Technology of assembly sequence planning for reflector

被引：0
|
作者：
Wang D. [1 ]
Duan X. [1 ]
Shao X. [1 ]
Gao F. [1 ]
机构：
[1] Key Laboratory of Manufacturing Equipment of Shaanxi Province, Xi'an University of Technology, Xi'an
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 06期
关键词：
Assembly sequence planning; Multi-objective genetic algorithm; Panel assembly; Reflector antenna;
D O I：
10.13196/j.cims.2020.06.024
中图分类号：
学科分类号：
摘要：
To improve the surface accuracy and assembly efficiency of the large reflector antenna, a assembly optimization method for antenna panels was studied. By considering the assembly process, the mathematical model for Assembly Sequence Planning (ASP) of reflector was constructed based on traditional Genetic Algorithm (GA), whose fitness function consisted of evaluation of assembly deformation, assembly cycle and assembly labor cost. Considering the assembly accuracy and consumption in the assembly process, the multi-objective optimization function was obtained through weighting the corresponding items.The finite element simulation model was given to simulate the dynamic assembly process of reflector according to the algorithm result of ASP based on GA, and the simulation results was used to evaluate ASP algorithm result timely and accurately. Taking the example of 9 m reflector antenna, the algorithm results and simulation results of different objective functions were discussed. The assembly sequence with the highest precision, with the best economic efficiency, and the assembly sequence considering both the improvement of assembly precision and economical efficiency were all calculated. The above results demonstrated the proposed ASP method based on GAand its mathematical model were accuracy and efficiency. © 2020, Editorial Department of CIMS. All right reserved.
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页码：1679 / 1690
页数：11
相关论文
共 18 条
  • [11] YU Haishan, LI Yuan, ZHANG Kaifu, Comprehensive evaluation system for multi-target aircraft assembly sequence, Journal of Northwestern Polytechnical University, 24, 6, pp. 808-811, (2006)
  • [12] ZHOU Kaijun, LI Dongbo, Product assembly sequences planning based on genetic simulated annealing algorithm, Computer Integrated Manufacturing Systems, 12, 7, pp. 1037-1041, (2006)
  • [13] ZENG Bing, LI Mingfu, ZHANG Yi, Et al., Research on assembly sequence planning based on firefly algorithm, Journal of Mechanical Engineering, 49, 11, pp. 177-184, (2013)
  • [14] LI Yuan, ZHANG Kaifu, WANG Ting, Et al., Assembly sequence planning optimization for aircraft assembly based on GA, Computer Integrated Manufacturing Systems, 12, 2, pp. 188-191, (2006)
  • [15] HUANG Jie, DU Pingan, LIAO Weizhi, Genetic algorithm for assembly sequences planning based on assembly constraint, Computer Integrated Manufacturing Systems, 13, 4, pp. 756-761, (2007)
  • [16] LU C, WONG Y S, FUH J Y H., An enhanced assembly planning approach using amulti-objective genetic algorithm[J], Proceedings of the Institution of Mechanical Engineers Part B Journal of EngineeringManufacture, 220, 2, pp. 255-272, (2006)
  • [17] LI Yong, LIU Jianchang, WANG Yu, Multi-objective optimization of improved weight adaptive GA and cold continuous rolling schedule, Control Theory & Applications, 26, 6, pp. 687-693, (2009)
  • [18] SOMANI A, CHAKRABARTI P P., An evolutionary algorithm-based approach to automated design of analog and RF circuits using adaptive normalized cost functions[J], IEEE Transactions on Evolutionary Computation, 11, 3, pp. 336-352, (2007)
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