数字孪生驱动的机械工艺系统研究进展
收稿日期: 2023-05-06
修回日期: 2023-06-23
录用日期: 2023-08-14
网络出版日期: 2023-09-06
基金资助
国家自然科学基金(52305476);山东省自然科学基金(ZR2022QE043)
Research progress of mechanical process system driven by digital twin
Received date: 2023-05-06
Revised date: 2023-06-23
Accepted date: 2023-08-14
Online published: 2023-09-06
Supported by
National Natural Science Foundation of China(52305476);Shandong Natural Science Foundation(ZR2022QE043)
数字孪生技术以数字化方式创建物体虚拟模型,基于数据模拟物体现实中的行为,借助虚实反馈、数据融合分析、决策迭代优化等方式,达到缩短产品研发周期、降低成本的目的。机械工艺系统中存在诸多问题如加工过程监测实时性和交互性差、设备故障难以诊断、加工误差影响大等,数字孪生技术是解决这些问题的有效手段。本文阐述了数字孪生的基本概念和数字孪生驱动的机械工艺系统技术路线;归纳了基于数字孪生技术建立虚实交互系统、实现多模型融合以及数据感知、算法预测和智能决策的关键技术等;从产品设计、产品制造、产品服务3个阶段总结了数字孪生驱动的机械工艺系统在产品全生命周期各阶段的具体应用,实现了产品的方案制定、加工状态监测及预测、加工参数优化、设备预测性维护和加工工艺评价等功能;探讨了新兴使能技术为数字孪生驱动的机械工艺系统带来的机遇和挑战。从关键技术、应用方向和数据利用3方面展望了未来发展趋势,以期为后续研究提供参考。
戚浩 , 李晓月 , 陶强 , 李亮 . 数字孪生驱动的机械工艺系统研究进展[J]. 航空学报, 2024 , 45(21) : 628970 -628970 . DOI: 10.7527/S1000-6893.2023.28970
Digital twin technology creates a virtual model of the object in a digital way, simulates the behavior of the object in reality based on the data, and achieves the purpose of shortening the product development cycle and reducing the cost by means of virtual and real feedback, data fusion analysis, and decision iteration optimization. There are many problems in the mechanical process system, such as poor real-time and interactivity of machining process monitoring, difficulty in diagnosing equipment faults, and large influence of machining errors. Digital twin technology is an effective way to solve these problems. This paper expounds the basic concept of digital twin and the technical route of mechanical process system driven by digital twin. The key technologies of virtual-real interaction system, multi-model fusion, algorithm prediction and intelligent decision-making based on digital twin technology are summarized. The specific applications of digital twin-driven mechanical process systems in each stage of the product lifecycle: product design, product manufacturing and product service, are summarized, and the functions of scheme formulation, process monitoring, process parameter optimization, equipment predictive maintenance and process evaluation of machined parts are realized. The opportunities and challenges brought by emerging enabling technologies for mechanical process system driven by digital twin are discussed. The future development trend is prospected from three aspects: key technology, application direction and data utilization, in order to provide reference for subsequent research.
1 | 周啸宇. 基于递归分析的机械加工过程状态监测[D]. 南昌: 南昌大学, 2022. |
ZHOU X Y. State monitoring of machining process based on recurrence analysis[D]. Nanchang: Nanchang University, 2022 (in Chinese). | |
2 | 吴定海, 任国全, 王怀光, 等. 基于卷积神经网络的机械故障诊断方法综述[J]. 机械强度, 2020, 42(5): 1024-1032. |
WU D H, REN G Q, WANG H G, et al. The review of mechanical fault diagnosis methods based on convolutional neural network[J]. Journal of Mechanical Strength, 2020, 42(5): 1024-1032 (in Chinese). | |
3 | 杨长远, 马赛, 韩勤锴. 基于多核监督流形学习的旋转机械故障诊断[J]. 航空动力学报, 2024, 39(10): 20220184. |
YANG C Y, MA S, HAN Q K. Multi-kernel supervised manifold learning for rotating machinery fault diagnosis[J]. Journal of Aerospace Power, 2024, 39(10): 20220184. | |
4 | 曹明, 黄金泉, 周健, 等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅰ: 气路、机械和FADEC系统故障诊断与预测[J]. 航空学报, 2022, 43(9): 625573. |
CAO M, HUANG J Q, ZHOU J, et al. Current status, challenges and opportunities of civil aero-engine diagnostics & health management I: Diagnosis and prognosis of engine gas path, mechanical and FADEC[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 625573 (in Chinese). | |
5 | 蒋栋年, 李炜. 多模型粒子滤波融合的机械系统寿命预测[J]. 控制工程, 2019, 26(3): 448-453. |
JIANG D N, LI W. Research on remaining useful life prediction of mechanical systems based on fusion of multi-model particle filter[J]. Control Engineering of China, 2019, 26(3): 448-453 (in Chinese). | |
6 | 郑刚, 饶金山, 吴雁, 等. 薄壁叶片侧铣变形误差补偿及刀位规划研究[J]. 制造技术与机床, 2021, 706(4): 126-130. |
ZHENG G, RAO J S, WU Y, et al. Research on error compensation of flank milling deformation and tool path planning of thin-walled blade[J]. Manufacturing Technology & Machine Tool, 2021, 706(4): 126-130 (in Chinese). | |
7 | 段飞宇. 基于复杂直纹面精加工的变形误差补偿及刀位优化研究[D]. 成都: 电子科技大学, 2020. |
DUAN F Y. Research on the compensation of deformation error and the optimization of tool position based on the fine machining of complex ruled surface[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese). | |
8 | 丁军, 古愉川, 黄霞, 等. 基于改进遗传算法优化人工神经网络的304不锈钢流变应力预测准确性研究[J]. 机械工程学报, 2022, 58(10): 78-86. |
DING J, GU Y C, HUANG X, et al. Research on prediction accuracy of flow stress of 304 stainless steel based on artificial neural network optimized by improved genetic algorithm[J]. Journal of Mechanical Engineering, 2022, 58(10): 78-86 (in Chinese). | |
9 | 曾莎莎, 彭卫平, 雷金. 基于混合算法的薄壁件铣削加工工艺参数优化[J]. 中国机械工程, 2017, 28(7): 842-845, 851. |
ZENG S S, PENG W P, LEI J. Optimization of milling process parameters based on hybrid algorithm for thin-walled workpieces[J]. China Mechanical Engineering, 2017, 28(7): 842-845, 851 (in Chinese). | |
10 | 陶飞, 张贺, 戚庆林, 等. 数字孪生十问: 分析与思考[J]. 计算机集成制造系统, 2020, 26(1): 1-17. |
TAO F, ZHANG H, QI Q L, et al. Ten questions towards digital twin: analysis and thinking[J]. Computer Integrated Manufacturing Systems, 2020, 26(1): 1-17 (in Chinese). | |
11 | UEGEL E J, INGRAFFEA A R, EASON T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. International Journal of Aerospace Engineering, 2011, 2011: 1-14. |
12 | GLAESSGREN E H, 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 - Special Session on the Digital Twin. 2012. |
13 | 赵敏. 数字虚体, 智能革命的助推器——解读《三体智能革命》[J]. 中国机械工程, 2018, 29(1): 110-119. |
ZHAO M. Digital virtual body, booster of intelligent revolution-interpretation of “three-body intelligent revolution”[J]. China Mechanical Engineering, 2018, 29(1): 110-119 (in Chinese). | |
14 | 庄存波, 刘检华, 熊辉, 等. 产品数字孪生体的内涵、 体系结构及其发展趋势[J]. 计算机集成制造系统, 2017, 23(4): 753-768. |
ZHUANG C B, LIU J H, XIONG H, et al. Connotation, architecture and trends of product digital twin[J]. Computer Integrated Manufacturing Systems. 2017, 23(4): 753-768 (in Chinese). | |
15 | 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18. |
TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18 (in Chinese). | |
16 | 工业数字孪生白皮书发布[J]. 工业控制计算机, 2021, 34(12): 19. |
Industrial digital twin white paper release[J]. Industrial Control Computer, 2021, 34 (12): 19 (in Chinese). | |
17 | 刘晓冰, 王姝婷, 白朝阳. 基于科学知识图谱的数字孪生发展可视化分析[J]. 计算机集成制造系统, 2022, 28(6): 1673-1684. |
LIU X B, WANG S T, Bai Z Y. Visual analysis of digital twin development based on scientific knowledge graph[J]. Computer Integrated Manufacturing Systems, 2022, 28(6): 1673-1684 (in Chinese). | |
18 | 郭万林. 机械产品全生命周期设计[J]. 中国机械工程, 2002(13): 79-84, 6. |
GUO W L. The total lifecycle design of mechanical products[J]. China Mechanical Engineering, 2002(13): 79-84, 6 (in Chinese). | |
19 | LI J J, ZHOU G H, ZHANG C. A twin data and knowledge-driven intelligent process planning framework of aviation parts[J]. International Journal of Production Research. 2022, 60(17): 5217-34. |
20 | 王乐, 周军, 崔艳林. 数字孪生在航空发动机领域的应用分析[J]. 航空动力, 2020, 16(5): 63-66. |
WANG L, ZHOU J, CUI Y L. Application of digital twin in aero engine[J]. Aerospace Power, 2020, 16(5): 63-66 (in Chinese). | |
21 | 张俊涛. 基于数字孪生的薄壁件铣削加工变形控制研究[D]. 哈尔滨: 哈尔滨理工大学, 2022. |
ZHANG J T. Research on deformation control in thin wall milling based on digital twin[D]. Harbin: Harbin University of Science and Technology, 2022 (in Chinese). | |
22 | 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 F, LIU W R, ZAHNG M, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18 (in Chinese). | |
23 | BRAND M V, CLEOPHAS L, GUNASEKARAN R, et al. Models meet data: challenges to create virtual entities for digital twins[C]∥2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). 2021: 225-228. |
24 | GHOSH A K, ULLAH A S, TETL R, et al. Developing sensor signal-based digital twins for intelligent machine tools[J]. Journal of Industrial Information Integration, 2021, 24: 100242. |
25 | 陶飞, 马昕, 戚庆林, 等. 数字孪生连接交互理论与关键技术[J]. 计算机集成制造系统, 2023, 29(1): 1-10. |
TAO F, MA X, QI Q L, et al. Theory and key technologies of digital twin connection interaction[J]. Computer Integrated Manufacturing System, 2023, 29(1): 1-10 (in Chinese). | |
26 | 钟华. 数控铣床数字孪生连接系统研究[D]. 南昌: 南昌大学, 2022. |
ZHONG H. Research on the digital twin connection system of CNC milling machine[D]. Nanchang: Nanchang University, 2022 (in Chinese). | |
27 | QI Q L, TAO F, NEE A Y C. Chapter 1 - From service to digital twin service[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022:1-31. |
28 | XIANG F, FAN J, KE S Q, et al. Chapter 2 - Digital twin-driven service collaboration[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022: 33-58. |
29 | LI H, WANG H Q. Chapter 3 - Digital twin-driven production line custom design service[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022: 59-88. |
30 | HONG L K Y, ZHENG P, LIEW D W. Chapter 4 - Digital twin-enhanced product family design and optimization service[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022: 89-118. |
31 | LIU M, FANG S, DONG H, et al. Review of digital twin about concepts, technologies, and industrial applications[J]. Journal of Manufacturing Systems, 2021, 58: 346-361. |
32 | 刘永泉, 黎旭, 任文成, 等. 数字孪生助力航空发动机跨越发展[J]. 航空动力, 2021, 19(2): 24-29. |
LIU Y Q, LI X, REN W C, et al. Digital twin boosting leap-forward development of aero engine[J]. Aerospace Power, 2021, 19(2): 24-29 (in Chinese). | |
33 | 吴雄, 彭云龙, 张波, 等. 基于数字孪生的舰载机发动机健康管理技术[J]. 航空动力, 2022, 27(4): 33-36. |
WU X, PENG Y L, ZHANG B, et al. Aero engine health management technology based on digital twin[J]. Aerospace Power, 2022, 27(4): 33-36 (in Chinese). | |
34 | 李聪波, 孙鑫, 侯晓博, 等. 数字孪生驱动的数控铣削刀具磨损在线监测方法[J]. 中国机械工程, 2022, 33(1): 78-87. |
LI C B, SUN X, HOU X B, et al. Online monitoring method for NC milling tool wear by digital twin-driven[J]. China Mechanical Engineering, 33(1), 2022: 78-87 (in Chinese). | |
35 | 路遥. 大型轴承座铣削加工过程中数字孪生监控系统研究[D]. 哈尔滨: 哈尔滨理工大学, 2022. |
LU Y. Research on digital twin monitoring system during milling of large bearing block[D]. Harbin: Harbin University of Science and Technology, 2022 (in Chinese). | |
36 | 李小龙. 基于数字孪生的机床加工过程虚拟监控系统研究与实现[D]. 成都: 电子科技大学, 2020. |
LI X L. Research and implementation of a virtual monitoring system for machine tools process based on digital twin[D]. Chengdu: University of Electronic Science and Technology of China, 2020 (in Chinese). | |
37 | 姜静. 基于数字孪生的数控机床加工路径优化方法研究[D]. 武汉: 武汉理工大学, 2019. |
JIANG J. Research on machining path method of CNC machine tools based on digital twin [D]. Wuhan: Wuhan University of Technology, 2019 (in Chinese). | |
38 | 张雷, 刘检华, 庄存波, 等. 基于数字孪生的多轴数控机床轮廓误差抑制方法[J]. 计算机集成制造系统, 2021, 27(12): 3391-3402. |
ZHANG L, LIU J H, ZHUANG C B, et al. Contour error reduction method for multi-axis CNC machine tools based on digital twin[J]. Computer Integrated Manufacturing Systems, 2021, 27(12): 3391-3402 (in Chinese). | |
39 | 赵丽丽. 基于数字孪生的数控加工切削参数优化方法研究[D]. 武汉: 武汉理工大学, 2021. |
ZHAO L L. Research on cutting parameter optimization method of CNC machining based on digital twin[D]. Wuhan: Wuhan University of Technology, 2021 (in Chinese). | |
40 | 李典伦. 基于数字孪生技术的数控机床虚实交互监控系统设计与研究[D]. 兰州: 兰州理工大学, 2021. |
LI D L. Design and research of virtual and real interactive monitoring system for CNC machine tools based on digital twin technology[D]. Lanzhou: Lanzhou University of Technology, 2021 (in Chinese). | |
41 | 江雪梅, 袁子航, 娄平, 等. 一种基于数字孪生的重型数控机床碰撞检测方法[J]. 中国机械工程, 2022, 33(22): 2647-2654, 2663. |
JIANG X M, YUAN Z H, LOU P, et al. A collision detection method of heavy-duty CNC machine tools based on digital twin[J]. China Mechanical Engineering, 2022, 33(22): 2647-2654, 2663 (in Chinese). | |
42 | 刘劲松. 高档数控机床数字孪生关键技术研究与应用[D]. 沈阳: 中国科学院大学(中国科学院沈阳计算技术研究所), 2022. |
LIU J S. Research and application of key technology of digital twin for high-end CNC machine tools[D]. Shenyang: University of Chinese Academy of Sciences (Shenyang Institute of Computing Technology, Chinese Academy of Sciences), 2022 (in Chinese). | |
43 | MA X, TAO F, ZHANG M, et al. Digital twin enhanced human-machine interaction in product lifecycle[J]. Procedia CIRP, 2019, 83: 789-793. |
44 | DUAN J G, MA T Y, ZAHNG Q L, et al. Design and application of digital twin system for the blade-rotor test rig[J]. Journal of Intelligent Manufacturing. 2023; 34(2): 753-769. |
45 | 孙明波, 安彬, 汪洪波, 等. 超燃冲压发动机仿真:从数值飞行到数智飞行[J]. 力学学报, 2022, 54(3): 588-600. |
SUN M B, AN B, WANG H B, et al. Numerical simulation of the scramjet engine: from numerical flight to intelligent numerical flight[J]. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(3): 588-600 (in Chinese). | |
46 | 田效康. 基于数字孪生的机床加工过程虚拟仿真监控系统研究与实现[D]. 成都: 电子科技大学, 2021. |
TIAN X K. Research and implementation of virtual simulation monitoring system for machine tool machining process based on digital twin[D]. Chengdu: University of Electronic Science and Technology of China, 2021 (in Chinese). | |
47 | 田凌, 郑孟蕾, 代菁洲, 等. 基于图数据库的机械产品数字孪生模型分层建模方法: CN111881578A[P]. 2020-11-03. |
TIAN L, ZHENG M L, DAI J Z, et al. A layered modeling method for digital twin models of mechanical products based on graph database: CN111881578A[P]. 2020-11-03 (in Chinese). | |
48 | 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[J]. 清华大学学报(自然科学版), 2021, 61(11): 1281-1288. |
ZHENG M L, TIAN L. Digital product twin modeling of massive dynamic data based on a time-series database[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(11): 1281-1288 (in Chinese). | |
49 | 郑孟蕾, 田凌. 基于区块链的机械产品数字孪生本体模型协同演进方法[J]. 计算机集成制造系统, 2023, 29(6): 1781-1794. |
ZHENG M L, TIAN L. Blockchain-based collaborative evolution method for digital twin ontology model of mechanical products[J]. Computer Integrated Manufacturing Systems, 2023, 29(6): 1781-1794 (in Chinese). | |
50 | YAGCI T, HOSCHLER K, PANNIER S, et al. A structural health monitoring (SHM) approach using a multidisciplinary digital twin for high-pressure turbine discs in civil aviation[C]??∥The 7th International Conference on Integrity-Reliability-Failure (IRF). 2020: 491-502. |
51 | HU W Y, WANG T Y, CHU F L, A Wasserstein generative digital twin model in health monitoring of rotating machines[J]. Computers in Industry, 2023, 145: 103807. |
52 | LIANG Z S, WANG S T, PENG Y L, et al. The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables[J]. Journal of Manufacturing Systems, 2022, 63: 78-94. |
53 | YU J S, SONG Y, TANG D Y, et al. A digital twin approach based on nonparametric Bayesian network for complex system health monitoring[J]. Journal of Manufacturing Systems, 2021, 58: 293-304. |
54 | 刘世民, 孙学民, 陆玉前, 等. 知识驱动的加工产品数字孪生拟态建模方法[J]. 机械工程学报, 2021, 57(23): 182-194. |
LIU S M, SUN X M, LU Y Q, et al. A knowledge-driven digital twin modeling method for machining products based on biomimicry[J]. Journal of Mechanical Engineering, 2021, 57(23): 182-194 (in Chinese). | |
55 | LIU S M, BAO J, LU Y Q, et al. Digital twin modeling method based on biomimicry for machining aerospace components[J]. Journal of Manufacturing Systems, 2020,58:180-195. |
56 | LIU S M, SHEN H, LI J, et al. An adaptive evolutionary framework for the decision-making models of digital twin machining system[C]?∥2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). 2021: 771-776. |
57 | 李琳利, 李浩, 顾复, 等. 基于数字孪生的复杂机械产品多学科协同设计建模技术[J]. 计算机集成制造系统, 2019, 25(6): 1307-1319. |
LI L L, LI H, GU F, et al. Multidisciplinary collaborative design modeling technologies for complex mechanical products based on digital twin[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1307-1319 (in Chinese). | |
58 | 沈慧, 刘世民, 许敏俊, 等. 面向加工领域的数字孪生模型自适应迁移方法[J]. 上海交通大学学报, 2022, 56(1): 70-80. |
SHEN H, LIU S M, XU M J, et al. Adaptive transferring method of digital twin model for machining domain[J]. Journal of Shanghai Jiao Tong University, 2022, 56(1): 70-80 (in Chinese). | |
59 | 孙惠斌, 潘军林, 张纪铎, 等. 面向切削过程的刀具数字孪生模型[J]. 计算机集成制造系统, 2019, 25(6): 1474-1480. |
SUN H B, PAN J L, ZHANG J D, et al. Digital twin model for cutting tools in machining process[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1474-1480 (in Chinese). | |
60 | 张辰源, 陶飞. 数字孪生模型评价指标体系[J]. 计算机集成制造系统, 2021, 27(8): 2171-2186. |
ZHANG C Y, TAO F. Evaluation index system for digital twin model[J]. Computer Integrated Manufacturing Systems, 2021, 27(8): 2171-2186 (in Chinese). | |
61 | BRAUN D, MVLLER T, SAHLAB N, et al. A graph-based knowledge representation and pattern mining supporting the digital twin creation of existing manufacturing systems[C]∥2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA). 2022: 1-4. |
62 | 惠恩明. 数控机床数字孪生自主感知技术研究[D]. 武汉: 华中科技大学, 2020. |
HUI E M. Research on digital twin self-awaring technology of NC machine tools[D]. Wuhan: Huazhong University of Science and Technology, 2020 (in Chinese). | |
63 | 付洋, 曹宏瑞, 郜伟强, 等. 数字孪生驱动的航空发动机涡轮盘剩余寿命预测[J]. 机械工程学报, 2021, 57(22): 106-113. |
FU Y, CAO H R, GAO W Q, et al. Digital twin driven remaining useful life prediction for aero-engine turbine discs[J]. Journal of Mechanical Engineering, 2021, 57(22): 106-113 (in Chinese). | |
64 | 巩超光, 胡天亮, 叶瑛歆. 基于数字孪生的铣削参数动态多目标优化策略[J]. 计算机集成制造系统, 2021, 27(2): 478-486. |
GONG C G, HU T L, YE Y X. Dynamic multi-objective optimization strategy of milling parameters based on digital twin[J]. Computer Integrated Manufacturing Systems, 2021, 27(2): 478-486 (in Chinese). | |
65 | 胡富琴, 杨芸, 刘世民, 等. 航天薄壁件旋压成型数字孪生高保真建模方法[J]. 计算机集成制造系统, 2022, 28(5): 1282-1292. |
HU F Q, YANG Y, LIU S M, et al. Digital twin high-fidelity modeling method for spinning forming of aerospace thin-walled parts[J]. Computer Integrated Manufacturing Systems, 2022, 28(5): 1282-1292 (in Chinese). | |
66 | 马兴瑞. 基于数字孪生模型的轴承故障特征生成与诊断方法研究[D]. 济南: 山东大学, 2022. |
MA X R. Research on fault feature generation and fault diagnosis based on digital twin model of bearing[D]. Ji’nan: Shandong University, 2022 (in Chinese). | |
67 | 宋飞虎, 王梦柯, 尹静, 等. 基于数字孪生控制的精密机床热误差模型[J]. 机电工程, 2023, 40(3): 391-398. |
SONG F H, WANG M K, YIN J, et al. Thermal error model for precision machine tools based on digital twin control[J]. Journal Mechanical and Electrical Engineering: 2023, 40(3): 391-398 (in Chinese). | |
68 | 拓云天, 崔洁, 王津沓, 等. 基于数字孪生的滚动轴承健康状态预测[J]. 制造技术与机床, 2022, 725(11): 156-162. |
TUO Y T. , CUI J, WANG J T, et al Predicting the health prediction of rolling bearings based on digital twin[J]. Manufacturing Technology and Machine Tool, 2022, 725(11): 156-162 (in Chinese). | |
69 | LIU S M, LU Y Q, ZHENG P, et al. Adaptive reconstruction of digital twins for machining systems: A transfer learning approach[J]. Robotics and Computer-Integrated Manufacturing, 2022, 78: 102390. |
70 | 张海军, 闫琼, 张国辉, 等. 基于数字孪生的制造资源动态优选决策[J]. 计算机集成制造系统, 2021, 27(2): 521-535. |
ZHANG H J, YAN Q, ZHANG G H, et al. Dynamic decision-making of manufacturing resource based on digital twin[J]. Computer Integrated Manufacturing Systems, 2021, 27(2): 521-535 (in Chinese). | |
71 | 刘魁, 刘婷, 魏杰, 等. 数字孪生在航空发动机可靠性领域的应用探索[J]. 航空动力, 2019, 9(4): 61-64. |
LIU K, LIU T, WEI J, et al. Digital twin and its potential application in the field of aero engine reliability[J]. Aerospace Power, 2019, 9(4): 61-64 (in Chinese). | |
72 | 刘婷, 张建超, 刘魁. 基于数字孪生的航空发动机全生命周期管理[J]. 航空动力, 2018, 1(1): 52-56. |
LIU T, ZHANG J C, LIU K. Aero engine life cycle management based on digital twin[J]. Aerospace Power 2018, 1(1): 52-56 (in Chinese). | |
73 | 曹增义, 单继东, 王昭阳, 等. 面向航空发动机制造的数字孪生应用架构探索与实践[J]. 航空制造技术, 2022, 65(19): 40-49. |
CAO Z Y, SHAN J D, WANG Z Y, et al. Exploration and practice of digital twin architecture for aero-engine manufacturing[J]. Aeronautical Manufacturing Technology, 2022, 65(19): 40-49 (in Chinese). | |
74 | 任祝寅, 周华, 张健, 等. 数字孪生在航空发动机燃烧室设计阶段的应用与展望[J]. 航空制造技术, 2022, 65(17): 34-39. |
REN Z Y, ZHOU H, ZHANG J, et al. Application and prospect of digital twin in design phase of aero-engine combustion chambers[J]. Aeronautical Manufacturing Technology 2022, 65(17): 34-39 (in Chinese). | |
75 | ZHANG M, SUI F Y, LIU A, et al. Chapter 1 - Digital twin driven smart product design framework[M]. TAOF, LIUA, HUT L, NEEAY C, editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 3-32. |
76 | WANG Y C, LIU A, TAO F, et al. Chapter 2 - Digital twin driven conceptual design[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 33-66. |
77 | WANG Y C, LIU L, LIU A. Chapter 3 - Conceptual design driven digital twin configuration[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 67-107. |
78 | WANG L, TAO F, LIU A, et al. Chapter 5 - Digital twin driven design evaluation[M]. In: TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 139-164. |
79 | XIANG F, HUANG Y Y, ZHANG Z, et al. Chapter 6 - Digital twin driven energy-aware green design[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 165-184. |
80 | HU T L, KONG T X, YE Y X, et al. Chapter 9 - Digital twin based computerized numerical control machine tool virtual prototype design[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 237-263. |
81 | WEI Y L, HU T L, ZHANG W L, et al. Chapter 10 - Digital twin driven lean design for computerized numerical control machine tools[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 265-287. |
82 | 白仲航, 孙意为, 许彤, 等.基于设计任务的概念设计中产品数字孪生模型的构建[J]. 工程设计学报, 2020, 27(6): 681-689. |
BAI Z H, SUN Y W, Xu T, et al. Construction of product digital twin model based on design task in conceptual design[J]. Chinese Journal of Engineering Design, 2020, 27(6): 681-689 (in Chinese). | |
83 | 王昊琪, 李浩, 文笑雨, 等. 基于数字孪生的产品设计过程和工作量预测方法[J]. 计算机集成制造系统, 2022, 28(1): 17-30. |
WANG H Q, Li H, WEN X Y, et al. Digital twin-based product design process and design effort prediction method[J]. Computer Integrated Manufacturing Systems, 2022, 28(1): 17-30 (in Chinese). | |
84 | 李浩, 陶飞, 王昊琪, 等. 基于数字孪生的复杂产品设计制造一体化开发框架与关键技术[J]. 计算机集成制造系统, 2019, 25(6): 1320-1336. |
LI H, TAO F, WANG H Q, et al. Integration framework and key technologies of complex product design-manufacturing based on digital twin[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1320-1336 (in Chinese). | |
85 | PAN L D, GUO X K, LUAN Y, et al. Design and realization of cutting simulation function of digital twin system of CNC machine tool[J]. Procedia Computer Science, 2021, 183: 261-266. |
86 | ANBALAGAN A, SHIVAKRISHNA B, SRIKANTH K S, A digital twin study for immediate design/redesign of impellers and blades : Part 1: CAD modelling and tool path simulation[J] Materials Today: Proceedings, 2021, 46(17): 8209-8217. |
87 | SHEN W D, HU T L, YIN Y S, et al. Chapter 11 - Digital twin based virtual commissioning for computerized numerical control machine tools[M]. TAO F, LIU A, HU T L, et al. editors. Digital twin driven smart design. Elsevier: Academic Press, 2020: 289-307. |
88 | WANG J, NIU X, GAO R X, et al. Digital twin-driven virtual commissioning of machine tool[J]. Robotics and Computer-Integrated Manufacturing, 2023, 81: 102499. |
89 | HOWARD D, The digital twin: virtual validation in electronics development and design[C]?∥2019 Pan Pacific Microelectronics Symposium (Pan Pacific). 2019, 1-9. |
90 | 王春晓. 基于数字孪生的数控机床多领域建模与虚拟调试关键技术研究[D]. 济南: 山东大学, 2018. |
WANG C X. Multi-domain modeling and virtual debugging of CNC machine tool based on digital twin[D]. Ji’nan: Shandong University, 2018 (in Chinese). | |
91 | 邓子毅. 面向发动机数字孪生工效虚拟人应用研究[D]. 天津: 中国民航大学, 2021. |
DENG Z Y. Research on the application of engine digital twin ergonomic virtual human[D]. Tianjin: Civil Aviation University of China, 2021 (in Chinese). | |
92 | 刘然, 刘虎沉. 基于数字孪生的产品制造过程质量管理研究[J]. 现代制造工程, 2022, 502(7): 50-56. |
LIU R, LIU H C. Research on quality management of product manufacturing process based on digital twin[J]. Modern Manufacturing Engineering 2022, 502(7): 50-56 (in Chinese). | |
93 | 马嵩华, 胡凯鑫, 胡天亮. 支持快速设计与性能跟踪的夹具数字孪生模型[J]. 计算机集成制造系统, 2022, 28(9): 2718-2725. |
MA S H, HU K X, HU T L. Digital twins of fixtures supporting rapid design and performance tracking[J]. Computer Integrated Manufacturing Systems, 2022, 28(9): 2718-2725 (in Chinese). | |
94 | 孟博洋, 李茂月, 刘献礼, 等. 机床智能控制系统体系架构及关键技术研究进展[J]. 机械工程学报, 2021, 57(9): 147-166. |
MENG B Y, LI M Y, LIU X L, et al. Research progress on the architecture and key technologies of machine tool intelligent control system[J]. Journal of Mechanical Engineering, 2021, 57(9): 147-166 (in Chinese). | |
95 | LIU J S, DONG Y, BI X X, et al. The Research of ontology-based digital twin machine tool modeling[C]∥2020 IEEE 6th International Conference on Computer and Communications (ICCC). 2020: 2130-2134. |
96 | 黄华, 李嘉然, 李典伦. 基于数字孪生的数控机床虚实交互监控系统设计[J]. 兰州理工大学学报, 2023, 49(1): 36-43. |
HUANG H, LI J R, LI D L. Design of virtual real interactive monitoring system for CNC machine tools based on digital twins[J]. Journal of Lanzhou University of Technology, 2023, 49(1): 36-43 (in Chinese). | |
97 | 王宇顺. 基于数字孪生的设备运行状态远程监测技术研究[D]. 武汉: 华中科技大学, 2020. |
WANG Y S. Remote monitoring technology of equipment operating status based on digital twin[D]. Wuhan: Huazhong University of Science and Technology, 2020 (in Chinese). | |
98 | 肖通, 江海凡, 丁国富, 等. 五轴磨床数字孪生建模与监控研究[J]. 系统仿真学报, 2021, 33(12): 2880-2890. |
XIAO T, JIANG H F., DING G F, et al. Research on digital twin-based modeling and monitoring of five-axis grinder[J]. Journal of System Simulation, 2021, 33(12): 2880-2890 (in Chinese). | |
99 | 谭飏, 张宇, 刘丽冰, 等. 面向动力学特性监测的主轴系统数字孪生体[J]. 中国机械工程, 2020, 31(18): 2231-2238, 2246. |
TAN Y, ZHANG Y, LIU L B, et al. Spindle system digital twin for dynamics characteristic monitoring [J]. China Mechanical Engineering, 2020, 31(18): 2231-2238, 2246 (in Chinese). | |
100 | LV J H, LI X Y, SUN Y C, et al. A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools[J]. Robotics and Computer-Integrated Manufacturing, 2023, 80: 102489. |
101 | YANG X, RAN Y, ZHANG G B, et al. A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool[J] Robotics and Computer-Integrated Manufacturing, 2022, 73: 102230. |
102 | LIU K, SONG L, HAN W, et al. Time-varying error prediction and compensation for movement axis of CNC machine tool based on digital twin[J]. IEEE Transactions on Industrial Informatics, 2022, 18(1): 109-118. |
103 | 陈艺文. 基于数字孪生表面模型的滚磨加工精细化仿真方法研究[D]. 太原: 太原理工大学, 2022. |
CHEN Y W. Research on precision simulation method of barrel finishing based on digital twin surface model[D]. Taiyuan: Taiyuan University of Technology, 2022 (in Chinese). | |
104 | GHOSH A, ULLAH A, KUBO A. Hidden Markov model-based digital twin construction for futuristic manufacturing systems[J]. AI EDAM-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 2019, 33(3): 317-331. |
105 | ZHU Z X, XI X L, XU X, et al. Digital twin-driven machining process for thin-walled part manufacturing[J]. Journal of Manufacturing Systems, 2021, 59: 453-466. |
106 | 邹琦, 侯志霞, 王明阳. 机加零件的数字孪生模型构建方法[J]. 航空制造技术, 2020, 63(3): 67-75. |
ZOU Q, HOU Z X, WANG M Y. Modeling method of digital twin models for machined parts[J] Aerospace Manufacturing Technology, 2020, 63(3): 67-75 (in Chinese). | |
107 | 岳彩旭, 张俊涛, 刘献礼, 等. 薄壁件铣削过程加工变形研究进展[J]. 航空学报, 2022,43(4): 525164. |
YUE C X, ZHANG J T, Liu X L, et al. Research progress on machining deformation of thin-walled parts in milling process[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(4): 525164 (in Chinese). | |
108 | 成彬, 樊琛. 数字节点链驱动的孪生工序动态模型构建[J]. 兵器装备工程学报, 2023, 44(2): 69-76. |
CHENG B, FAN C. Construction of a twin processes dynamic model driven by digital node chains[J]. Journal of Ordnance Equipment Engineering, 2023, 44(2): 69-76 (in Chinese). | |
109 | 朱宇, 李海宁, 曹志涛, 等. 数字孪生在航空发动机制造工艺中的应用探索[J]. 航空动力, 2019(4): 56-60. |
ZHU Y, LI H N, CAO Z T, et al. The Application of digital twin in manufacturing process of aero engine[J]. Aerospace Power, 2019(4): 56-60 (in Chinese). | |
110 | 杨康. 基于数字孪生的叶片动应变反演[D]. 北京: 北京化工大学, 2021. |
YANG K, Dynamic strain inversion of blade based on digital twin[D]. Beijing: Beijing University of Chemical Technology, 2021 (in Chinese). | |
111 | QIAO Q Z, WANG J J, YE L K, et al. Digital twin for machining tool condition prediction[J]. Procedia CIRP, 2019, 81: 1388-1393. |
112 | ZHANG H, QI Q L, JI W, et al. An update method for digital twin multi-dimension models[J]. Robotics and Computer-Integrated Manufacturing, 2023, 80: 102481. |
113 | 王军, 周婷婷, 衣明东. 金属切削刀具磨损监测技术研究进展[J]. 工具技术, 2021, 55(1): 3-10. |
WANG J, ZHOU T T, YI M D. Review on research status of wear monitoring technology for metal cutting tool[J]. Tool Technology, 2021, 55(1): 3-10 (in Chinese). | |
114 | 王军. 基于数字孪生的刀具磨损监测和预测方法研究[D]. 济南: 齐鲁工业大学, 2021. |
WANG J. Research on the monitoring and prediction method of cutting tool wear based on digital twin[D]. Ji’nan: Qilu University of Technology, 2021 (in Chinese). | |
115 | 李恒帅. 基于数字孪生技术的刀具磨损图像检测[D]. 哈尔滨: 哈尔滨理工大学, 2021. |
LI H S. Tool wear image detection based on digital twin technology[D]. Harbin: Harbin University of Science and Technology, 2021 (in Chinese). | |
116 | 宋清华, 彭业振, 王润琼, 等. 数字孪生驱动的薄壁件铣削刀具磨损状态识别方法[J]. 航空制造技术, 2023, 66(3): 46-52, 60. |
SONG Q H, PENG Y Z, WANG R Q, et al. Tool wear state identification method of thin-walled parts milling process driven by digital twin[J]. Aerospace Manufacturing Technology, 2023, 66 (3): 46-52, 60 (in Chinese). | |
117 | 张春霖, 周婷婷, 胡天亮, 等. 刀具切削变工况数字孪生模型构建方法研究[J]. 计算机集成制造系统, 2023, 29(6): 1852-1866. |
ZHANG C L, ZHOU T T, HU T L, et al. Construction method of digital twin model for cutting tools under variable working conditions[J]. Computer Integrated Manufacturing Systems, 2023, 29(6): 1852-1866. | |
118 | 谢楠, 寇锐, 刘雪梅. 基于云计算的刀具状态监测数字孪生系统研究[J]. 机械制造, 2021, 59(3): 78-82, 92. |
XIE N, KOU R, LIU X M. Research on digital twin system monitoring of tool condition based on cloud computing[J]. Machinery, 2021, 59(3): 78-82, 92 (in Chinese). | |
119 | 李亚. 数控机床刀具健康状态评估及预测技术研究[D]. 上海: 上海交通大学, 2020. |
LI Y. Research on health status evaluation and prediction of CNC tool[D] Shanghai: Shanghai Jiao Tong University, 2020 (in Chinese). | |
120 | 孙鑫. 基于数字孪生的数控铣削刀具磨损在线预测及节能工艺优化方法[D]. 重庆: 重庆大学, 2021. |
SUN X. On-line prediction of tool wear in CNC milling optimization of energy-saving technology based on digital twin[D] Chongqing: Chongqing University, 2021 (in Chinese). | |
121 | XIE Y, LIAN K L, LIU Q, et al. Digital twin for cutting tool: Modeling, application and service strategy[J]. Journal of Manufacturing Systems, 2021, 58: 305-312. |
122 | 赵飞, 侯星宇, 王骏, 等. 基于数字孪生的新型四工位刀架设计[J]. 实验技术与管理, 2020, 37(10): 144-150. |
ZHAO F, HOU X Y, WANG J, Design of new four-position tool holder based on digital twin[J]. Experimental Technology and Management, 2020, 37(10): 144-150 (in Chinese). | |
123 | WANG G, CAO Y S, ZHANG Y F. Digital twin-driven clamping force control for thin-walled parts[J]. Advanced Engineering Informatics, 2022, 51: 101468. |
124 | WECKX S, ROBYNS S, BAAKE J, et al. A cloud-based digital twin for monitoring of an adaptive clamping mechanism used for high performance composite machining[J]. Procedia Computer Science, 2022, 200: 227-236. |
125 | 刘明浩, 岳彩旭, 夏伟, 等. 基于数字孪生的铣刀状态实时监控[J]. 计算机集成制造系统: 2023, 29(6): 2118-2129. |
LIU M H, YUE C X, XIA W, et al. Real-time monitoring of milling tool state based on digital twin[J]. Computer Integrated Manufacturing Systems, 2023, 29(6): 2118-2129 (in Chinese). | |
126 | 崔馨予. 数控铣削数字孪生模型研究及面向能效的加工参数优化[D]. 哈尔滨: 哈尔滨工业大学, 2021. |
CUI X Y. Research on digital twin model for NC milling and optimization of machining parameters for energy efficiency[D]. Harbin: Harbin Institute of Technology, 2021 (in Chinese). | |
127 | 方喜峰, 张杰, 程德俊, 等. 数字孪生驱动的船用柴油机关键件加工质量管控方法[J]. 机械设计与制造, 2023, (3): 46-52. |
FANG X F, ZHANG J, CHENG D J, et al. Manufacturing quality control method of key parts of marine diesel driven by digital twin[J]. Machinery Design and Manufacture, 2023, (3): 46-52 (in Chinese). | |
128 | 张蕾. 基于数字孪生的设备预测性维护模式研究[J]. 电子工业专用设备, 2021, 50(3): 12-15. |
ZHANG L. Research on predictive maintenance mode of equipment based on digital twin[J]. Equipment for Electronic Products, 2021, 50(3): 12-15 (in Chinese). | |
129 | CLASSENS K, WHEEMELS M, OOMEN T. Digital twins in mechatronics: from model-based control to predictive maintenance[C]∥2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). 2021: 336-339. |
130 | YU J S, TANG D Y. Chapter 8 - Digital twin-driven prognostics and health management[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022: 205-250. |
131 | LUO W C, HU T L, YE Y X, et al. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65: 101974. |
132 | 骆伟超. 基于Digital Twin的数控机床预测性维护关键技术研究[D]. 济南: 山东大学, 2020. |
LUO W C. Research on key technologies of machine tool predictive maintenance based on digital twin[D]. Ji’nan: Shandong University, 2020 (in Chinese). | |
133 | 王成城, 王金江, 黄祖广, 等. 智能制造预测性维护标准体系研究与应用[J]. 制造技术与机床, 2023, 728(2): 73-82. |
WANG C C, WANG J J, HUANG Z G, et al. Research and application of intelligent manufacturing predictive maintenance standard system[J]. Manufacturing Technology and Machine Tool, 2023, 728(2): 73-82 (in Chinese). | |
134 | 李晓, 陈雨晨, 阮渊鹏, 等. 基于DT的售后设备预测性维护协同模式研究[J]. 工业工程与管理, 2023, 28(1): 67-80. |
LI X, CHEN Y C, RUAN Y P, et al. Research on collaborative model of predictive maintenance for after-sales equipment based on DT[J]. Industrial Engineering and Management, 2023, 28(1): 67-80 (in Chinese). | |
135 | 陆剑峰, 徐煜昊, 夏路遥, 等. 数字孪生支持下的设备故障预测与健康管理方法综述[J]. 自动化仪表, 2022, 43(6): 1-7, 12. |
LU J F, XU Y H, XIA L Y, et al. Review of digital twin-enabled device prognostics and health management approaches[J]. Process Automation Instrumentation, 2022, 43(6): 1-7, 12 (in Chinese). | |
136 | 柳宇翀. 基于数字孪生技术的液压系统故障诊断与预测方法[D]. 大连: 大连理工大学, 2022. |
LIU Y C. A Hydraulic system fault diagnosis and prediction method based on digital twin technology[D]. Dalian: Dalian University of Technology, 2022 (in Chinese). | |
137 | LIU J F, ZHOU H G, LIU X J . et al ., Dynamic evaluation method of machining process planning based on digital twin[J]. IEEE Access, 2019, 7: 19312-19323. |
138 | 赵鹏. 基于数字孪生的加工工艺评价方法研究[D]. 镇江: 江苏科技大学, 2021. |
ZHAO P. Research on evaluation method of machining process based on digital twin[D]. Zhenjiang: Jiangsu University of Science and Technology, 2021 (in Chinese). | |
139 | 康献民, 陈尧, 王建生, 等. 机械装备的数字孪生结构参数分析与评价方法研究[J]. 机床与液压, 2022, 50(19): 14-19. |
KANG X M, CHEN Y, WANG J S . et al. Research on digital twin parameter analysis and evaluation method of manipulator[J]. Machine Tool and Hydraulics, 2022, 50(19): 14-19 (in Chinese). | |
140 | PEREVERZEW P P, AKINTSEVA A V, ALSIGAR M K, et al. Designing optimal automatic cycles of round grinding based on the synthesis of digital twin technologies and dynamic programming method[J]. Mechanical Sciences, 2019, 10(1): 331-341. |
141 | SUN H B, YAO Y. Chapter 7-Digital twin-driven cutting tool service[M]. TAO F, QI Q L, NEE A Y C, editors. Digital twin driven service. Elsevier: Academic Press, 2022: 173-203. |
142 | VISHNU V S, VARGHESE K G, GURUMOORTHY B. A data-driven digital twin of CNC machining processes for predicting surface roughness[J]. Procedia CIRP, 2021, 104: 1065-1070. |
143 | 王岭. 基于数字孪生的航空发动机低压涡轮单元体对接技术研究[J]. 计算机测量与控制, 2018, 26(10): 286-290, 303. |
WANG L. Research on the docking technology of final installation for aeroengine low pressure turbine unit based on digital twin[J]. Computer Measurement & Control, 2018, 26(10): 286-290, 303 (in Chinese). | |
144 | 许敏俊, 刘世民, 沈慧, 等. 数字孪生驱动下的弱刚性钻削毛刺控制[J]. 计算机集成制造系统, 2023, 29(4): 1115-1126. |
XU M J, LIU S M, SHEN H, et al. Burr control of weak rigid drilling process driven by digital twin[J]. Computer Integrated Manufacturing Systems, 2023, 29(4): 1115-1126 (in Chinese). | |
145 | 周程辉. 基于数字孪生的渐进弯曲成形系统的研究[D]. 广州: 华南理工大学, 2021. |
ZHOU P C. Research on incremental bending forming system based on digital twin[D] Guangzhou: South China University of Technology, 2021 (in Chinese). | |
146 | “碳达峰与碳中和关键技术”专辑[J]. 中南大学学报(自然科学版), 2022, 53(12): 4583. |
“Carbon Peak and carbon neutrality key technology” album[J]. Journal of Central South University (Natural Science Edition), 2022, 53(12): 4583 (in Chinese). | |
147 | 向峰, 黄圆圆, 张智, 等. 基于数字孪生的产品生命周期绿色制造新模式[J]. 计算机集成制造系统, 2019, 25(6): 1505-1514. |
XIANG F, HUANG Y, ZHANG Z, et al. New paradigm of green manufacturing for product life cycle based on digital twin[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1505-1514 (in Chinese). | |
148 | XIANG F, ZHANG Z, ZUO Y, et al. Digital twin driven green material optimal-selection towards sustainable manufacturing[J]. Procedia CIRP, 2019, 81: 1290-1294. |
149 | LI L H, MAO C L, SUN H X, et al. Digital twin driven green performance evaluation methodology of intelligent manufacturing: hybrid model based on Fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II[J]. Complexity in Economics and Business, 2020, 2020: 1-24. |
150 | KERIN M, HARTONO N. PHAM D T. Optimising remanufacturing decision-making using the bees algorithm in product digital twins[J]. Scientific Reports, 2023, 13: 701. |
151 | 陶飞, 张辰源, 张贺, 等. 未来装备探索: 数字孪生装备[J]. 计算机集成制造系统, 2022, 28(1): 1-16. |
TAO F, ZHANG C Y, ZHANG H, et al. Future equipment exploration: digital twin equipment[J]. Computer Integrated Manufacturing Systems, 2022, 28(1): 1-16 (in Chinese). | |
152 | 黄圆圆. 基于数字孪生的制造服务能耗评估方法研究[D]. 武汉: 武汉科技大学, 2020. |
HUANG Y Y. Research on manufacturing services energy consumption evaluation method based on digital twin[D]. Wuhan: Wuhan University of Science and Technology, 2020 (in Chinese). | |
153 | 刘魁, 王潘, 刘婷. 数字孪生在航空发动机运行维护中的应用[J]. 航空动力, 2019, 9(4): 70-74. |
LIU K, WANG P, LIU T. The application of digital twin in aero engine operation and maintenance[J]. Aerospace Power, 2019, 9(4): 70-74 (in Chinese). | |
154 | 高士根, 周敏, 郑伟, 等. 基于数字孪生的高端装备智能运维研究现状与展望[J]. 计算机集成制造系统, 2022, 28(7): 1953-1965. |
GAO S G, ZHOU M, ZHENG W, et al. Intelligent operation and maintenance for advanced equipment based on digital twin: Challenges and future [J]. Computer Integrated Manufacturing Systems, 2022, 28(7): 1953-1965 (in Chinese). | |
155 | 吕伟. 基于数字孪生的液压运维系统[J]. 机械制造, 2022, 60(6): 40-44. |
LV W. Hydraulic operation and maintenance system based on digital twin[J]. Machinery, 2022, 60(6): 40-44 (in Chinese). | |
156 | 陈晓红, 刘飞香, 艾彦迪, 等. 面向智能制造的工业数字孪生关键技术特性[J]. 科技导报, 2022, 40(11): 45-54. |
CHEN X H, LIU F X, AI Y D, et al. Key characteristics analysis of industrial digital twins for smart manufacturing[J]. Science Technology Review, 2022, 40(11): 45-54 (in Chinese). | |
157 | 黄彬彬, 张映锋, 黄博, 等. 数字孪生驱动的复杂产品智能运维服务体系与核心技术[J]. 机械工程学报, 2022, 58(12): 250-260. |
HUANG B B, ZHANG Y F, HUANG B, et al. Architecture and key technologies of digital-twin-driven intelligent operation maintenance services for complex product[J]. Journal of Mechanical Engineering, 2022, 58(12), 250-260 (in Chinese). | |
158 | 曹明, 王鹏, 左洪福, 等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅱ: 地面综合诊断、寿命管理和智能维护维修决策[J]. 航空学报, 2022, 43(9): 625574. |
CAO M, WANG P, ZUO H F, et al. Current status, challenges and opportunities of civil aero-engine diagnostics & health management Ⅱ: Comprehensive off-board diagnosis, life management and intelligent condition based MRO[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(9): 625574 (in Chinese). | |
159 | QI Q L, TAO F, HU T L, et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems, 2021, 58: 3-21. |
160 | 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): 623173 (in Chinese). | |
161 | TAO F, ZHANG M, NEE A Y C. Chapter 8 - Digital twin and cloud, fog, edge computing[M]. TAO F, ZHANG M, NEE A Y C, editors. Digital twin driven smart manufacturing. Elsevier: Academic Press, 2019: 171-181. |
162 | 孟麒, 胡天亮, 马嵩华. 云-雾-边缘协同的数字孪生制造系统仿真过程动态扰动响应方法[J]. 计算机集成制造系统, 2023, 29(6): 1996-2005. |
MENG Q, HU T L, MA S H. Cloud-fog-edge collaboration digital twin manufacturing system simulation process and dynamic disturbance response method[J]. Computer Integrated manufacturing systems, 2023, 29(6): 1996-2005 (in Chinese). | |
163 | QI Q L, TAO F. Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison[J]. IEEE Access, 2018, 6: 3585-3593. |
164 | ZHANG C, ZHOU G H, LI J J, et al. A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0[J]. Journal of Manufacturing Systems, 2023, 66: 56-70. |
165 | LIU Z F, CHEN W, ZHANG C X, et al. Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop[J]. Journal of Manufacturing Systems, 2021, 58: 157-167. |
166 | MO F, REHMAN H U, MONETTI F M, et al. A framework for manufacturing system reconfiguration and optimization utilizing digital twins and modular artificial intelligence[J]. Robotics and Computer-Integrated Manufacturing, 2023, 82: 102524. |
167 | REN Z J, WAN J F. Strengthening digital twin applications based on machine learning for complex equipment[C]∥2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). 2021: 609-614. |
168 | TAO F, ZHANG M, NEE A Y C. Chapter 11 - Digital twin and virtual reality and augmented reality/mixed reality[M]. TAO F, ZHANG M, NEE A Y C, editors. Digital twin driven smart manufacturing. Elsevier: Academic Press, 2019: 219-241. |
169 | ZHU Z X, LIU C, XU X. Visualisation of the digital twin data in manufacturing by using augmented reality[J]. Procedia CIRP, 2019, 81: 898-903. |
170 | TAO F, ZHANG M, NEE A Y C. Chapter 12 - Digital twin, cyber-physical system, and internet of things[M]. TAO F, ZHANG M, NEE A Y C, editors. Digital twin driven smart manufacturing. Elsevier: Academic Press; 2019: 243-256. |
171 | MERTES J, GLATT M, SCHELLENBERGER C, et al. Development of a 5G-enabled digital twin of a machine tool[J]. Procedia CIRP, 2022, 107: 173-178. |
172 | LIU S M, LU Y Q, LI J, et al. A blockchain-based interactive approach between digital twin-based manufacturing systems[J]. Computers & Industrial Engineering, 2023, 175: 108827. |
173 | 张超, 周光辉, 李晶晶, 等. 新一代信息技术赋能的数字孪生制造单元系统关键技术及应用研究[J]. 机械工程学报, 2022, 58(16): 329-343. |
ZAHNG C, ZHOU G H, LI J J, et al. Research on key technologies and application of new IT-driven digital twin manufacturing cell system[J]. Journal of Mechanical Engineering, 2022, 58(16): 329-343 (in Chinese). | |
174 | FULLER A, FAN Z, DAY C, et al. Digital twin: enabling technologies, challenges and open research[J]. IEEE Access, 2020, 8: 108952-108971. |
175 | MIHAI S, YAQOOB M, HUNG D V, et al. Digital twins: a survey on enabling technologies, challenges, trends and future prospects[J]. IEEE Communications Surveys & Tutorials, 2022, 24(4): 2255-2291. |
176 | MOHSEN A, BILGE G C, twin Digital : benefits, use cases, challenges, and opportunities[J]. Decision Analytics Journal, 2023, 6: 100165. |
177 | WU J, YANG Y, CENG X, et al. The development of digital twin technology review[C]∥2020 Chinese Automation Congress (CAC). 2020: 4901-4906. |
/
〈 |
|
〉 |