基于蒙特卡洛-自适应差分进化算法的飞机容差分配多目标优化方法

doi:10.7527/S1000-6893.2021.25278

材料工程与机械制造

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基于蒙特卡洛-自适应差分进化算法的飞机容差分配多目标优化方法

荆涛, 田锡天

西北工业大学机电学院, 西安 710072

收稿日期:2021-01-15 修回日期:2021-03-02 出版日期:2022-03-15 发布日期:2021-05-31
通讯作者: 田锡天 E-mail:tianxt@nwpu.edu.cn

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

JING Tao, TIAN Xitian

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

摘要/Abstract

摘要： 针对飞机复杂组件装配过程中，由于装配偏差的非线性叠加导致通用容差分配模型优化方法存在精度误差和效率低下的问题，提出一种容差分配模型多目标优化方法。首先，分别面向制造成本、装配性能、质量损失构建基于替代工艺的容差分配模型，并采用最优化理论建立多目标优化模型。然后，提出基于蒙特卡洛-自适应差分进化算法的多目标优化策略，其中基于装配偏差传递的图树模型提取偏差的非线性关系模型，结合提取的非线性关系模型采用蒙特卡洛方法对初始样本进行前处理，以提高初始种群的多样性；自适应差分进化算法中的突变阶段采用Lévy flight概率分布进行改进提高全局搜索效率和鲁棒性。通过飞机登机门组件验证容差分配方法，结果表明所提方法能够更加精确、高效的优化容差分配。相比于初始容差分配，优化容差分配结果使得制造成本降低了21.78%，质量损失降低了11.12%，装配性能提高了12.28%。

关键词: 飞机装配, 容差分配, 多目标优化, 非线性提取, 蒙特卡洛-自适应差分进化算法

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

中图分类号:

V262.4⁺1

荆涛, 田锡天. 基于蒙特卡洛-自适应差分进化算法的飞机容差分配多目标优化方法[J]. 航空学报, 2022, 43(3): 425278-425278.

JING Tao, TIAN Xitian. Multi-objective optimization method for aircraft tolerance allocation based on Monte Carlo-adaptive differential evolution algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 425278-425278.

参考文献

[1] 何胜强.飞机数字化装配技术体系[J].航空制造技术, 2010(23):32-37. HE S Q.Digital assembly technology system of aircraft[J].Aeronautical Manufacturing Technology, 2010(23):32-37(in Chinese).
[2] 隋少春, 许艾明, 黎小华, 等.面向航空智能制造的DT与AI融合应用[J].航空学报, 2020, 41(7):624173. SUI S C, XU A M, LI X H, et al.Fusion application of DT and AI for aviation intelligent manufacturing[J].Acta Aeronautica et Astronautica Sinica, 2020, 41(7):624173(in Chinese).
[3] 郭健彬, 谭欣欣, 孙宇锋, 等.基于最大容差域的容差设计方法[J].航空学报, 2009, 30(5):946-951. GUO J B, TAN X X, SUN Y F, et al.Tolerance design method based on maximum tolerance region[J].Acta Aeronautica et Astronautica Sinica, 2009,30(5):946-951(in Chinese).
[4] 许国康.大型飞机自动化装配技术[J].航空学报, 2008, 29(3):734-740. XU G K.Automatic assembly technology for large aircraft[J].Acta Aeronautica et Astronautica Sinica, 2008, 29(3):734-740(in Chinese).
[5] WANG Y, LI L, HARTMAN N W.Allocation of assembly tolerances to minimize costs[J].CIRP Annals-Manufacturing Technology, 2019, 68(1):13-16.
[6] EDEL D H, AUER T B.Determine the least cost combination for tolerance accumulations in a drive shaft seal assembly[J].Journal of Management in Engineering, 1964, 4(2):37-38.
[7] SPECKHART F H.Calculation of tolerance based on a minimum cost approach[J].Journal of Engineering for Industry, 1972, 94(2):447-453.
[8] SPOTTS M F.Allocation of tolerances to minimize cost of assembly[J].Journal of Engineering for Industry, 1973, 95(3):762-764.
[9] CHASE K W, KENNETH W, WILLIAM H, et al.Design issues in mechanical tolerance analysis[J].Manufacturing Review, 1988, 1(1):50-59.
[10] TAGUCHI G, WOODALL W H.Quality engineering in production systems[J].Journal of Quality Technology, 1989, 21(4):297-298.
[11] MAGHSOODLOO S.Optimization of mechanical assembly tolerances by incorporating Taguchi's quality loss function[J].Journal of Manufacturing Systems, 1995, 14(4):264-276.
[12] YE B, SALUSTRI F A.Simultaneous tolerance synthesis for manufacturing and quality[J].Research in Engineering Design, 2003, 14(2):98-106.
[13] HSIEH K L.The study of cost-tolerance model by incorporating process capability index into product lifecycle cost[J].International Journal of Advanced Manufacturing Technology, 2006, 28(5):638-642.
[14] RAO Y S.A genetic algorithm approach for simultaneous tolerance synthesis for manufacturing and quality with different stack-up conditions[J].International Journal of Applied Engineering Research, 2008, 3(9):4551-4562.
[15] 杨将新, 顾大强.基于神经网络的机械加工成本-公差模型[J].中国机械工程, 1996, 7(6):41-42. YANG J X, GU D Q.Machining cost-tolerance model based on neural network[J].China Mechanical Engineering, 1996, 7(6):41-42(in Chinese).
[16] 赵延明, 刘德顺, 徐小艳, 等.面向零件公差设计的产品质量损失成本模型[J].中国机械工程, 2011, 22(11):1347-1351. ZHAO Y M, LIU D S, XU X Y, et al.A novel quality loss cost model for product tolerance design[J].China Mechanical Engineering, 2011, 22(11):1347-1351(in Chinese).
[17] 吴文, 张伟社.基于制造-质量损失成本的公差优化分配法[J].长安大学学报(自然科学版), 2005(3):89-93. WU W, ZHANG W S.Optimal method of tolerance allocation based on comprehensive model of manufacture-quality loss cost[J].Journal of Chang' an University (Natural Science Edition), 2005(3):89-93(in Chinese).
[18] 彭和平, 刘晓军, 蒋向前.基于多重相关特征质量损失函数的并行公差设计[J].中国机械工程, 2010, 21(6):690-693, 697. PENG H P, LIU X J, JIANG X Q.Concurrent tolerance design based on quality loss function with multi-correlated characteristics[J].China Mechanical Engineering, 2010, 21(6):690-693, 697(in Chinese).
[19] SANZ L A, EMILIO G, JESUS P.A Proposal of cost-tolerance models directly collected from the manufacturing process[J].International Journal of Production Research, 2015, 54(15):4584-4598.
[20] MCKENNA V, JIN Y, MURPHY A.Cost-oriented process optimization through variation propagation management for aircraft wing spar assembly[J].Robotics and Computer Integrated Manufacturing, 2019, 57(6):435-451.
[21] CHASE K W, GAO J, MAGLABY S P.A comprehensive system for computer-aided tolerance analysis of 2-D and 3-D mechanical assemblies[J].IIE Transactions, 2000, 28(10):795-807.
[22] FENG C X, KUSIAK A.Robust tolerance synthesis with the design of experiments approach[J].Journal of Manufacturing Science and Engineering, 2000, 122(3):520-529.
[23] WANG G D, YANG Y, WANG W.Variable coefficients reciprocal squared model based on multi-constraints of aircraft assembly tolerance allocation[J].The International Journal of Advanced Manufacturing Technology, 2016, 82(1):227-234.
[24] ZHANG Y, JI S, ZHAO J, et al.Tolerance analysis and allocation of special machine tool for manufacturing globoidal cams[J].International Journal of Advanced Manufacturing Technology, 2016, 87(5-8):1-11.
[25] PRABHAHARAN G, ASOKAN P, RAJENDRAN S.Sensitivity-based conceptual design and tolerance allocation using the Continuous Ants Colony Algorithm (CACO)[J].International Journal of Advanced Manufacturing Technology, 2005, 25(5-6):516-526.
[26] HAQ A N, SIVAKUMAR K, SARAVANAN R, et al.Tolerance design optimization of machine elements using genetic algorithm[J].International Journal of Advanced Manufacturing Technology, 2005, 25(3-4):385-391.
[27] KRISHNA A G, RAO K M.Simultaneous optimal selection of design and manufacturing tolerances with different stack-up conditions using scatter search[J].The International Journal of Advanced Manufacturing Technology, 2006, 30(3-4):376-388.
[28] PRABHAHARAN G, RAMESH R, ASOKAN P.Concurrent optimization of assembly tolerances for quality with position control using scatter search approach[J].International Journal of Production Research, 2007, 45(21):4959-4988.
[29] COELHO L D S.Self-organizing migration algorithm applied to machining allocation of clutch assembly[J].Mathematics and Computers in Simulation, 2009, 80(2):427-435.
[30] CHENG Q, ZHAO H W, LIU Z F, et al.Robust geometric accuracy allocation of machine tools to minimize manufacturing costs and quality loss[J].ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C:Journal of Mechanical Engineering Science, 2015, 230(15):2728-2744.
[31] NATARAJAN J, SIVASANKARAN R, KANAG G.Bi-objective optimization for tolerance allocation in an interchangeable assembly under diverse manufacturing environment[J].International Journal of Advanced Manufacturing Technology, 2018, 95(5):1571-1595.
[32] JING T, TIAN X T, LIU X, et al.A multiple alternative processes-based cost-tolerance optimal model for aircraft assembly[J].The International Journal of Advanced Manufacturing Technology, 2020, 107(5-8):667-677.
[33] 贾宝惠, 于灵杰, 蔺越国, 等.基于AHP-SPA方法的民机修理级别确定综合分析模型[J].航空学报, 2017, 38(11):178-186. JIA B H, YU L J, LIN Y G, et al.Comprehensive analysis model for determination of civil aircraft repair level based on AHP-SPA method[J].Acta Aeronautica et Astronautica Sinica, 2017, 38(11):178-186(in Chinese).
[34] CUI L, LI G, LIN Q, et al.Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations[J].Computers and Operations Research, 2015, 67(3):155-173.
[35] CHENG L, WANG Q, LI J.Propagation analysis of variation for fuselage structures in multi-station aircraft assembly[J].Assembly Automation, 2018, 38(1):67-76.
[36] 薛羽, 庄毅, 张友益, 等.基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策[J].航空学报, 2013, 34(2):343-351. XUE Y, ZHUANG Y, ZHANG Y Y, et al.Multiple UCAV cooperative jamming air combat decision making based on heuristic self-adaptive discrete differential evolution algorithm[J].Acta Aeronautica et Astronautica Sinica, 2013, 34(2):343-351(in Chinese).
[37] PAVLYUKEVICH I.Lévy flights, non-local search and simulated annealing[J].Journal of Computational Physics, 2007, 226(2):1830-1844.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于蒙特卡洛-自适应差分进化算法的飞机容差分配多目标优化方法

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

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