Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (7): 1457-1463    DOI: 10.3785/j.issn.1008-973X.2022.07.021
    
Shape control method of fuselage driven by digital twin
Yong-sheng ZHAO1,2(),Rui-xiang LI1,2,Na-na NIU1,3,Zhi-yong ZHAO1,3
1. Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100020, China
2. Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100020, China
3. Machinery Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Technology, Beijing University of Technology, Beijing 100020, China
Download: HTML     PDF(1277KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A method of fuselage shape control based on digital twin drive was proposed in order to solve the problems of low precision, low efficiency and excessive local stress of manual adjustment. A digital twin system integrating shape control strategy optimization algorithm and virtual debugging technology was constructed. The digital twin system was driven by real-time data. The data interaction and the dynamic mapping between physical space and virtual space of fuselage shape control system were realized. The optimization problem of shape control strategy combining genetic algorithm and deep learning was analyzed. Then the availability of shape control strategy was verified through the ANSYS batch multi-load step method. The shape of fuselage was adjusted by maintaining the stress balance of fuselage barrel section. The experimental results show that the digital twin system and shape control strategy can effectively improve the control accuracy of fuselage barrel section shape by 25.8%, improve the control efficiency of fuselage barrel section shape by 414.3% and reduce the local maximum stress by 42.5%.



Key wordsdigital twin      fuselage shape      genetic algorithm      deep learning      strategy optimization     
Received: 24 December 2021      Published: 26 July 2022
CLC:  V 264  
Fund:  国家自然科学基金资助项目(52075012);航天伺服驱动与传动技术实验室开放基金资助项目(LASAT-20210103)
Cite this article:

Yong-sheng ZHAO,Rui-xiang LI,Na-na NIU,Zhi-yong ZHAO. Shape control method of fuselage driven by digital twin. Journal of ZheJiang University (Engineering Science), 2022, 56(7): 1457-1463.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.07.021     OR     https://www.zjujournals.com/eng/Y2022/V56/I7/1457


数字孪生驱动的机身形状控制方法

针对人工调整机身筒段形状过程中存在的精度低、效率低以及局部过大应力的问题,提出基于数字孪生驱动的机身形状控制方法,搭建融合形状控制策略优化算法和虚拟调试技术的数字孪生系统. 在实时数据的驱动下,实现机身形状控制系统物理空间与虚拟空间的数据交互、动态映射. 研究结合遗传算法和深度学习的形状控制策略优化问题,通过ANSYS批处理多载荷步的方法,验证形状控制策略的可用性,在保持机身筒段应力均衡的状态下调整机身形状. 实验结果表明,利用构建的数字孪生系统和形状控制策略,可以有效地将筒段形状控制精度提高25.8%,将筒段形状控制效率提高414.3%,将局部最大应力减小42.5%.


关键词: 数字孪生,  机身形状,  遗传算法,  深度学习,  策略优化 
Fig.1 Digital twin system architecture of fuselage shape adjustment
Fig.2 Flow chart of fuselage shape control
Fig.3 Comparison of predicted stress and actual stress
Fig.4 Phased shape control strategy
Fig.5 Flow of fuselage shape control strategy optimization
Fig.6 Test bench for shape adjustment of fuselage tube section
Fig.7 Schematic of fuselage deformation
序号 ΔS/mm 序号 ΔS/mm
1 5.44 7 ?13.25
2 6.41 8 ?10.64
3 1.86 9 ?2.33
4 ?3.36 10 1.04
5 ?9.03 11 8.31
6 ?12.87 12 7.36
Tab.1 Result of fuselage deformation measurement
阶段 S1 S2 S3 S4 S5 S6
1 5.03 ?1.12 ?12.91 ?13.35 ?2.27 6.83
2 4.96 ?0.83 ?12.65 ?13.06 ?2.66 6.35
3 4.88 ?0.62 ?12.32 ?12.74 ?2.76 6.06
4 4.85 ?0.57 ?11.83 ?12.28 ?2.65 5.74
5 4.56 ?0.42 ?11.36 ?11.72 ?2.48 5.35
6 4.24 ?0.45 ?10.84 ?11.23 ?2.27 4.93
7 3.98 ?0.54 ?10.38 ?10.75 ?2.07 4.54
8 3.62 ?0.58 ?9.87 ?10.24 ?1.89 4.18
9 3.32 ?0.69 ?9.39 ?9.78 ?1.68 3.77
10 3.14 ?0.64 ?8.84 ?9.29 ?1.45 3.33
11 2.83 ?0.58 ?8.31 ?8.77 ?1.22 2.93
12 2.54 ?0.55 ?7.82 ?8.22 ?1.04 2.56
13 2.39 ?0.46 ?7.35 ?7.74 ?0.86 2.39
14 2.04 ?0.47 ?6.86 ?7.26 ?0.68 2.08
15 1.86 ?0.32 ?6.38 ?6.72 ?0.47 1.87
16 1.67 ?0.33 ?5.82 ?6.23 ?0.38 1.69
17 1.48 ?0.36 ?5.33 ?5.77 ?0.39 1.49
18 1.22 ?0.25 ?4.84 ?5.24 ?0.27 1.26
19 1.04 ?0.28 ?4.32 ?4.76 ?0.25 1.08
20 0.86 ?0.22 ?3.81 ?4.28 ?0.22 0.87
21 0.78 ?0.17 ?3.32 ?3.78 ?0.16 0.78
22 0.69 ?0.16 ?2.83 ?3.27 ?0.15 0.65
23 0.51 ?0.14 ?2.35 ?2.75 ?0.13 0.52
24 0.42 ?0.05 ?1.81 ?2.22 ?0.02 0.46
25 0.34 ?0.13 ?1.35 ?1.74 ?0.08 0.33
26 0.29 0.05 ?0.81 ?1.27 0.07 0.29
27 0.27 0.11 ?0.33 ?0.75 0.16 0.28
28 0.23 0.09 0.16 ?0.25 0.22 0.12
29 0.19 0.13 0.21 0.16 0.16 ?0.08
30 0.13 ?0.09 0.17 0.13 0.08 ?0.18
Tab.2 Fuselage shape control strategy
Fig.8 Simulation environment of shape control strategy
序号 ΔS/mm 序号 ΔS/mm
1 0.16 7 0.23
2 0.21 8 0.13
3 0.11 9 ?0.05
4 ?0.07 10 ?0.11
5 ?0.12 11 0.08
6 0.18 12 0.13
Tab.3 Result of fuselage deformation measurement
[1]   王清运, 刘勇, 徐志刚, 等 大型舱段校正机构设计与仿真分析[J]. 机械设计, 2019, 36 (11): 25- 31
WANG Qing-yun, LIU Yong, XU Zhi-gang, et al Design and simulation analysis of the deformation-correction mechanism for large cabin sections[J]. Journal of Machine Design, 2019, 36 (11): 25- 31
[2]   WEN Y C, YUE X W, HUNT J H, et al Feasibility analysis of composite fuselage shape control via finite element analysis[J]. Journal of Manufacturing Systems, 2018, 46: 272- 281
doi: 10.1016/j.jmsy.2018.01.008
[3]   DU J, CAO S S, HUNT J H, et al. Optimal shape control via L∞ loss for composite fuselage assembly [EB/OL]. [2021-12-01]. https://www.webofscience.com/wos/alldb/full-record/WOS:000780697200001.
[4]   YUE X, WEN Y, HUNT J H, et al Surrogate model-based control considering uncertainties for composite fuselage assembly[J]. Journal of Manufacturing Science and Engineering, 2018, 140 (4): 041017
[5]   GRAESSLER I, POEHLER A. Integration of a digital twin as human representation in a scheduling procedure of a cyber-physical production system [C]// 2017 IEEE International Conference on Industrial Engineering and Engineering Management. Singapore: IEEE, 2017: 289-293.
[6]   REINER A, SEBASTIAN H, KLAUS S, et al Digital twin technology: an approach for industrie 4.0 vertical and horizontal lifecycle integration[J]. Information Technology, 2018, 60 (3): 125- 132
[7]   BRENNER B, HUMMEL V Digital twin as enabler for an innovative digital shopfloor management system in the ESB logistics learning factory at Reutlingen–University[J]. Procedia Manufacturing, 2017, 9: 198- 205
doi: 10.1016/j.promfg.2017.04.039
[8]   PADOVANO A, LONGO F, NICOLETTI L, et al A digital twin based service oriented application for a 4.0 knowledge navigation in the smart Factory – ScienceDirect[J]. IFAC-PapersOnLine, 2018, 51 (11): 631- 636
doi: 10.1016/j.ifacol.2018.08.389
[9]   CHEN Z, HUANG L Digital twins for information-sharing in remanufacturing supply chain: a review[J]. Energy, 2021, 220: 119712
doi: 10.1016/j.energy.2020.119712
[10]   HNAL A, SCHNELLHARDT T, WENKLER E, et al. The development of a digital twin for machining processes for the application in aerospace industry [C]// 53rd CIRP Conference on Manufacturing Systems. Amsterdam: [s. n.], 2020, 93: 1399-1404.
[11]   GLAESSGEN E, STARGEL D. The digital twin paradigm for future NASA and U. S. air force vehicles [C]// 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Honolulu: AIAA, 2012: 23-26.
[12]   CAI Y, CHEN L, CHEN Z Design and implementation of a knowledge engineering-oriented aircraft assembly fault management platform[J]. Advanced Manufacturing Technology, 2020, 63 (4): 96- 100
[13]   陶飞, 刘蔚然, 张萌, 等 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25 (1): 1- 18
TAO Fei, LIU Wei-ran, ZHANG Meng, et al Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25 (1): 1- 18
[14]   QI Q, TAO F, HU T, et al Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems, 2021, 58 (B): 3- 21
[15]   TAO F, XIN M A, HU T, et al Research on digital twin standard system[J]. Computer Integrated Manufacturing Systems, 2019, 25 (10): 2405- 2418
[16]   刘蔚然, 陶飞, 程江峰, 等 数字孪生卫星: 概念, 关键技术及应用[J]. 计算机集成制造系统, 2020, 26 (3): 565- 588
LIU Wei-ran, TAO Fei, CHENG Jiang-feng, et al Digital twin satellite: concept, key technologies and applications[J]. Computer Integrated Manufacturing Systems, 2020, 26 (3): 565- 588
[17]   徐慧, 邹孝付, 王海天, 等. 基于数字孪生的化纤长丝落卷作业优化方法及验证[EB/OL]. [2021-11-15]. http://kns.cnki.net/kcms/detail/11.59 46.TP.20211112.1723.018.html.
XU Hui, ZOU Xiao-fu, WANG Hai-tian, et al. Optimization method and justification of chemical fiber filament doffing operation based on digital twin [EB/OL]. [2021-11-15]. http://kns.cnki.net/kcms/detail/11.59 46.TP.20211112.1723.018.html.
[18]   张佳朋, 刘检华, 龚康, 等 基于数字孪生的航天器装配质量监控与预测技术[J]. 计算机集成制造系统, 2021, 27 (2): 605- 616
ZHANG Jia-peng, LIU Jian-hua, GONG Kang, et al Spacecraft assembly quality control and prediction technology based on digital twin[J]. Computer Integrated Manufacturing Systems, 2021, 27 (2): 605- 616
[19]   LI Ming-chao, LU Qiao-rong, BAI Shuo, et al Digital twin-driven virtual sensor approach for safe construction operations of trailing suction hopper dredger[J]. Automation in Construction, 2021, 132: 103961
[20]   XU Zheng, JI Fen-zhu, DING Shui-ting, et al Digital twin-driven optimization of gas exchange system of 2-stroke heavy fuel aircraft engine[J]. Journal of Manufacturing Systems, 2021, 58 (B): 132- 145
[21]   TAO F, ZHANG M, LIU Y, et al Digital twin driven prognostics and health management for complex equipment[J]. CIRP Annals, 2018, 67 (1): 169- 172
doi: 10.1016/j.cirp.2018.04.055
[22]   徐荣飞, 范开国. 数字孪生的电主轴热特性研究[EB/OL]. [2021-10-22]. http://kns.cnki.net/kcms/detail/42.1294.TH.20211022.1335.010.html.
XU Rong-fei, FAN Kai-guo. Research on thermal characteristics of motorized spindle based on digital twin [EB/OL]. [2021-10-22]. http://kns.cnki.net/kcms/detail/42.1294.TH.20211022.1335.010.html.
[23]   张胜文, 杨凌翮. 数字孪生驱动的离心泵机组故障诊断方法研究[EB/OL]. [2021-10-08]. http://kns.cnki.net/kcms/detail/11.5946.tp.20211004.2143.002.html.
ZHANG Sheng-wen, YANG Ling-he. Fault diagnosis method of centrifugal pump driven by digital twin [EB/OL]. [2021-10-08]. http://kns.cnki.net/kcms/detail/11.5946.tp.20211004.2143.002.html.
[24]   陶飞, 张萌, 程江峰, 等 数字孪生车间: 一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23 (1): 1- 9
TAO Fei, ZHANG Meng, CHENG Jiang-feng, et al Digital twin workshop: a new paradigm for future workshop[J]. Computer Integrated Manufacturing Systems, 2017, 23 (1): 1- 9
[1] Bao-feng SUN,Xin-kang ZHANG,Gen-dao LI,Jiao-jiao LIU. Joint decision-making of balancing and sequencing for type-II robotic mixed-model assembly line[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1097-1106.
[2] Bo-han LENG,Tang-bin XIA,He SUN,Hao WANG,Li-feng XI. Digital twin mapping modeling and method of monitoring and simulation for reconfigurable manufacturing system[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(5): 843-855.
[3] Li HE,Shan-min PANG. Face reconstruction from voice based on age-supervised learning and face prior information[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(5): 1006-1016.
[4] Xue-qin ZHANG,Tian-ren LI. Breast cancer pathological image classification based on Cycle-GAN and improved DPN network[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 727-735.
[5] Lin-li LI,Fu GU,Hao LI,Xin-jian GU,Guo-fu LUO,Zhi-qiang WU,Yi-jin GANG. Framework and key technologies of digital twin system cyber security under perspective of bionics[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(3): 419-435.
[6] Jing-hui CHU,Li-dong SHI,Pei-guang JING,Wei LV. Context-aware knowledge distillation network for object detection[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(3): 503-509.
[7] Ruo-ran CHENG,Xiao-li ZHAO,Hao-jun ZHOU,Han-chen YE. Review of Chinese font style transfer research based on deep learning[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(3): 510-519, 530.
[8] Xin-ying ZHANG,Lu CHEN,Wen-hui YANG. A parallel-machine scheduling problem with time-changing effect and preventive maintenance[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(2): 408-418.
[9] Tong CHEN,Jian-feng GUO,Xin-zhong HAN,Xue-li XIE,Jian-xiang XI. Visible and infrared image matching method based on generative adversarial model[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 63-74.
[10] Song REN,Qian-wen ZHU,Xin-yue TU,Chao DENG,Xiao-shu WANG. Lining disease identification of highway tunnel based on deep learning[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 92-99.
[11] Sheng-tao XIANG,Da WANG. Model interactive modification method based on improved quantum genetic algorithm[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 100-110.
[12] Xing LIU,Jian-bo YU. Attention convolutional GRU-based autoencoder and its application in industrial process monitoring[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1643-1651.
[13] Xue-yun CHEN,Xiao-qiao HUANG,Li XIE. Classification and detection method of blood cells images based on multi-scale conditional generative adversarial network[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(9): 1772-1781.
[14] Zhe-wu CHENG,Shui-guang TONG,Zhe-ming TONG,Qin-guo ZHANG. Review of digital design and digital twin of industrial boiler[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1518-1528.
[15] Fei JU,Wei-chao ZHUANG,Liang-mo WANG,Jing-xing LIU,Qun WANG. Velocity planning strategy for economic cruise of hybrid electric vehicles[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1538-1547.