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A multiple alternative processes-based cost-tolerance optimal model for aircraft assembly

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Abstract

In the aircraft manufacturing process, the tolerance allocation of complex components has an extremely important impact on the life cycle of the aircraft. With the increasing demand for aircraft assembly performance and reliability, tolerance allocation has become one of the most concerned issues for process designers. However, the traditional cost-tolerance models hardly took into account the effects of multiple alternative manufacturing processes. The global search strategy and convergence of optimization methods of the model were insufficient, and it was difficult to obtain more optimized results. Therefore, this paper proposed a novel cost-tolerance model that considers the impact of multiple alternative manufacturing processes on component manufacturing cost and quality loss. Then, a hybrid optimization algorithm combining Monte Carlo simulation and self-adaptive differential evolution (SADE) was presented to achieve cost minimization while ensuring high assembly accuracy. The Monte Carlo simulation was used to solve the problem of tolerance superposition and provided a proper initial population for SADE, which can accelerate the convergence speed and enhances the robustness of the SADE. Moreover, based on the traditional SADE, a new mutation strategy, which expands the diversity of the population, was proposed in combination with the Lévy distribution. Improved SADE algorithm had a good optimization effect for large space, nonlinear and non-derivable discrete problems. Finally, a case study of aircraft door assembly was illustrated to verify the effectiveness of the proposed model. The experimental results show the advantage of the method in achieving the cost reduction compared with traditional method.

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Abbreviations

C T :

Total manufacturing cost of an assembly

C P :

Processing cost of components

C Q :

Cost of quality loss

N i :

Numbers of parts required to produce Y assemblies that meet the requirements

α i :

The probability that the ith part meets its accuracy range

fi(x):

Quality characteristic probability density function of the ith component

U i :

Upper deviation values of the ith component

L i :

Lower deviation values of the ith component

C i :

Processing cost of a single component

A i :

Fixed cost of processing

B i :

Influence coefficient of the tolerance on processing cost model

m i :

Index of the tolerance on processing cost model

T(ψ) :

Tolerance domain

σ i :

Standard deviation of the ith component

k i :

Constants related to component specifications

T M :

Limit value of the unilateral tolerance superposition of the dimension chain

E :

Coefficient of quality loss

T i :

Manufacturing deviation of the ith component

Np :

Initial population size

G :

Current number of iterations

G m :

Total number of iterations

r1i,r2i,r3i,r4i :

Mutually different integer randomly generated in the range of [1, Np] and not equal to the index i

x ibest,G :

Optimal value generated when the number of iterations is G

v i,G :

Mutation vector of the generation G

K :

Random value between [0,1]

F 0 :

Initial mutation operator

F :

Mutation operator

L(χ):

Lévy distribution function

β :

Index

χ :

Search step size

Γ:

Standard gamma function

μ :

Constant coefficient, initialized to 1.5 in this paper

randb(j):

jth estimated value generated by the random number simulator between [0,1]

rnbr(i)∈(1,2,...,D):

Randomly selected sequence

C r :

Crossover operator

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Authors and Affiliations

  1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

    Tao Jing, Xitian Tian, Xiang Liu, Hao Hu, Min Zhang & Bo Li

  2. Institute of Intelligent Manufacturing, Northwestern Polytechnical University, Xi’an, 710072, China

    Tao Jing, Xitian Tian, Xiang Liu, Hao Hu, Min Zhang & Bo Li

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  1. Tao Jing
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  2. Xitian Tian
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  3. Xiang Liu
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  4. Hao Hu
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  5. Min Zhang
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Correspondence to Xitian Tian.

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Jing, T., Tian, X., Liu, X. et al. A multiple alternative processes-based cost-tolerance optimal model for aircraft assembly. Int J Adv Manuf Technol 107, 667–677 (2020). https://doi.org/10.1007/s00170-020-05020-7

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