Review Open Access

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Intelligent and Sustainable Manufacturing. 2024, 1(1), 10007;
Shuguo Hu 1    Changhe Li 1,2 *    Benkai Li 1    Min Yang 1,2    Xiaoming Wang 1    Teng Gao 1    Wenhao Xu 1    Yusuf Suleiman Dambatta 1,3    Zongming Zhou 2,4    Peiming Xu 5   
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
Qingdao Jimo Qingli intelligent manufacturing industry Research Institute, Qingdao 266201, China
Mechanical Engineering Department, Ahmadu Bello University, Zaria 810106, Nigeria
Hanergy (Qingdao) Lubrication Technology Co. Ltd., Qingdao 266200, China
Taishan Sports Industry Group Co., Ltd., Dezhou 253600, China
Authors to whom correspondence should be addressed.

Received: 26 Feb 2024    Accepted: 26 Mar 2024    Published: 28 Mar 2024   


Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.


Cui X, Li CH, Yang M, Liu M Z, Gao T, Wang X M, et al. Enhanced grindability and mechanism in the magnetic traction nanolubricant grinding of Ti-6Al-4 V. Tribol. Int. 2023, 186, 108603. [Google Scholar]
Duan ZJ, Li CH, Zhang YB, Yang M, Gao T, Liu X, et al. Mechanical behavior and semiempirical force model of aerospace aluminum alloy milling using nano biological lubricant. Front. Mech. Eng. 2023, 18, doi:10.1007/s11465-022-0720-4.
Cui X, Li CH, Zhang YB, Ding WF, An QL, Liu B, et al. Comparative assessment of force, temperature, and wheel wear in sustainable grinding aerospace alloy using biolubricant. Front. Mech. Eng. 2023, 18, doi:10.1007/s11465-022-0719-x.
Longo F, Nicoletti L, Padovano A. Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput. Ind. Eng. 2017, 113, 144–159. [Google Scholar]
Leng JW, Wang DW, Shen WM, Li XY, Liu Q, Chen X. Digital twins-based smart manufacturing system design in Industry 4.0: A review. J. Manuf. Syst. 2021, 60, 119–137. [Google Scholar]
Ilari S, Di Carlo F, Ciarapica FE, Bevilacqua M. Machine Tool Transition from Industry 3.0 to 4.0: A Comparison between Old Machine Retrofitting and the Purchase of New Machines from a Triple Bottom Line Perspective. Sustainability 2021, 13, 10441. [Google Scholar]
Fan YP, Yang JZ, Chen JH, Hu PC, Wang XY, Xu JC, et al. A digital-twin visualized architecture for Flexible Manufacturing System. J. Manuf. Syst. 2021, 60, 176–201. [Google Scholar]
Li BH, Hou BC, Yu WT, Lu XB, Yang CW. Applications of artificial intelligence in intelligent manufacturing: a review. Front. Inf. Technol. Electron. Eng. 2017, 18, 86–96. [Google Scholar]
Wu DZ, Rosen DW, Wang LH, Schaefer D. Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Comput.-Aided Des. 2015, 59, 1–14. [Google Scholar]
Wu DZ, Liu X, Hebert S, Gentzsch W, Terpenny J. Democratizing digital design and manufacturing using high performance cloud computing: Performance evaluation and benchmarking. J. Manuf. Syst. 2017, 43, 316–326. [Google Scholar]
Wang BC, Tao F, Fang XD, Liu C, Liu YF, Freiheit T. Smart Manufacturing and Intelligent Manufacturing: A Comparative Review. Engineering 2021, 7, 738–757. [Google Scholar]
Liu MZ, Li CH, Zhang YB, Yang M, Cui X, Li BK, et al. Heat Transfer Mechanism and Convective Heat Transfer Coefficient Model of Cryogenic Air Minimum Quantity Lubrication Grinding Titanium Alloy. J. Mech. Eng. 2023, 59, 343–357. [Google Scholar]
Aheleroff S, Xu X, Zhong RY, Lu YQ. Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model. Adv. Eng. Inf. 2021, 47, 101225. [Google Scholar]
Xu WH, Li CH, Zhang YB, Yang M, Zhou ZM, Chen Y, et al. Research Progress and Application of Electrostatic Atomization Minimum Quantity Lubrication. J. Mech. Eng. 2023, 59, 110–138. [Google Scholar]
Zhong RY, Xu X, Klotz E, Newman ST. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering 2017, 3, 616–630. [Google Scholar]
Silvestri L, Forcina A, Introna V, Santolamazza A, Cesarotti V. Maintenance transformation through Industry 4.0 technologies: A systematic literature review. Comput. Ind. 2020, 123, 103335. [Google Scholar]
Aloqaily M, AL Ridhawi I, Kanhere S. Reinforcing Industry 4.0 With Digital Twins and Blockchain-Assisted Federated Learning. IEEE J. Sel. Areas Commun. 2023, 41, 3504–3516. [Google Scholar]
Li LH, Lei BB, Mao CL. Digital twin in smart manufacturing. J. Ind. Inf. Integr. 2022, 26, 100289. [Google Scholar]
Zhang CY, Xu WJ, Liu JY, Liu ZH, Zhou ZD, Pham DT. Digital twin-enabled reconfigurable modeling for smart manufacturing systems. Int. J. Comput. Integr. Manuf. 2021, 34, 709–733. [Google Scholar]
Lu YQ, Liu C, Wang KI K, Huang HY, Xu X. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Rob. Comput. Integr. Manuf. 2020, 61, 101837. [Google Scholar]
Cheng J, Zhang H, Tao F, Juang CF. DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing. Rob. Comput. Integr. Manuf. 2020, 62, 101881. [Google Scholar]
Wang XM, Li CH, Yang M, Zhang YB, Liu MZ, Gao T, et al. Research Progress on the Physical Mechanism of Minimum Quantity Lubrication Machining with Nano-Biolubricants. J. Mech. Eng. 2024, 1–37, doi:11.2187.TH.20240123.1216.040.
Leng JW, Zhou M, Xiao YX, Zhang H, Liu Q, Shen WM, et al. Digital twins-based remote semi-physical commissioning of flow-type smart manufacturing systems. J. Clean. Prod. 2021, 306, 127278. [Google Scholar]
Qi QL, Tao F. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access 2018, 6, 3585–3593. [Google Scholar]
Geng RX, Li M, Hu ZY, Han ZX, Zheng RX. Digital Twin in smart manufacturing: remote control and virtual machining using VR and AR technologies. Struct. Multidiscip. Optim. 2022, 65, doi:10.1007/s00158-022-03426-3.
Hu SG, Li CH, Zhou ZM, Liu B, Zhang YB, Yang M, et al. Nanoparticle-enhanced coolants in machining: mechanism, application, and prospects. Front. Mech. Eng. 2023, 18, doi:10.1007/s11465-023-0769-8.
Xu WH, Li CH, Zhang YB, Ali HM, Sharma S, Li RZ, et al. Electrostatic atomization minimum quantity lubrication machining: from mechanism to application. Int. J. Extreme Manuf. 2022, 4, doi:10.1088/2631-7990/ac9652.
Cui X, Li CH, Ding WF, Chen Y, Mao C, Xu XF, et al. Minimum quantity lubrication machining of aeronautical materials using carbon group nanolubricant: From mechanisms to application. Chin. J. Aeronaut. 2022, 35, 85–112. [Google Scholar]
Wang XM, Li CH, Zhang YB, Ali HM, Sharma S, Li RZ, et al. Tribology of enhanced turning using biolubricants: A comparative assessment. Tribol. Int. 2022, 174, 107766. [Google Scholar]
Gu GQ, Wang DZ, Wu SJ, Zhou S, Zhang BX. Research Status and Prospect of Ultrasonic Vibration and Minimum Quantity Lubrication Processing of Nickel-based Alloys. Intell. Sustain. Manuf. 2024, 1, 10006. [Google Scholar]
Zhang H, Liu Q, Chen X, Zhang D, Leng JW. A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line. IEEE Access 2017, 5, 26901–26911. [Google Scholar]
Zhao GH, Jia P, Huang C, Zhou AM, Fang Y. A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks. IEEE Access 2020, 8, 65462–65471. [Google Scholar]
Cui X, Li CH, Zhang YB, Said Z, Debnath S, Sharma S, et al. Grindability of titanium alloy using cryogenic nanolubricant minimum quantity lubrication. J. Manuf. Processes 2022, 80, 273–286. [Google Scholar]
Tao F, Qi QL, Wang LH, Nee AYC. Digital Twins and Cyber-Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison. Engineering 2019, 5, 653–661. [Google Scholar]
Ciano MP, Pozzi R, Rossi T, Strozzi F. Digital twin-enabled smart industrial systems: a bibliometric review. Int. J. Comput. Integr. Manuf. 2021, 34, 690–708. [Google Scholar]
Pimenov DY, Bustillo A, Wojciechowski S, Sharma VS, Gupta MK, Kuntoglu M. Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J. Intell. Manuf. 2023, 34, 2079–2121. [Google Scholar]
Böttjer T, Tola D, Kakavandi F, Wewer CR, Ramanujan D, Gomes C, et al. A review of unit level digital twin applications in the manufacturing industry. CIRP J. Manuf. Sci. Technol. 2023, 45, 162–189. [Google Scholar]
Tao F, Zhan H, Liu A, Nee AYC. Digital Twin in Industry: State-of-the-Art.  IEEE Trans. Ind. Inf. 2019, 15, 2405–2415. [Google Scholar]
Tao F, Zhang M, Cheng J, Qi Q. Digital twin workshop: a new paradigm for future workshop. Comput. Integr. Manuf. Syst. 2017, 23, 1–9. [Google Scholar]
Tao F, Liu W, Zhang M, Hu T-L, Qi Q, Zhang H, et al. Five-dimension digital twin model and its ten applications. Comput. Integr. Manuf. Syst. 2019, 25, 1–18. [Google Scholar]
Wang BC, Liu YF, Zhou Y, Wen Z. Emerging nanogenerator technology in China: A review and forecast using integrating bibliometrics, patent analysis and technology roadmapping methods. Nano Energy 2018, 46, 322–330. [Google Scholar]
Muhuri PK, Shukla AK, Abraham A. Industry 4.0: A bibliometric analysis and detailed overview. Eng. Appl. Artif. Intell. 2019, 78, 218–235. [Google Scholar]
Grabowska S, Saniuk S, Gajdzik B. Industry 5.0: improving humanization and sustainability of Industry 4.0. Scientometrics 2022, 127, 3117–3144. [Google Scholar]
Fuller A, Fan Z, Day C, Barlow C. Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access 2020, 8, 108952–108971. [Google Scholar]
Alcácer V, Cruz-Machado V. Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Eng. Sci. Technol. Int. J. 2019, 22, 899–919. [Google Scholar]
Qi QL, Tao F, Hu TL, Anwer N, Liu A, Wei YL, et al. Enabling technologies and tools for digital twin. J. Manuf. Syst. 2021, 58, 3–21. [Google Scholar]
Maddikunta PKR, Pham QV, Prabadevi, Deepa N, Dev K, Gadekallu TR, et al. Industry 5.0: A survey on enabling technologies and potential applications. J. Ind. Inf. Integr. 2022, 26, 100257. [Google Scholar]
Barricelli BR, Casiraghi E, Fogli D. A survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access 2019, 7, 167653–167671. [Google Scholar]
Jin T, Sun ZD, Li L, Zhang Q, Zhu ML, Zhang ZX, et al. Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications. Nat. Commun. 2020, 11, 5381. [Google Scholar]
Cimino C, Negri E, Fumagalli L. Review of digital twin applications in manufacturing. Comput. Ind. 2019, 113, 103130. [Google Scholar]
Lim KYH, Zheng P, Chen CH. A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives. J. Intell. Manuf. 2020, 31, 1313–1337. [Google Scholar]
Wei HL, Mukherjee T, Zhang W, Zuback JS, Knapp GL, De A, Debroy T. Mechanistic models for additive manufacturing of metallic components. Prog. Mater Sci. 2021, 116, 100703. [Google Scholar]
Liu Q, Zhang H, Leng JW, Chen X. Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int. J. Prod. Res. 2019, 57, 3903–3919. [Google Scholar]
Liu Q, Zhang H, Leng JW, Chen X. Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int. J. Prod. Res. 2019, 57, 3903–3919. [Google Scholar]
Minerva R, Lee GM, Crespi N. Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models. Proc. IEEE 2020, 108, 1785–1824. [Google Scholar]
Luo WC, Hu TL, Ye YX, Zhang CR, Wei YL. A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Rob. Comput. Integr. Manuf. 2020, 65, 101974. [Google Scholar]
Liu SM, Lu YQ, Li J, Shen XW, Sun XM, Bao JS. A blockchain-based interactive approach between digital twin-based manufacturing systems. Comput. Ind. Eng. 2023, 175, 108827. [Google Scholar]
Tao F, Cheng JF, Qi QL, Zhang M, Zhang H, Sui FY. Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 2018, 94, 3563–3576. [Google Scholar]
Nath P, Mahadevan S. Probabilistic Digital Twin for Additive Manufacturing Process Design and Control. J. Mech. Des. 2022, 144, 91704. [Google Scholar]
Liu Q, Leng JW, Yan DX, Zhang D, Wei LJ, Yu AL, et al. Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. J. Manuf. Syst. 2021, 58, 52–64. [Google Scholar]
El Saddik A. Digital Twins the Convergence of Multimedia Technologies. IEEE Multimed. 2018, 25, 87–92. [Google Scholar]
Tao F, Sui FY, Liu A, Qi QL, Zhang M, Song BY, et al. Digital twin-driven product design framework. Int. J. Prod. Res. 2019, 57, 3935–3953. [Google Scholar]
Zhuang CB, Liu JH, Xiong H, Ding XY, Liu SL, Weng G. Connotation,architecture and trends of product digital twin. Comput. Integr. Manuf. Syst. 2017, 23, 753–768. [Google Scholar]
Lee J, Bagheri B, Kao H-A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [Google Scholar]
Söderberg R, Wärmefjord K, Carlson JS, Lindkvist L. Toward a Digital Twin for real-time geometry assurance in individualized production. CIRP Ann. 2017, 66, 137–140. [Google Scholar]
Grieves M, Vickers J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches; Springer International Publishing: New York City, NY, USA, 2017; pp. 85–113.
Haag S, Anderl R. Digital twin–Proof of concept. Manuf. Lett. 2018, 15, 64–66. [Google Scholar]
Li H, Wang HQ, Cheng Y, Tao F, Hao B, Wang XC, et al. Technology and Application of Data-driven Intelligent Services for Complex Products. China Mech. Eng. 2020, 31, 757–772. [Google Scholar]
He B, Bai KJ. Digital twin-based sustainable intelligent manufacturing: A review. Adv. Manuf. 2021, 9, 1–21, doi:10.1007/s40436-020-00302-5.
Li JJ, Zhou GH, Zhang C. A twin data and knowledge-driven intelligent process planning framework of aviation parts. Int. J. Prod. Res. 2022, 6060, 5217–5234. [Google Scholar]
Wang L, Zhou J, Cui YL. Application of Digital Twin in Aero Engine. Aerospace Power 2020, 63–66, doi:10.1016/j.jmsy.2020.04.012.
Zhang C, Zhou GH, Li JJ, Chang FT, Ding K, Ma DX. A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0. J. Manuf. Syst. 2023, 66, 56–70. [Google Scholar]
Ghosh AK, Ullah A, Teti R, Kubo A. Developing sensor signal-based digital twins for intelligent machine tools. J. Ind. Inf. Integr. 2021, 24, 100242. [Google Scholar]
Gao T, Li CH, Zhang YB, Yang M, Cao HJ, Wang DZ, et al. Mechanical Behavior of Material Removal and Predictive Force Model for CFRP Grinding Using Nano Reinforced Biological Lubricant. J. Mech. Eng. 2023, 59, 325–342. [Google Scholar]
Cui X, Li CH, Zhang YB, Yang M, Zhou ZM, Liu B, et al. Force Model and Verification of Magnetic Traction Nanolubricant Grinding. J. Mech. Eng. 2023, 1–15, doi:11.2187.TH.20231025.1742.022.
Liu DW, Li CH, Qin AG, Liu B, Chen Y, Zhang YB. Kinematic Analysis and Milling Force Model for Disc Milling Cutter of Indexable Inserts Considering Tool Runout. J. Mech. Eng. 2024, 1–13. Available online: (accessed on 26 February 2024).
Liu MN, Fang SL, Dong HY, Xu CZ. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346–361. [Google Scholar]
Cao X, Zhao G, Xiao WL. Digital Twin-oriented real-time cutting simulation for intelligent computer numerical control machining. Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. 2022, 236, 5–15. [Google Scholar]
Li CB, Sun X, Hou XB, Zhao XK, Wu SQ. Online Monitoring Method for NC Milling Tool Wear by Digital Twin-driven. China Mech. Eng. 2022, 33, 78–87. [Google Scholar]
Zhang L, Liu JH, Zhuang CB, Wang Y. Contour error reduction method for multi-axis CNC machine tools based on digital twin. Comput. Integr. Manuf. Syst. 2021, 27, 3391–3402. [Google Scholar]
Jiang XM, Yuan ZH, Lou P, Zhang XM, Yan JW, Hu JW. A Collision Detection Method of Heavy-duty CNC Machine Tools Based on Digital Twin. China Mech. Eng. 2022, 33, doi:10.3969/j.issn.1004-132X.2022.22.001.
Duan JG, Ma TY, Zhang QL, Liu Z, Qin JY. Design and application of digital twin system for the blade-rotor test rig. J. Intell. Manuf. 2023, 34, 753–769. [Google Scholar]
Sun MB, An B, Wang HB, Wang CL. Numerical Simulation of the Scramject Engine: From Numerical Flight to Intelligent Numerical Flight. Chin. J. Theor. Appl. Mech. 2022, 54, 588–600. [Google Scholar]
Liu ZF, Chen W, Zhang CX, Yang CB, Cheng Q. Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop. J. Manuf. Syst. 2021, 58, 157–167. [Google Scholar]
Yang M, Kong M, Li CH, Long YZ, Zhang YB, Sharma S, et al. Temperature field model in surface grinding: a comparative assessment. Int. J. Extreme Manuf. 2023, 5, doi:10.1088/2631-7990/acf4d4.
Sun JA, Li CH, Zhou ZM, Liu B, Zhang YB, Yang M, et al. Material Removal Mechanism and Force Modeling in Ultrasonic Vibration-Assisted Micro-Grinding Biological Bone. Chin. J. Mech. Eng. 2023, 36, doi:10.1186/s10033-023-00957-8.
Huang ZH, Li CH, Zhou ZM, Liu B, Zhang YB, Yang M, et al. Magnetic bearing: structure, model, and control strategy. Int. J. Adv. Manuf. Technol. 2023, doi:10.1007/s00170-023-12389-8.
Shi Z, Li CH, Liu DW, Zhang YB, Qin AG, Cao HJ, et al. Instantaneous Milling Force Model and Verification of Unequal Helix Angle End Mill. J. Mech. Eng. 2024, 1–14. Available online: (accessed on 26 February 2024).
Mo F, Rehman HU, Monetti FM, Chaplin JC, Sanderson D, et al. A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Rob. Comput. Integr. Manuf. 2023, 82, 102524. [Google Scholar]
Lombardo G, Picone M, Mamei M, Mordonini M, Poggi A. Digital Twin for Continual Learning in Location Based Services. Eng. Appl. Artif. Intell. 2024, 127, 107203. [Google Scholar]
Alexopoulos K, Nikolakis N, Chryssolouris G. Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. Int. J. Comput. Integr. Manuf. 2020, 33, 429–439. [Google Scholar]
Gong P, Zhang YB, Wang CJ, Cui X, Li RZ, Sharma S, et al. Residual stress generation in grinding: Mechanism and modeling. J. Mater. Process. Technol. 2024, 324, 118262. [Google Scholar]
Wang YC, Wang XH, Liu A, Zhang JQ, Zhang JH. Ontology of 3D virtual modeling in digital twin: a review, analysis and thinking. J. Intell. Manuf. 2023, doi:10.1007/s10845-023-02246-6.
Zheng MT, Tian L. Digital product twin modeling of massive dynamic data based on a time-series database. J. Tsinghua Univ. 2021, 61, 1281–1288. doi:10.16511/j.cnki.qhdxxb.2021.26.006.
Zheng ML, Tian L. Blockchain-based collaborative evolution method for digital twin ontology model of mechanical products. Comput. Integr. Manuf. Syst. 2023, 29, 1781–1794. [Google Scholar]
Sun XM, Bao JS, Li J, Zhang YM, Liu SM, Zhou B. A digital twin-driven approach for the assembly-commissioning of high precision products. Rob. Comput. Integr. Manuf. 2020, 61, 101839. [Google Scholar]
Hu WY, Wang TY, Chu FL. A Wasserstein generative digital twin model in health monitoring of rotating machines. Comput. Ind. 2023, 145, 103807. [Google Scholar]
Liang ZS, Wang ST, Peng YL, Mao XY, Yuan X, Yang AD, et al. The process correlation interaction construction of Digital Twin for dynamic characteristics of machine tool structures with multi-dimensional variables J. Manuf. Syst. 2022, 63, 78–94. [Google Scholar]
Yu JS, Song Y, Tang DY, Dai J. A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring. J. Manuf. Syst. 2021, 58, 293–304. [Google Scholar]
Liu SM, Bao JS, Lu YQ, Li J, Lu SY, Sun XM. Digital twin modeling method based on biomimicry for machining aerospace components. J. Manuf. Syst. 2021, 58, 180–195. [Google Scholar]
Liu SM, Sun YC, Zheng P, Lu YQ, Bao JS. Establishing a reliable mechanism model of the digital twin machining system: An adaptive evaluation network approach. J. Manuf. Syst. 2022, 62, 390–401. [Google Scholar]
Shen H, Liu SM, Xu MJ, Huang DL, Bao JS, Zheng XH. Adaptive Transferring Method of Digital Twin Model for Machining Domain. J. Shanghai Jiaotong Univ. 2022, 56, 70–80. doi:10.16183/j.cnki.jsjtu.2021.167.
Bergs T, Biermann D, Erkorkmaz K, M’saoubi R. Digital twins for cutting processes. CIRP Ann.-Manuf. Technol. 2023, 72, 541–567. [Google Scholar]
Wei YL, Hu TL, Wei SY, Ma SH, Wang YQ. Digital twin technology applicability evaluation method for CNC machine tool. Int. J. Adv. Manuf. Technol. 2022, doi:10.1007/s00170-022-10050-4.
Li LY, Zhang YB, Cui X, Said Z, Sharma S, Liu MZ, et al. Mechanical behavior and modeling of grinding force: A comparative analysis. J. Manuf. Processes 2023, 102, 921–954. [Google Scholar]
Jia DZ, Li CH, Liu JH, Zhang YB, Yang M, Gao T, et al. Prediction model of volume average diameter and analysis of atomization characteristics in electrostatic atomization minimum quantity lubrication. Friction 2023, 11, 2107–2131. [Google Scholar]
Liu MZ, Li CH, Zhang YB, Yang M, Gao T, Cui X, et al. Analysis of grain tribology and improved grinding temperature model based on discrete heat source. Tribol. Int. 2023, 180, 108196. [Google Scholar]
Liu DW, Li CH, Dong L, Qin AG, Zhang YB, Yang M, et al. Kinematics and improved surface roughness model in milling. Int. J. Adv. Manuf. Technol. 2022, doi:10.1007/s00170-022-10729-8.
Kaewunruen S, Lian Q. Digital twin aided sustainability-based lifecycle management for railway turnout systems. J. Clean. Prod. 2019, 228, 1537–1551. [Google Scholar]
Liu MZ, Li CH, Zhang YB, Yang M, Gao T, Cui X, et al. Analysis of grinding mechanics and improved grinding force model based on randomized grain geometric characteristics. Chin. J. Aeronaut. 2023, 36, 160–193. [Google Scholar]
Zhang XT, Li CH, Zhou ZM, Liu B, Zhang YB, Yang M, et al. Vegetable Oil-Based Nanolubricants in Machining: From Physicochemical Properties to Application. Chin. J. Mech. Eng. 2023, 36, doi:10.1186/s10033-023-00895-5.
Liu MZ, Li CH, Yang M, Gao T, Wang XM, Cui X, et al. Mechanism and enhanced grindability of cryogenic air combined with biolubricant grinding titanium alloy. Tribol. Int. 2023, 187, 108704. [Google Scholar]
Zhao WT, Zhang C, Fan B, Wang JG, Gu FS, Peyrano OG, et al. Research on rolling bearing virtual-real fusion life prediction with digital twin. Mech. Syst. Signal Process 2023, 198, 110434. [Google Scholar]
Feng K, Ji JC, Zhang YC, Ni Q, Liu Z, Beer M. Digital twin-driven intelligent assessment of gear surface degradation. Mech. Syst. Signal Process 2023, 186, 109896. [Google Scholar]
Zheng CM, Zhang L, Kang YH, Zhan YJ, Xu YC. In-process identification of milling parameters based on digital twin driven intelligent algorithm. Int. J. Adv. Manuf. Technol. 2022, 121, 6021–6033. [Google Scholar]
Zhao WT, Zhang C, Wang JG, Wang S, Lv D, Qin FF. Research on Digital Twin Driven Rolling Bearing Model-Data Fusion Life Prediction Method.  IEEE Access 2023, 11, 48611–48627. [Google Scholar]
Luo WC, Hu TL, Zhang CR, Wei YL. Digital twin for CNC machine tool: modeling and using strategy. J. Ambient Intell. Hum. Comput. 2019, 10, 1129–1140. [Google Scholar]
Liu SM, Lu YQ, Zheng P, Shen H, Bao JS. Adaptive reconstruction of digital twins for machining systems: A transfer learning approach. Rob. Comput. Integr. Manuf. 2022, 78, 102390. [Google Scholar]
De Giacomo G, Favorito M, Leotta F, Mecella M, Silo L. Digital twin composition in smart manufacturing via Markov decision processes. Comput. Ind. 2023, 149, 103916. [Google Scholar]
Donato L, Galletti C, Parente A. Self-updating digital twin of a hydrogen-powered furnace using data assimilation. Appl. Therm. Eng. 2024, 236, 121431. [Google Scholar]
Friederich J, Francis DP, Lazarova-Molnar S, Mohamed N. A framework for data-driven digitial twins of smart manufacturing systems. Comput. Ind. 2022, 136, 103586. [Google Scholar]
Haynes P, Yang S. Supersystem digital twin-driven framework for new product conceptual design. Adv. Eng. Inf. 2023, 58, 102149. [Google Scholar]
Karagiannis D, Buchmann RA, Utz W. The OMiLAB Digital Innovation environment: Agile conceptual models to bridge business value with Digital and Physical Twins for Product-Service Systems development. Comput. Ind. 2022, 138, 103631. [Google Scholar]
Liu JF, Zhou HG, Liu XJ, Tian GZ, Wu MF, Cao LP, et al. Dynamic Evaluation Method of Machining Process Planning Based on Digital Twin. IEEE Access 2019, 7, 19312–19323. [Google Scholar]
Yu P, Wang ZY, Guo YF, Tai NL, Jun W. Application prospect and key technologies of digital twin technology in the integrated port energy system. Front. Energy Res. 2023, 10, 1044978. [Google Scholar]
Huang SH, Wang GX, Yan Y. Building blocks for digital twin of reconfigurable machine tools from design perspective. Int. J. Prod. Res. 2022, 60, 942–956. [Google Scholar]
Xue RJ, Zhang PS, Huang ZG, Wang JJ. Digital twin-driven fault diagnosis for CNC machine tool. Int. J. Adv. Manuf. Technol. 2022, doi:10.1007/s00170-022-09978-4.
Guo MY, Fang XF, Hu ZT, Li Q. Design and research of digital twin machine tool simulation and monitoring system. Int. J. Adv. Manuf. Technol. 2023, 124, 4253–4268. [Google Scholar]
Huang SH, Wang GX, Yan Y, Fang XB. Blockchain-based data management for digital twin of product. J. Manuf. Syst. 2020, 54, 361–371. [Google Scholar]
Wu YD, Zhou LZ, Zheng P, Sun YQ, Zhang KK. A digital twin-based multidisciplinary collaborative design approach for complex engineering product development. Adv. Eng. Inf. 2022, 52, 101635. [Google Scholar]
Pan L, Guo X, Luan Y, Wang H. Design and realization of cutting simulation function of digital twin system of CNC machine tool. Procedia Comput. Sci. 2021, 183, 261–266. [Google Scholar]
Wang JJ, Niu XT, Gao RX, Huang ZG, Xue RJ. Digital twin-driven virtual commissioning of machine tool. Rob. Comput. Integr. Manuf. 2023, 81, 102499. [Google Scholar]
Wang JJ, Niu XT, Gao RX, Huang ZG, Xue RJ. Digital twin-driven virtual commissioning of machine tool. Rob. Comput. Integr. Manuf. 2023, 81, 102499. [Google Scholar]
Seidel R, Rachinger B, Thielen N, Schmidt K, Meier S, Franke J. Development and validation of a digital twin framework for SMT manufacturing. Comput. Ind. 2023, 145, 103831. [Google Scholar]
Liu JS, Yu D, Hu Y, Yu HY, He WW, Zhang LP. CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework. IEEE Access 2021, 9, 118801–118814. [Google Scholar]
Lv JH, Li XY, Sun YC, Zheng Y, Bao JS. A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools. Rob. Comput. Integr. Manuf. 2023, 80, 102489. [Google Scholar]
Yang X, Ran Y, Zhang GB, Wang HW, Mu ZY, Zhi SG. A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool. Rob. Comput. Integr. Manuf. 2022, 73, 102230. [Google Scholar]
Liu K, Song L, Han W, Cui YM, Wang YQ. Time-Varying Error Prediction and Compensation for Movement Axis of CNC Machine Tool Based on Digital Twin. IEEE Trans. Ind. Inf. 2022, 18, 109–118. [Google Scholar]
Zhu LD, Liu CF. Recent progress of chatter prediction, detection and suppression in milling. Mech. Syst. Signal Process 2020, 143, 106840. [Google Scholar]
Ghosh AK, Ullah A, Kubo A. Hidden Markov model-based digital twin construction for futuristic manufacturing systems. AI EDAM 2019, 33, 317–331. [Google Scholar]
Zhu ZX, Xi XL, Xu X, Cai YL. Digital Twin-driven machining process for thin-walled part manufacturing. J. Manuf. Syst. 2021, 59, 453–466. [Google Scholar]
Wang G, Cao YS, Zhang YF. Digital twin-driven clamping force control for thin-walled parts. Adv. Eng. Inf. 2022, 51, 101468. [Google Scholar]
Dai S, Zhao G, Yu Y, Zheng P, Bao QW, Wang W. Ontology-based information modeling method for digital twin creation of as-fabricated machining parts. Rob. Comput. Integr. Manuf. 2021, 72, 102173. [Google Scholar]
Zhang W, Zhang X, Zhao WH. Research on the multi-physical coupling characteristics of the machine tool and milling process based on the systematically integrated model. J. Manuf. Processes 2023, 105, 46–69. [Google Scholar]
Li JY, Zhao G, Zhang PF, Xu MC, Cheng H, Han PF. A Digital Twin-based on-site quality assessment method for aero-engine assembly. J. Manuf. Syst. 2023, 71, 565–580. [Google Scholar]
Wang BK, Sun WL, Wang HW, Xu TT, Zou Y. Research on rapid calculation method of wind turbine blade strain for digital twin. Renew. Energy 2024, 221, 119783. [Google Scholar]
Kale AP, Wahul RM, Patange AD, Soman R, Ostachowicz W. Development of Deep Belief Network for Tool Faults Recognition. Sensors 2023, 23, 1872. [Google Scholar]
Cheng MH, Jiao L, Yan P, Jiang HS, Wang RB, Qiu TY, et al. Intelligent tool wear monitoring and multi-step prediction based on deep learning model. J. Manuf. Syst. 2022, 62, 286–300. [Google Scholar]
An Q, Yang J, Li J, Liu G, Chen M, Li C. A State-of-the-art Review on the Intelligent Tool Holders in Machining. Intell. Sustain. Manuf. 2024, 1, 10002. [Google Scholar]
Gao T, Li CH, Wang YQ, Liu XS, An QL, Li HN, et al. Carbon fiber reinforced polymer in drilling: From damage mechanisms to suppression. Compos. Struct. 2022, 286, 115232. [Google Scholar]
Qiao Q, Wang J, Ye L, Gao RX. Digital twin for machining tool condition prediction. Procedia CIRP 2019, 81, 1388–1393. [Google Scholar]
Natarajan S, Thangamuthu M, Gnanasekaran S, Rakkiyannan J. Digital Twin-Driven Tool Condition Monitoring for the Milling Process. Sensors 2023, 23, 5431. [Google Scholar]
Deebak BD, Al-Turjman F. Digital-twin assisted: Fault diagnosis using deep transfer learning for machining tool condition. Int. J. Intell. Syst. 2022, 37, 10289–10316. [Google Scholar]
Zhuang KJ, Shi ZC, Sun YB, Gao ZM, Wang L. Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process. Symmetry 2021, 13, 1438. [Google Scholar]
Zhang H, Qi QL, Ji W, Tao F. An update method for digital twin multi-dimension models. Rob. Comput. Integr. Manuf. 2023, 80, 102481. [Google Scholar]
Xia W, Liu XL, Yue CX, Li HS, Li RY, Wei XD. Tool wear image on-machine detection based on trajectory planning of 6-DOF serial robot driven by digital twin. Int. J. Adv. Manuf. Technol. 2023, 125, 3761–3775. [Google Scholar]
Chen JL, Li S, Leng XL, Li CP, Kurniawan R, Kwak Y, et al. Bionic digital brain realizing the digital twin-cutting process. Rob. Comput. Integr. Manuf. 2023, 84, 102591. [Google Scholar]
Liu LL, Zhang XY, Wan X, Zhou SC, Gao ZG. Digital twin-driven surface roughness prediction and process parameter adaptive optimization. Adv. Eng. Inf. 2022, 51, 101470. [Google Scholar]
Song QH, Peng ZY, Wang RQ, Liu ZQ. Tool wear state identification method of thin-walled parts milling process driven by digital twin. Aeronaut. Manuf. Technol. 2023, 66, doi:10.16080/j.issn1671-833x.2023.03.046.
Zhang CL, Zhou TT, Hu TL, Xiao GC, Cheng ZQ. Construction method of digital twin model for cutting tools under variable working conditions. Comput. Integr. Manuf. Syst. 2023, 29, 1852–1866. [Google Scholar]
Xie Y, Lian KL, Liu Q, Zhang CY, Liu HQ. Digital twin for cutting tool: Modeling, application and service strategy. J. Manuf. Syst. 2021, 58, 305–312. [Google Scholar]
Weckx S, Robyns S, Baake J, Kikken E, De Geest R, Birem M, et al. A cloud-based digital twin for monitoring of an adaptive clamping mechanism used for high performance composite machining. Procedia Comput. Sci. 2022, 200, 227–236. [Google Scholar]
Wang KJ, Lee YH, Angelica S. Digital twin design for real-time monitoring - a case study of die cutting machine. Int. J. Prod. Res. 2021, 59, 6471–6485. [Google Scholar]
Huang ZQ, Fey M, Liu C, Beysel E, Xu X, Brecher C. Hybrid learning-based digital twin for manufacturing process: Modeling framework and implementation. Rob. Comput. Integr. Manuf. 2023, 82, 102545. [Google Scholar]
Tong X, Liu Q, Zhou YN, Sun PP. A digital twin-driven cutting force adaptive control approach for milling process. J. Intell. Manuf. 2023, doi:10.1007/s10845-023-02193-2.
Schoenemann L, Riemer O, Karpuschewski B, Schreiber P, Klemme H, Denkena B. Digital surface twin for ultra-precision high performance cutting. Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 2022, 77, 349–359. [Google Scholar]
Van Dinter R, Tekinerdogan B, Catal C. Predictive maintenance using digital twins: A systematic literature review. Inf. Software Technol. 2022, 151, 107008. [Google Scholar]
Zhong D, Xia ZL, Zhu Y, Duan JH. Overview of predictive maintenance based on digital twin technology. Heliyon 2023, 9, e14534. [Google Scholar]
D’amico RD, Erkoyuncu JA, Addepalli S, Penver S. Cognitive digital twin: An approach to improve the maintenance management. CIRP J. Manuf. Sci. Technol. 2022, 38, 613–630. [Google Scholar]
He B, Liu L, Zhang D. Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review. J. Comput. Inf. Sci. Eng. 2021, 21, doi:10.1115/1.4049537.
Van Dinter R, Tekinerdogan B, Catal C. Reference architecture for digital twin-based predictive maintenance systems. Comput. Ind. Eng. 2023, 177, 109099. [Google Scholar]
Xu Y, Sun YM, Liu XL, Zheng YH. A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning. IEEE Access 2019, 7, 19990–19999. [Google Scholar]
Zhao LL, Fang YL, Lou P, Yan JW, Xiao AR. Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin.  Int. J. Precis. Eng. Manuf. 2021, 22, 933–949. [Google Scholar]
Pereverzev PP, Akintseva AV, Alsigar MK, Ardashev DV. Designing optimal automatic cycles of round grinding based on the synthesis of digital twin technologies and dynamic programming method. Mech. Sci. 2019, 10, 331–341. [Google Scholar]
Vishnu VS, Varghese KG, Gurumoorthy B. A data-driven digital twin framework for key performance indicators in CNC machining processes. Int. J. Comput. Integr. Manuf. 2023, 36, 1823–1841. [Google Scholar]
Chen JL, Li S, Teng HW, Leng XL, Li CP, Kurniawan R, Ko TJ. Digital twin-driven real-time suppression of delamination damage in CFRP drilling. J. Intell. Manuf. 2024. doi:10.1007/s10845-023-02315-w.
Liu SM, Bao JS, Zheng P. A review of digital twin-driven machining: From digitization to intellectualization. J. Manuf. Syst. 2023, 67, 361–378. [Google Scholar]
Ritto TG, Rochinha FA. Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mech. Syst. Signal Process 2021, 155, 107614. [Google Scholar]
Liu SM, Lu YQ, Shen XW, Bao JS. A digital thread-driven distributed collaboration mechanism between digital twin manufacturing units. J. Manuf. Syst. 2023, 68, 145–159. [Google Scholar]
Chen M, Zhang Y, Liu B, Zhou Z, Zhang N, Wang H, et al. Design of Intelligent and Sustainable Manufacturing Production Line for Automobile Wheel Hub. Intell. Sustain. Manuf. 2024, 1, 10003. [Google Scholar]
Zhang M, Tao F, Nee AYC. Digital Twin Enhanced Dynamic Job-Shop Scheduling. J. Manuf. Syst. 2021, 58, 146–156. [Google Scholar]
Xiang F, Zhang Z, Zuo Y, Tao F. Digital twin driven green material optimal-selection towards sustainable manufacturing. Procedia Cirp. 2019, 81, 1290–1294. [Google Scholar]
Li LH, Mao CL, Sun HX, Yuan YP, Lei BB. Digital Twin Driven Green Performance Evaluation Methodology of Intelligent Manufacturing: Hybrid Model Based on Fuzzy Rough-Sets AHP, Multistage Weight Synthesis, and PROMETHEE II. Complexity 2020, 2020, doi:10.1155/2020/3853925.
Kerin M, Hartono N, Pham DT. Optimising remanufacturing decision-making using the bees algorithm in product digital twins. Sci. Rep. 2023, 13, doi:10.1038/s41598-023-27631-2.
Li YF, Li M, Yan Z, Li RX, Tian A, Xu XM, et al. Application of Life Cycle of Aeroengine Mainshaft Bearing Based on Digital Twin. Processes 2023, 11, 1768. [Google Scholar]
Fu Y, Zhu G, Zhu ML, Xuan FZ. Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview. Chin. J. Mech. Eng. 2022, 35, 80. [Google Scholar]
Chen C, Fu HB, Zheng Y, Tao F, Liu Y. The advance of digital twin for predictive maintenance: The role and function of machine learning. J. Manuf. Syst. 2023, 71, 581–594. [Google Scholar]
Huang BB, Zhang YF, Huang B, Ren S, Shi LC. Architecture and Key Technologies of Digital-twin-driven Intelligent Operation & Maintenance Services for Complex Product. J. Mech. Eng. 2022, 58, 250–260. [Google Scholar]
Bofill J, Abisado M, Villaverde J, Sampedro GA. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors 2023, 23, 7087. [Google Scholar]
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