorithm

JING Tao, TIAN Xitian   

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2021-01-15 Revised:2021-03-02 Online:2022-03-15 Published:2021-05-31

Abstract: The optimization method of the general tolerance allocation model has precision errors and low efficiency due to nonlinear stack-up of assembly deviations in the assembly process of complicated aircraft components.In this paper, a novel multi-objective optimization method of tolerance allocation model is proposed.First, a tolerance allocation model for the manufacturing cost, assembly performance, and quality loss of the aircraft is constructed based on alternative processes.A multi-objective optimization model is established using the optimization theory.Then, a multi-objective optimization strategy is proposed based on the Monte Carlo-adaptive differential evolution algorithm, in which the non-linear relationship model of deviation is extracted based on the graph tree model of assembly deviation propagation.Based on the non-linear relationship model extracted, the initial samples are pre-processed using the Monte Carle method to improve the diversity of the initial population.In the mutation stage in the adaptive differential evolution algorithm, the Lévy flight probability distribution is used to improve the global search efficiency and robustness.The tolerance allocation method is verified by assembly of the aircraft boarding gate components.The results demonstrate that the proposed method can optimize tolerance allocation more accurately and efficiently.Compared with the initial tolerance allocation, the optimized tolerance allocation results in reduction of the manufacturing cost by 21.78% and the quality loss by 11.12%, and increase of assembly performance by 12.28%.

Key words: aircraft assembly, tolerance allocation, multi-objective optimization, non-linear extraction, Monte Carlo-adaptive differential evolution algorithm

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