[1]
|
谢福贵, 梅斌, 刘辛军, 张加波, 乐毅. 一种大型复杂构件加工新 模式及新装备探讨. 机械工程学报, 2020, 56(19): 70−78 doi: 10.3901/JME.2020.19.070Xie Fu-Gui, Mei Bin, Liu Xin-Jun, Zhang Jia-Bo, Yue Yi. Novel mode and equipment for machining large complex components. Journal of Mechanical Engineering, 2020, 56(19): 70−78 doi: 10.3901/JME.2020.19.070
|
[2]
|
王耀南, 江一鸣, 姜娇, 张辉, 谭浩然, 彭伟星, 等. 机器人感知与控制关键技术及其智能制造应用. 自动化学报, 2023, 49(3): 494−513Wang Yao-Nan, Jiang Yi-Ming, Jiang Jiao, Zhang Hui, Tan Hao-Ran, Peng Wei-Xing, et al. Key technologies of robot perception and control and its intelligent manufacturing applications. Acta Automatica Sinica, 2023, 49(3): 494−513
|
[3]
|
武文亮, 周兴社, 沈博, 赵月. 集群机器人系统特性评价研究综述. 自动化学报, 2022, 48(5): 1153−1172Wu Wen-Liang, Zhou Xing-She, Shen Bo, Zhao Yue. A review of swarm robotic systems property evaluation research. Acta Automatica Sinica, 2022, 48(5): 1153−1172
|
[4]
|
Ren L, Dong J B, Liu S, Zhang L, Wang L H. Embodied intelligence toward future smart manufacturing in the era of AI foundation model. IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2024.3456250
|
[5]
|
赵建国, 邓春利, 郭洪杰, 于思阳. 飞机装配协同测量技术应用. 航空制造技术, 2018, 61(13): 59−62Zhao Jian-Guo, Deng Chun-Li, Guo Hong-Jie, Yu Si-Yang. Application on cooperative measure technology for aircraft assembly. Aeronautical Manufacturing Technology, 2018, 61(13): 59−62
|
[6]
|
王耀南. 集群机器人协同制造技术应用与发展趋势. 新经济导刊, 2023(8): 14−17 doi: 10.3969/j.issn.1009-959X.2023.08.003Wang Yao-Nan. Application and development trend of cluster robot co-manufacturing technology. New Economy Weekly, 2023(8): 14−17 doi: 10.3969/j.issn.1009-959X.2023.08.003
|
[7]
|
王耀南. 工业互联网推动机器人协同智能制造发展. 机器人技术与应用, 2023(5): 8−11 doi: 10.3969/j.issn.1004-6437.2023.05.008Wang Yao-Nan. Industrial internet promotes collaborative smart manufacturing with robots. Robot Technique and Application, 2023(5): 8−11 doi: 10.3969/j.issn.1004-6437.2023.05.008
|
[8]
|
周济. 智能制造——“中国制造2025”的主攻方向. 中国机械工程, 2015, 26(17): 2273−2284 doi: 10.3969/j.issn.1004-132X.2015.17.001Zhou Ji. Intelligent manufacturing——Main direction of “Made in China 2025”. China Mechanical Engineering, 2015, 26(17): 2273−2284 doi: 10.3969/j.issn.1004-132X.2015.17.001
|
[9]
|
Tao F, Qi Q L. New IT driven service-oriented smart manufacturing: Framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(1): 81−91 doi: 10.1109/TSMC.2017.2723764
|
[10]
|
Lv Z H, Xie S X. Artificial intelligence in the digital twins: State of the art, challenges, and future research topics. Digital Twin, 2022, 1: Article No. 12 doi: 10.12688/digitaltwin.17524.2
|
[11]
|
Tao F, Qi Q L. Make more digital twins. Nature, 2019, 573(7775): 490−491 doi: 10.1038/d41586-019-02849-1
|
[12]
|
Hananto A L, Tirta A, Herawan S G, Idris M, Soudagar M E M, Djamari D W, et al. Digital twin and 3D digital twin: Concepts, applications, and challenges in Industry 4.0 for digital twin. Computers, 2024, 13(4): Article No. 100 doi: 10.3390/computers13040100
|
[13]
|
Grieves M W. Product lifecycle management: The new paradigm for enterprises. International Journal of Product Development, 2005, 2(1−2): 71−84
|
[14]
|
Grieves M, Vickers J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer, 2017. 85−113
|
[15]
|
Glaessgen E H, Stargel D S. The digital twin paradigm for future NASA and U.S. Air Force vehicles. In: Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Hawaii, USA: AIAA, 2012. Article No. 1818
|
[16]
|
Gartner. Gartners top 10 technology trends 2017 [Online], available: https://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017, August 4, 2024
|
[17]
|
Gartner. Gartner top 10 strategic technology trends for 2018 [Online], available: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018, August 4, 2024
|
[18]
|
Gartner. Gartner top 10 strategic technology trends for 2019 [Online], available: https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019, August 4, 2024
|
[19]
|
International Data Corporation. From “Egosystems” to ecosystems: IDC reveals the transformative power of digital twins across all industries [Online], available: https://www.idc.com/getdoc.jsp?containerId=prEUR252046924, August 4, 2024
|
[20]
|
李欣, 刘秀, 万欣欣. 数字孪生应用及安全发展综述. 系统仿真学报, 2019, 31(3): 385−392Li Xin, Liu Xiu, Wan Xin-Xin. Overview of digital twins application and safe development. Journal of System Simulation, 2019, 31(3): 385−392
|
[21]
|
陶飞, 程颖, 程江峰, 张萌, 徐文君, 戚庆林. 数字孪生车间信息物理融合理论与技术. 计算机集成制造系统, 2017, 23(8): 1603−1611Tao Fei, Cheng Ying, Cheng Jiang-Feng, Zhang Meng, Xu Wen-Jun, Qi Qing-Lin. Theories and technologies for cyber-physical fusion in digital twin shop-floor. Computer Integrated Manufacturing Systems, 2017, 23(8): 1603−1611
|
[22]
|
陶飞, 刘蔚然, 张萌, 胡天亮, 戚庆林, 张贺, 等. 数字孪生五维模型及十大领域应用. 计算机集成制造系统, 2019, 25(1): 1−18Tao Fei, Liu Wei-Ran, Zhang Meng, Hu Tian-Liang, Qi Qing-Lin, Zhang He, et al. Five-dimension digital twin model and its ten applications. Computer Integrated Manufacturing Systems, 2019, 25(1): 1−18
|
[23]
|
Tao F, Qi Q L, Liu A. Inaugural editorial-digital twin. Digital Twin, 2021, 1: Article No. 1 doi: 10.12688/digitaltwin.17471.1
|
[24]
|
YouTube. GTC March 2024 keynote with NVIDIA CEO Jensen Huang [Online], available: https://www.youtube.com/watch?v=Y2F8yisiS6E, March 3, 2024
|
[25]
|
Ullrich M, Thalappully R, Heieck F, Lüdemann-Ravit B. Virtual commissioning of linked cells using digital models in an industrial metaverse. Automation, 2024, 5(1): 1−12 doi: 10.3390/automation5010001
|
[26]
|
Novikov S V, Sazonov A A. Application of the open operating system ‘MindSphere’ in digital transformation of high-tech enterprises. Economics Journal, 2019, 1(1): 20−26
|
[27]
|
Nath S V, van Schalkwyk P, Isaacs D. Building Industrial Digital Twins: Design, Develop, and Deploy Digital Twin Solutions for Real-world Industries Using Azure Digital Twins. Birmingham: Packt Publishing Ltd, 2021.
|
[28]
|
Wang Z R, Han K, Tiwari P. Digital twin simulation of connected and automated vehicles with the unity game engine. In: Proceedings of the 1st IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI). Beijing, China: IEEE, 2021. 1−4
|
[29]
|
Unity 中国. 海尔对话Unity: 作为数字转型的高阶形态, 数字孪生发展前景不可逆 [Online], available: https://developer.unity.cn/projects/5fc8a9e0edbc2a4ff2bafe89, August 4, 2024Unity China. Haier talks with Unity: As an advanced form of digital transformation, the development of digital twins is irreversible [Online], available: https://developer.unity.cn/projects/5fc8a9e0edbc2a4ff2bafe89, August 4, 2024
|
[30]
|
Mhenni F, Vitolo F, Rega A, Plateaux R, Hehenberger P, Patalano S, et al. Heterogeneous models integration for safety critical mechatronic systems and related digital twin definition: Application to a collaborative workplace for aircraft assembly. Applied Sciences, 2022, 12(6): Article No. 2787 doi: 10.3390/app12062787
|
[31]
|
Warwick G. GE advances analytical maintenance with digital twins. Aviation Week & Space Technology, 2015.
|
[32]
|
Todorovic M H, Datta R, Stevanovic L, She X, Cioff P, Mandrusiak G. Design and testing of a modular SiC based power block. In: Proceedings of the PCIM Europe International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. Nuremberg, Germany: VDE, 2016. 1−4
|
[33]
|
Wu Q C, Mao Y S, Chen J X, Wang C. Application research of digital twin-driven ship intelligent manufacturing system: Pipe machining production line. Journal of Marine Science and Engineering, 2021, 9(3): Article No. 338 doi: 10.3390/jmse9030338
|
[34]
|
Zhang C, Zhou G H, He J, Li Z, Cheng W. A data-and knowledge-driven framework for digital twin manufacturing cell. Procedia CIRP, 2019, 83: 345−350 doi: 10.1016/j.procir.2019.04.084
|
[35]
|
Leng J W, Zhang H, Yan D X, Liu Q, Chen X, Zhang D. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(3): 1155−1166 doi: 10.1007/s12652-018-0881-5
|
[36]
|
EurAsian Times Desk. Dassault (Rafale) group's ‘digital twin technology’ to be used to develop turkey fifth-gen TF-X fighter jet [Online], available: https://www.eurasiantimes.com/dassault-rafale-groupsdigital-twin-technology-to-be-used-to-develop-turkey-fifthgen-tf-x-fighter-jet, August 4, 2024
|
[37]
|
Kinard D A. F-35 production——Advanced manufacturing and the digital thread. In: Proceedings of the Aviation Technology, Integration, and Operations Conference. Atlanta, USA: AIAA, 2018. Article No. 3369
|
[38]
|
Marah H, Challenger M. MADTwin: A framework for multi-agent digital twin development: Smart warehouse case study. Annals of Mathematics and Artificial Intelligence, 2024, 92(4): 975−1005 doi: 10.1007/s10472-023-09872-z
|
[39]
|
Wang L K, Wang Z, Gumma K, Turner A, Ratchev S. Multi-agent cooperative swarm learning for dynamic layout optimisation of reconfigurable robotic assembly cells based on digital twin. Journal of Intelligent Manufacturing, 2025, 36(5): 2959−2982 doi: 10.1007/s10845-023-02229-7
|
[40]
|
Nguyen H, Hussein A, Garratt M A, Abbass H A. Swarm metaverse for multi-level autonomy using digital twins. Sensors, 2023, 23(10): Article No. 4892 doi: 10.3390/s23104892
|
[41]
|
Cejudo J G, Andrés F E, Lujak M, Casamayor C C, Fernandez A, López L H. Towards agrirobot digital twins: Agri-RO5——A multi-agent architecture for dynamic fleet simulation. Electronics, 2023, 13(1): Article No. 80 doi: 10.3390/electronics13010080
|
[42]
|
Lei L, Shen G Q, Zhang L J, Li Z L. Toward intelligent cooperation of UAV swarms: When machine learning meets digital twin. IEEE Network, 2021, 35(1): 386−392 doi: 10.1109/MNET.011.2000388
|
[43]
|
Gao Y P, Chang D F, Chen C H, Sha M. A digital twin-based decision support approach for AGV scheduling. Engineering Applications of Artificial Intelligence, 2024, 130: Article No. 107687 doi: 10.1016/j.engappai.2023.107687
|
[44]
|
Xu W J, Yang H, Ji Z R, Ba M Y. Cognitive digital twin-enabled multi-robot collaborative manufacturing: Framework and approaches. Computers & Industrial Engineering, 2024, 194: Article No. 110418
|
[45]
|
Oo K H, Koomsap P, Ayutthaya D H N. Digital twin-enabled multi-robot system for collaborative assembly of unorganized parts. Journal of Industrial Information Integration, 2025, 44: Article No. 100764 doi: 10.1016/j.jii.2024.100764
|
[46]
|
陶飞, 刘蔚然, 刘检华, 刘晓军, 刘强, 屈挺, 等. 数字孪生及其应用探索. 计算机集成制造系统, 2018, 24(1): 1−18Tao Fei, Liu Wei-Ran, Liu Jian-Hua, Liu Xiao-Jun, Liu Qiang, Qu Ting, et al. Digital twin and its potential application exploration. Computer Integrated Manufacturing Systems, 2018, 24(1): 1−18
|
[47]
|
Tao F, Xiao B, Qi Q L, Cheng J F, Ji P. Digital twin modeling. Journal of Manufacturing Systems, 2022, 64: 372−389 doi: 10.1016/j.jmsy.2022.06.015
|
[48]
|
Echeverria G, Lassabe N, Degroote A, Lemaignan S. Modular open robots simulation engine: MORSE. In: Proceedings of the IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011. 46−51
|
[49]
|
Li X, Huang Z F, Li H T. Geometric and kinematics modeling of tele-operated virtual construction robot. Journal of Software, 2013, 8(10): 2517−2521
|
[50]
|
Li Z, Xiang H Y, Li Z Q, Han B A, Huang J J. The research of reverse engineering based on geomagic studio. Applied Mechanics and Materials, 2013, 365−366: 133−136 doi: 10.4028/www.scientific.net/AMM.365-366.133
|
[51]
|
Zhang C M, Zhang H. Points cloud data processing based on IMAGEWARE & mold design and rapid prototyping manufacturing. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology. Chengdu, China: IEEE, 2010. V-3477−V-3479
|
[52]
|
Channa G S. Reverse Engineering to Create High Quality Polymer Parts Using Additive Manufacturing [Master thesis], The University of Texas at Arlington, USA, 2021.
|
[53]
|
Li Z S, Müller T, Evans A, Taylor R H, Unberath M, Liu M Y, et al. Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, Canada: IEEE, 2023. 8456−8465
|
[54]
|
Cox M G. The numerical evaluation of B-splines. IMA Journal of Applied Mathematics, 1972, 10(2): 134−149 doi: 10.1093/imamat/10.2.134
|
[55]
|
Bazilevs Y, Calo V M, Cottrell J A, Evans J A, Hughes T J R, Lipton S, et al. Isogeometric analysis using T-splines. Computer Methods in Applied Mechanics and Engineering, 2010, 199(5−8): 229−263 doi: 10.1016/j.cma.2009.02.036
|
[56]
|
Li M L, Nan L L. Feature-preserving 3D mesh simplification for urban buildings. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173: 135−150 doi: 10.1016/j.isprsjprs.2021.01.006
|
[57]
|
杜莹莹, 罗映, 彭义兵, 刘璇, 吴竟宁. 基于数字孪生的工业机器人三维可视化监控. 计算机集成制造系统, 2023, 29(6): 2130−2138Du Ying-Ying, Luo Ying, Peng Yi-Bing, Liu Xuan, Wu Jing-Ning. 3D visual monitoring system of industrial robot based on digital twin. Computer Integrated Manufacturing Systems, 2023, 29(6): 2130−2138
|
[58]
|
Szabó B, Babuška I. Finite Element Analysis: Method, Verification and Validation. Hoboken: John Wiley & Sons, 2021.
|
[59]
|
Liao C Y, Wang Y R, Ding X D, Ren Y, Duan X M, He J P. Performance comparison of typical physics engines using robot models with multiple joints. IEEE Robotics and Automation Letters, 2023, 8(11): 7520−7526 doi: 10.1109/LRA.2023.3320019
|
[60]
|
Erez T, Tassa Y, Todorov E. Simulation tools for model-based robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Seattle, USA: IEEE, 2015. 4397−4404
|
[61]
|
Wei Y L, Hu T L, Zhou T T, Ye Y X, Luo W C. Consistency retention method for CNC machine tool digital twin model. Journal of Manufacturing Systems, 2021, 58: 313−322 doi: 10.1016/j.jmsy.2020.06.002
|
[62]
|
Zhi J N, Cao Y L, Li T K, Nabil A, Liu F, Jiang X Q, et al. A digital twin-based analysis method to assess geometric variations for parts in batch production. Digital Twin, 2023, 3: Article No. 7 doi: 10.12688/digitaltwin.17863.2
|
[63]
|
Boulfani F, Gendre X, Ruiz-Gazen A, Salvignol M. Anomaly detection for aircraft electrical generator using machine learning in a functional data framework. In: Proceedings of the Global Congress on Electrical Engineering (GC-ElecEng). Valencia, Spain: IEEE, 2020. 27−32
|
[64]
|
Luo W C, Hu T L, Ye Y X, Zhang C R, Wei Y L. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robotics and Computer-integrated Manufacturing, 2020, 65: Article No. 101974 doi: 10.1016/j.rcim.2020.101974
|
[65]
|
Abele E, Weigold M, Rothenbücher S. Modeling and identification of an industrial robot for machining applications. CIRP Annals, 2007, 56(1): 387−390 doi: 10.1016/j.cirp.2007.05.090
|
[66]
|
Hayat A A, Chittawadigi R G, Udai A D, Saha S K. Identification of Denavit-Hartenberg parameters of an industrial robot. In: Proceedings of the Conference on Advances in Robotics. Pune, India: ACM, 2013. 1−6
|
[67]
|
唐苏妍, 朱一凡, 李群, 雷永林. 多Agent系统任务分配方法综述. 系统工程与电子技术, 2010, 32(10): 2155−2161Tang Su-Yan, Zhu Yi-Fan, Li Qun, Lei Yong-Lin. Survey of task allocation in multi agent systems. Systems Engineering and Electronics, 2010, 32(10): 2155−2161
|
[68]
|
原魁, 李园, 房立新. 多移动机器人系统研究发展近况. 自动化学报, 2007, 33(8): 785−794Yuan Kui, Li Yuan, Fang Li-Xin. Multiple mobile robot systems: A survey of recent work. Acta Automatica Sinica, 2007, 33(8): 785−794
|
[69]
|
Hong Q, Sun Y F, Liu T Y, Fu L, Xie Y F. TAD-Net: An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor. Digital Twin, 2021, 1: Article No. 10 doi: 10.12688/digitaltwin.17408.1
|
[70]
|
Liu T Y, Xia M M, Hong Q, Sun Y F, Zhang P, Fu L, et al. Modeling of cross-scale human activity for digital twin workshop. Digital Twin, 2024, 1: Article No. 11 doi: 10.12688/digitaltwin.17404.2
|
[71]
|
Pronost G, Mayer F, Camargo M, Dupont L. Digital twins along the product lifecycle: A systematic literature review of applications in manufacturing. Digital Twin, 2024, 3: Article No. 3 doi: 10.12688/digitaltwin.17807.2
|
[72]
|
Ritto T G, Rochinha F A. Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mechanical Systems and Signal Processing, 2021, 155: Article No. 107614 doi: 10.1016/j.ymssp.2021.107614
|
[73]
|
Wagg D J, Worden K, Barthorpe R J, Gardner P. Digital twins: State-of-the-art and future directions for modeling and simulation in engineering dynamics applications. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2020, 6(3): Article No. 030901
|
[74]
|
Li X M, Liu H, Wang W X, Zheng Y, Lv H B, Lv Z H. Big data analysis of the internet of things in the digital twins of smart city based on deep learning. Future Generation Computer Systems, 2022, 128: 167−177 doi: 10.1016/j.future.2021.10.006
|
[75]
|
Lv Z H, Li Y X, Feng H L, Lv H B. Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16666−16675 doi: 10.1109/TITS.2021.3113779
|
[76]
|
Lermer M, Reich C. Creation of digital twins by combining fuzzy rules with artificial neural networks. In: Proceedings of the 45th Annual Conference of the IEEE Industrial Electronics Society. Lisbon, Portugal: IEEE, 2019. 5849−5854
|
[77]
|
Scheffel R M, Fröhlich A A, Silvestri M. Automated fault detection for additive manufacturing using vibration sensors. International Journal of Computer Integrated Manufacturing, 2021, 34(5): 500−514 doi: 10.1080/0951192X.2021.1901316
|
[78]
|
Tao F, Zhang M, Liu Y S, Nee A Y C. Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 2018, 67(1): 169−172 doi: 10.1016/j.cirp.2018.04.055
|
[79]
|
Booyse W, Wilke D N, Heyns S. Deep digital twins for detection, diagnostics and prognostics. Mechanical Systems and Signal Processing, 2020, 140: Article No. 106612 doi: 10.1016/j.ymssp.2019.106612
|
[80]
|
Zhang M, Tao F, Huang B Q, Liu A, Wang L H, Anwer N, et al. Digital twin data: Methods and key technologies. Digital Twin, 2022, 1: Article No. 2 doi: 10.12688/digitaltwin.17467.2
|
[81]
|
Codd E F. A relational model of data for large shared data banks. Communications of the ACM, 1970, 13(6): 377−387 doi: 10.1145/362384.362685
|
[82]
|
Kashyap N K, Pandey B K, Mandoria H L, Kumar A. A review of leading databases: Relational & non-relational database. Journal on Information Technology, 2016, 5(2): Article No. 34
|
[83]
|
Last M, Klein Y, Kandel A. Knowledge discovery in time series databases. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2001, 31(1): 160−169 doi: 10.1109/3477.907576
|
[84]
|
郑孟蕾, 田凌. 基于时序数据库的产品数字孪生模型海量动态数据建模方法. 清华大学学报(自然科学版), 2021, 61(11): 1281−1288Zheng Meng-Lei, Tian Ling. Digital product twin modeling of massive dynamic data based on a time-series database. Journal of Tsinghua University (Science & Technology), 2021, 61(11): 1281−1288
|
[85]
|
孙云, 江海凡, 丁国富. 面向生产过程管控的数据建模、集成与存储技术. 中国机械工程, 2022, 33(3): 356−365Sun Yun, Jiang Hai-Fan, Ding Guo-Fu. Data modeling, integration and storage technology for production process management and control. China Mechanical Engineering, 2022, 33(3): 356−365
|
[86]
|
Li Y J, Liu W, Zhang Y, Zhang W L, Gao C Y, Chen Q H, et al. Interactive real-time monitoring and information traceability for complex aircraft assembly field based on digital twin. IEEE Transactions on Industrial Informatics, 2023, 19(9): 9745−9756 doi: 10.1109/TII.2023.3234618
|
[87]
|
Correia J B, Abel M, Becker K. Data management in digital twins: A systematic literature review. Knowledge and Information Systems, 2023, 65(8): 3165−3196 doi: 10.1007/s10115-023-01870-1
|
[88]
|
Hansen O H, Jaiswal V, Stang J. A system for continuous assurance and certifications of data quality for digital twins. In: Proceedings of the 43rd ASME International Conference on Ocean, Offshore and Arctic Engineering. Singapore: ASME, 2024. Article No. V001T01A044
|
[89]
|
Barimah A K, Niculita O, McGlinchey D, Cowell A. Data-quality assessment for digital twins targeting multi-component degradation in industrial internet of things (ⅡoT)-enabled smart infrastructure systems. Applied Sciences, 2023, 13(24): Article No. 13076 doi: 10.3390/app132413076
|
[90]
|
Rodríguez F, Chicaiza W D, Sánchez A, Escaño J M. Updating digital twins: Methodology for data accuracy quality control using machine learning techniques. Computers in Industry, 2023, 151: Article No. 103958 doi: 10.1016/j.compind.2023.103958
|
[91]
|
He X, Ai Q, Pan B, Tang L, Qiu R. Spatial-temporal data analysis of digital twin. Digital Twin, 2022, 2: Article No. 7 doi: 10.12688/digitaltwin.17446.1
|
[92]
|
Zhang K, Cao J Y, Zhang Y. Adaptive digital twin and multiagent deep reinforcement learning for vehicular edge computing and networks. IEEE Transactions on Industrial Informatics, 2022, 18(2): 1405−1413 doi: 10.1109/TII.2021.3088407
|
[93]
|
Xiong K, Wang Z H, Leng S P, He J H. A digital-twin-empowered lightweight model-sharing scheme for multirobot systems. IEEE Internet of Things Journal, 2023, 10(19): 17231−17242 doi: 10.1109/JIOT.2023.3273402
|
[94]
|
Li H, Liu G, Wang H Q, Wen X Y, Xie G Z, Luo G F, et al. Mechanical movement data acquisition method based on the multilayer neural networks and machine vision in a digital twin environment. Digital Twin, 2021, 1: Article No. 6
|
[95]
|
Bruno G, Aliev K. Digital twin application for dynamic task allocation. Towards a Smart, Resilient and Sustainable Industry: Proceedings of the 2nd International Symposium on Industrial Engineering and Automation ISIEA 2023. Cham: Springer, 2023. 145−154
|
[96]
|
Li Y B, Tao Z Y, Wang L, Du B G, Guo J, Pang S B. Digital twin-based job shop anomaly detection and dynamic scheduling. Robotics and Computer-integrated Manufacturing, 2023, 79: Article No. 102443 doi: 10.1016/j.rcim.2022.102443
|
[97]
|
Lv Z H, Cheng C, Lv H B. Multi-robot distributed communication in heterogeneous robotic systems on 5G networking. IEEE Wireless Communications, 2023, 30(2): 98−104 doi: 10.1109/MWC.001.2200315
|
[98]
|
Ding G Z, Guo S Y, Wu X H. Dynamic scheduling optimization of production workshops based on digital twin. Applied Sciences, 2022, 12(20): Article No. 10451 doi: 10.3390/app122010451
|
[99]
|
Pérez L, Rodríguez-Jiménez S, Rodríguez N, Usamentiaga R, García D F. Digital twin and virtual reality based methodology for multi-robot manufacturing cell commissioning. Applied Sciences, 2020, 10(10): Article No. 3633 doi: 10.3390/app10103633
|
[100]
|
Ren L, Dong J B, Huang D, Lv J H. Digital twin robotic system with continuous learning for grasp detection in variable scenes. IEEE Transactions on Industrial Electronics, 2024, 71(7): 7650−7660 doi: 10.1109/TIE.2023.3299049
|
[101]
|
罗瑞平, 盛步云, 黄宇哲, 菅宇超, 宋堃, 陆应康, 等. 基于数字孪生的生产系统仿真软件关键技术与发展趋势. 计算机集成制造系统, 2023, 29(6): 1965−1982Luo Rui-Ping, Sheng Bu-Yun, Huang Yu-Zhe, Jian Yu-Chao, Song Kun, Lu Ying-Kang, et al. Key technologies and development trends of digital twin-based production system simulation software. Computer Integrated Manufacturing Systems, 2023, 29(6): 1965−1982
|
[102]
|
Garg G, Kuts V, Anbarjafari G. Digital twin for FANUC robots: Industrial robot programming and simulation using virtual reality. Sustainability, 2021, 13(18): Article No. 10336 doi: 10.3390/su131810336
|
[103]
|
Lei Z C, Zhou H, Hu W S, Liu G P. Web-based digital twin online laboratories: Methodologies and implementation. Digital Twin, 2023, 2: Article No. 3 doi: 10.12688/digitaltwin.17563.3
|
[104]
|
Stark J. Product lifecycle management (PLM). Product Lifecycle Management (Volume 1) 21st Century Paradigm for Product Realisation (Fifth edition). Cham: Springer, 2022. 1−32
|
[105]
|
Komninos I. Product Life Cycle Management [Master thesis], Aristotle University of Thessaloniki, Greece, 2002. 1−26
|
[106]
|
Sun X W, Zhou C, Duan X D, Sun T. A digital twin network solution for end-to-end network service level agreement (SLA) assurance. Digital Twin, 2021, 1: Article No. 5 doi: 10.12688/digitaltwin.17448.1
|
[107]
|
Hu Y, Miao X W, Si Y, Pan E S, Zio E. Prognostics and health management: A review from the perspectives of design, development and decision. Reliability Engineering & System Safety, 2022, 217: Article No. 108063
|
[108]
|
Zio E. Prognostics and health management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 2022, 218: Article No. 108119
|
[109]
|
Xia J Y, Huang R Y, Li J P, Chen Z Y, Li W H. Digital twin-assisted fault diagnosis of rotating machinery without measured fault data. IEEE Transactions on Instrumentation and Measurement, 2024, 73: Article No. 3531210
|
[110]
|
Xu C Y, Gui X C, Zhao Y. Digital twin-assisted multiview reconstruction enhanced domain adaptation graph networks for aero-engine gas path fault diagnosis. IEEE Sensors Journal, 2024, 24(13): 21694−21705 doi: 10.1109/JSEN.2024.3400249
|
[111]
|
Siatras V, Bakopoulos E, Mavrothalassitis P, Nikolakis N, Alexopoulos K. Production scheduling based on a multi-agent system and digital twin: A bicycle industry case. Information, 2024, 15(6): Article No. 337 doi: 10.3390/info15060337
|
[112]
|
Li Y L, Tsang Y P, Wu C H, Lee C K M. A multi-agent digital twin-enabled decision support system for sustainable and resilient supplier management. Computers & Industrial Engineering, 2024, 187: Article No. 109838
|
[113]
|
Gutiérrez Á. Recent advances in swarm robotics coordination: Communication and memory challenges. Applied Sciences, 2022, 12(21): Article No. 11116 doi: 10.3390/app122111116
|
[114]
|
Nedjah N, Ribeiro L M, de Macedo Mourelle L. Communication optimization for efficient dynamic task allocation in swarm robotics. Applied Soft Computing, 2021, 105: Article No. 107297 doi: 10.1016/j.asoc.2021.107297
|
[115]
|
Li Z Y, Mei X S, Zhang D W, Sun Z, Xu J. A quick response data collection and management system for digital twin production line based on cloud-fog-edge computing collaboration. Digital Twin, 2025, 4: Article No. 7 doi: 10.12688/digitaltwin.17907.2
|
[116]
|
Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, Aleman F L, et al. Gpt-4 technical report. arXiv preprint arXiv: 2303.08774, 2023.
|
[117]
|
Putra R V W, Marchisio A, Zayer F, Dias J, Shafique M. Embodied neuromorphic artificial intelligence for robotics: Perspectives, challenges, and research development stack. In: Proceedings of the 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). Dubai, United Arab Emirates: IEEE, 2024. 612−619
|
[118]
|
Rudin N, Hoeller D, Reist P, Hutter M. Learning to walk in minutes using massively parallel deep reinforcement learning. In: Proceedings of the 5th Conference on Robot Learning. London, UK: PMLR, 2021. 91−100
|
[119]
|
Dai T Y, Wong J, Jiang Y F, Wang C, Gokmen C, Zhang R H, et al. Automated creation of digital cousins for robust policy learning. arXiv preprint arXiv: 2410.07408, 2024.
|
[120]
|
秦龙, 武万森, 刘丹, 胡越, 尹全军, 阳东升, 等. 基于大语言模型的复杂任务自主规划处理框架. 自动化学报, 2024, 50(4): 862−872Qin Long, Wu Wan-Sen, Liu Dan, Hu Yue, Yin Quan-Jun, Yang Dong-Sheng, et al. Autonomous planning and processing framework for complex tasks based on large language models. Acta Automatica Sinica, 2024, 50(4): 862−872
|
[121]
|
刘华平, 郭迪, 孙富春, 张新钰. 基于形态的具身智能研究: 历史回顾与前沿进展. 自动化学报, 2023, 49(6): 1131−1154Liu Hua-Ping, Guo Di, Sun Fu-Chun, Zhang Xin-Yu. Morphology-based embodied intelligence: Historical retrospect and research progress. Acta Automatica Sinica, 2023, 49(6): 1131−1154
|