Smart Digital Twin Engineering for Advanced CNC Machine Tools

doi:10.3901/JME.260473

Abstract

Abstract: Advanced computer numerical control (CNC) machines are significant to the development of modern manufacturing. Enabling next-generation smart manufacturing requires the development of high-performance, intelligent CNC machines and significant improvements in their design, operation, and maintenance capabilities. To achieve these objectives, this paper builds on the authors’ prior work on the five-dimensional digital twin model, as well as on digital twin theories, technologies, tools, and standards. First, 16 requirements for next-generation advanced CNC machines are discussed. Second, the state of the art of digital twin–enabled advanced CNC machines across their entire life-cycles is systematically reviewed, particularly in the aerospace, aviation, maritime, automotive, and power-equipment domains, and the limitations of current research, applications, standards, and software are analyzed. Third, an architecture for smart digital twin engineering of CNC machines is presented; this architecture involves life-cycle, model, digital, intelligent, software, and digital–physical interaction and fusion engineering. Fourth, eight enabling technologies for smart digital twin engineering of advanced CNC machines are outlined, including intelligent generative and forward-design technologies, high-performance smart manufacturing, cyber-physical ETV, smart operation and maintenance, full-life-cycle modeling and data integration, software platform development, and supporting technologies. Moreover, a series of insights and recommendations are provided for the development and implementation of smart digital twin engineering for CNC machines. This work aims to provide guidance on the intelligent upgrading of CNC machines and to support China’s transition from a manufacturing powerhouse to a leader in smart manufacturing.

Key words: advanced CNC machine tool, smart digital twin engineering, digital twin, digital engineering, artificial intelligence, smart digital twin

CLC Number:

I253

TAO Fei, AN Yida, LI Yilin, LAN Wenkai, ZHANG Chenyuan, HE Jue, ZHANG Yongping, ZUO Ying, SONG Lukai, XUE Ruijuan, HUANG Zuguang, HU Tianliang, JI Shuai, SUN Zheng, LIU Xiaojun, YANG Xingkai, REN Yinghui, FENG Kai. Smart Digital Twin Engineering for Advanced CNC Machine Tools[J]. Journal of Mechanical Engineering, 2026, 62(13): 241-269.

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References

[1] 陶飞，张辰源，刘蔚然，等. 数字工程及十个领域应用展望[J]. 机械工程学报，2023，59(13)：193-215. TAO Fei，ZHANG Chenyuan，LIU Weiran，et al. Digital engineering and its ten application outlooks[J]. Journal of Mechanical Engineering，2023，59(13)：193-215.
[2] 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 Fei，LIU Weiran，LIU Jianhua，et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems，2018，24(1)：1-18.
[3] 陶飞，刘蔚然，张萌，等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统，2019，25(1)：1-18. TAO Fei，LIU Weiran，ZHANG Meng，et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems，2019，25(1)：1-18.
[4] TAO F，SUN X，CHENG J，et al. make Twin：A reference architecture for digital twin software platform[J]. Chinese Journal of Aeronautics，2024，37(1)：1-18.
[5] QI Q，TAO F，HU T，et al. Enabling technologies and tools for digital twin[J]. Journal of Manufacturing Systems，2021，58：3-21.
[6] 陶飞，孙清超，孙惠斌，等. 航空发动机数字孪生工程:内涵与关键技术[J]. 航空学报，2024，45(21)：630283. TAO Fei，SUN Qingchao，SUN Huibin，et al. Aero-engine digital twin engineering：Connotation and key technologies[J]. Acta Aeronautica et Astronautica Sinica，2024，45(21)：630283.
[7] 陶飞，高鹏飞，张辰源，等. 数字试验测试验证：理论、关键技术及应用探索[J]. 机械工程学报，2024，60(15)：227-254. TAO Fei，GAO Pengfei，ZHANG Chenyuan，et al. D-ETV：Digital experiment，testing and verification[J]. Journal of Mechanical Engineering，2024，60(15)：227-254.
[8] 郭东明. 高性能制造[J]. 机械工程学报，2022，58(21)：225-242. GUO Dongming. High performance manufacturing[J]. Journal of Mechanical Engineering，2022，58(21)：225-242.
[9] TAO F，SUI F，LIU A，et al. Digital twin-driven product design framework[J]. International Journal of Production Research，2019，57(12)：3935-3953.
[10] TAO F，LIU A，HU T，Digital twin driven smart design[M]. London：Academic Press，2020.
[11] XIAO J，FAN K. Research on the digital twin for thermal characteristics of motorized spindle[J]. The International Journal of Advanced Manufacturing Technology，2022，119(7)：5107-5118.
[12] YI H，FAN K. Co-simulation-based digital twin for thermal characteristics of motorized spindle[J]. The International Journal of Advanced Manufacturing Technology，2023，125(9)：4725-4737.
[13] ARIEF T M，LIN W Z，ROYANDI M A，et al. Development of a digital model for predicting the variation in bearing preload and dynamic characteristics of a milling spindle under thermal effects[J]. Lubricants，2024，12(6)：185.
[14] YUCE B，LI H，WANG L，et al. Digital twin-based smart feeding system design for machine tools[J]. Electronics，2024，13(23)：4831.
[15] ZHANG W，ZHU D，HUANG Z，et al. Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system[J]. Advances in Mechanical Engineering，2022，14(6)：16878132221108491.
[16] ZHANG M，SHENG Z，XIONG J，et al. Accurate modeling of threaded connections and evaluation of contact surface distribution errors oriented to accurate digital twins[J]. Measurement，2025，247：116821.
[17] PIVKIN P M. Digital twin of the technological process for grinding helical flutes of a cutting tool[C]//Real-time Processing of Image，Depth，and Video Information 2024. SPIE，2024，13000：9-20.
[18] 丁明娜，刘献礼，岳彩旭，等. 面向智能制造过程的刀具设计制备与管控技术[J]. 机械工程学报，2023，59(19)：429-459. DING Mingna，LIU Xianli，YUE Caixu，et al. Design，manufacturing，management and control technology of cutting tools for intelligent manufacturing process[J]. Journal of Mechanical Engineering，2023，59(19)：429-459.
[19] LI C，GE W，HUANG Z，et al. Digital twin-driven modeling and application of carbon emission for machine tool[J]. The International Journal of Advanced Manufacturing Technology，2024，133(11)：5595-5609.
[20] Florescu A. Digital twin for flexible manufacturing systems and optimization through simulation：A case study[J]. Machines，2024，12(11)：785.
[21] 高鸿飞. 智能浪潮系统为先——“华中9型”新一代智能数控系统在京正式发布[J]. 制造技术与机床，2021(5)：2-23. GAO Hongfei. Intelligent wave system takes the lead：the new generation intelligent CNC system ‘Huazhong Type 9’officially launched in beijing[J]. Manufacturing Technology & Machine Tools，2021(5)：22-23.
[22] 薛瑞娟，王楚婷，于东，等. 智能化数控系统标准体系研究[J]. 制造技术与机床，2025(5)：68-79. XUE Ruijuan，WANG Chuting，YU Dong，et al. Research on intelligent CNC system standard system[J]. Manufacturing Technology & Machine Tool，2025(5)：68-79.
[23] 肖文磊，曹宪，赵罡，等. 面向数控加工的数字孪生系统[J]. 航空制造技术，2020，63(Z2)：46-55. XIAO Wenlei，CAO Xian，ZHAO Gang，et al. Digital twin system for CNC machining[J]. Aeronautical Manufacturing Technology，2020，63(Z2)：46-55.
[24] LIU W，GUO D，ROGERS J. Catch-up of CoPs in GIS and IVC：Evidence from Chinese CNC machine tools[J]. Science and Public Policy，2025，52(3)：475-488.
[25] 刘国强，段明皞，王堃宇，等. 基于数字孪生的国产五轴数控机床虚拟调试系统的研究与实现[J]. 组合机床与自动化加工技术，2025(4)：13-17，22. LIU Guoqiang，DUAN Minghao，WANG Kunyu，et al. Research and implementation of virtual debugging system for domestic five axis CNC Machine Tools Based on digital twins[J]. Combination Machine Tool and Automation Processing Technology，2025(4)：13-17，22
[26] ZHANG C，SUN Q C，SUN W，et al. A construction method of digital twin model for contact characteristics of assembly interface[J]. The International Journal of Advanced Manufacturing Technology，2021，113：2685-2699.
[27] HUANG S，WANG G，YAN Y. Building blocks for digital twin of reconfigurable machine tools from design perspective[J]. International of Production Research，2022，60(3)：942-956.
[28] ZHAO P，SUN B. Adaptive modification of digital twin model of CNC machine tools coordinately driven by mechanism model and data model[C]//Journal of Physics：Conference Series. IOP Publishing，2021，1875(1)：012003.
[29] BERNARDIN P，HAJICEK Z，JANDA P，et al. Methodology of using CAx and digital twin methods in the development of a multifunctional portal centre in its pre-production phase[J]. Applied Sciences，2025，15(6)：3312-3312.
[30] HUYNH H，ALTINTAS Y. Modeling the dynamics of five-axis machine tool using the multibody approach[J]. Journal of Manufacturing Science and Engineering，2021，143(2)：021012.
[31] TAO F，ZHANG M，NEE AYC. Digital twin driven smart manufacturing[M]. London：Academic Press，2019.
[32] 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 Fei，ZHANG Chenyuan，ZHANG He，et al. Future equipment exploration：Digital twin equipment[J]. Computer Integrated Manufacturing Systems，2022，28(1)：1-16.
[33] ZUO Y，YOU H，ZOU X，et al. Digital twin enhanced quality prediction method of powder compaction process[J]. Robotics and Computer-Integrated Manufacturing，2024，89：102762.
[34] WANG Y，TAO F，ZUO Y，et al. Digital-twin-enhanced quality prediction for the composite materials[J]. Engineering，2023，22：23-33.
[35] XUE R，ZHOU X，HUANG Z，et al. Digital twin-driven CNC spindle performance assessment[J]. International Journal of Advanced Manufacturing Technology，2022，119(3-4)：1821-1833.
[36] YUAN Y，FAN K. Digital twin-driven life health monitoring for motorized spindle[J]. Journal of Manufacturing Processes，2024，113：373-387.
[37] ZEYNIVAND M，ESMAILI P，CRISTALDI L，et al. Modern digital twin for validation and generation of datasets for machine tool spindle modeling[C]//2024 International Symposium on Power Electronics，Electrical Drives，Automation and Motion (SPEEDAM). IEEE，2024：430-435.
[38] FAN K，LIU J. Digital twin-driven lifecycle management for motorized spindle[J]. The International Journal of Advanced Manufacturing Technology，2024，135(1)：443-455.
[39] RUDEL V，KIENAST P，VINOGRADOV G，et al. Cloud-based process design in a digital twin framework with integrated and coupled technology models for blisk milling[J]. Frontiers in Manufacturing Technology，2022，2：1021029.
[40] SUN X，ZHANG F，NIU X，et al. A digital twin commissioning method for machine tools based on scenario simulation[J]. Journal of Manufacturing Systems，2024，77：697-707.
[41] BANI-HANI M，HANENKAMP N. Enhancing energy efficiency in machining through digital twin technology：Predictive modeling of thermal loads in machine tool spindles[J]. Procedia CIRP，2025，133：662-667.
[42] LU Q，ZHU D，WANG M，et al. Digital twin-driven thermal error prediction for CNC machine tool spindle[J]. Lubricants，2023，11：219.
[43] ZHENG Y，YANG H，JIANG G，et al. Data-mechanism fusion modeling and compensation for the spindle thermal error of machining center based on digital twin[J]. Measurement，2025，250：117152.
[44] SA G，JIANG Z，LIU Z，et al. An integrated optimization method for measurement points layout and error modeling for digital twin of CNC machine tools[J]. Precision Engineering，2024，90：1-11.
[45] LIU J，MA C，YUAN Q. Spindle unit thermal error modeling and compensation based on digital twin[J]. The International Journal of Advanced Manufacturing Technology，2024，132(3)：1525-1555.
[46] MA S，LENG J，ZHENG P，et al. A digital twin-assisted deep transfer learning method towards intelligent thermal error modeling of electric spindles[J]. Journal of Intelligent Manufacturing，2025，36(3)：1659-1688.
[47] 马帅，冷杰武，陈祝云，等. 基于数字孪生和深度迁移学习的电主轴热误差建模方法[J]. 机械工程学报，2025，61(3)：52-66. MA Shuai，LENG Jiewu，CHEN Zhuyun，et al. Modeling method for thermal error of electric spindles based on digital twin and deep transfer learning[J]. Transactions of the Chinese Society for Mechanical Engineering，2025，61(3)：52-66.
[48] YANG Y，LV J，XIAO Y，et al. Enhanced modeling method of thermal behaviors in machine tool motorized spindles based on the mixture of thermal mechanism and machine learning[J]. Journal of Intelligent Manufacturing，2025，36(1)：221-242.
[49] TAN F，CHEN H，PENG J，et al. Efficient boundary conditions identification in thermal simulation of the spindle system with reduced order model and differential evolution algorithm[J]. Case Studies in Thermal Engineering，2024，59：104526.
[50] FUHRLÄNDER-VÖLKER D，LINDNER M，Elling M，et al. Method for the development and application of digital twins in manufacturing[J]. Production Engineering，2025：1-15.
[51] MA C，GUI H，LIU J. Self learning-empowered thermal error control method of precision machine tools based on digital twin[J]. Journal of Intelligent Manufacturing，2023，34：695-717.
[52] 尹佳，白乐乐，田辉，等. 基于模糊神经网络的机床进给速度数字孪生调控方法[J]. 哈尔滨理工大学学报，2025，30(1)：108-118. YIN Jia，BAI Lele，TIAN Hui，et al. A digital twin control method for machine tool feed rate based on fuzzy neural networks[J]. Journal of Harbin University of Science & Technology，2025，30(1)：108-118.
[53] 黄华，李嘉然，赵秋舸，等. 基于混合驱动的进给系统数字孪生模型自适应更新法[J]. 计算机集成制造系统，2023，29(6)：1840-1851. HUANG Hua，LI Jiaran，ZHAO Qiuguo，et al. Adaptive update method for digital twin models of hybrid-driven feed systems[J]. Computer-Integrated Manufacturing Systems，2023，29(6)：1840-1851.
[54] SIM B，LEE W. Digital twin based machining condition optimization for CNC machining center[J]. International Journal of Precision Engineering and Manufacturing-Smart Technology，2023，1(2)：115-123.
[55] ZHANG X，XIE X. A data model-driven prediction and compensation strategy for load disturbances in CNC machine tool feeding systems via digital twin technology[J]. Proceedings of the Institution of Mechanical Engineers，Part C：Journal of Mechanical Engineering Science，2025：09544062241309251.
[56] LI M，MA C，LIU J，et al. Digital twin driven thermal error control of linear axis for face gear grinding machine tool[J]. Procedia CIRP，2025，133：436-441.
[57] ZHOU Z，WALTHER M，Verl A. A digital twin of a ball screw drive under different conditions：A case study about temperature drift[C]//2024 IEEE International Conference on Industrial Technology (ICIT). IEEE，2024：1-6.
[58] GUPTA H，KUNDU P. Digital twin-driven condition monitoring：Development for ball screw feed drive systems[C]//2024 Prognostics and System Health Management Conference (PHM). 2024：380-386.
[59] 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，2022，18：109-118.
[60] WU Z，MA C，ZHANG L，et al. Predicting and compensating for small-sample thermal information data in precision machine tools：A spatial-temporal interactive integration network and digital twin system approach[J]. Applied Soft Computing，2024，161：111760.
[61] FUJITA T，XI T，IKEDA R，et al. Identification of a practical digital twin for simulation of machine tools[J]. International Journal of Automation Technology，2022，16(3)：261-268.
[62] LIU J，MA C，GUI H，et al. Intelligent digital-twin prediction and reverse control system architecture for thermal errors enabled by deep learning and cloud-edge computing[J]. Expert Systems with Applications，2023，225：120122.
[63] JI S，NI H，HU T，et al. DT-CEPA：A digital twin-driven contour error prediction approach for machine tools based on hybrid modeling and sparse time series[J]. Robotics and Computer-Integrated Manufacturing，2024，88：102738.
[64] MCPHERSON J，MEHRABI M，AHMADI K. In-process identification of feed drive dynamics considering machining forces[J]. Precision Engineering，2025，95：468-483.
[65] KOMBAROV V，FOJTU P，SULITKA M，et al. Prediction and compensation of motion differential characteristics influence on position error in CNC machine tools[J]. The International Journal of Advanced Manufacturing Technology，2025，137：5951–598.
[66] 徐将铭，孙小敏，沈敏，等. 基于西门子ONE系统的数控蜗轮滚齿软件开发[J]. 机床与液压，2025，53(6)：76-81. XU Jiangming，SUN Xiaomin，SHEN Min，et al. Development of CNC worm gear hobbing software based on the siemens ONE system[J]. Machine Tool & Hydraulics，2025，53(6)：76-81
[67] PETRU C，MORARIU F，BRRAZ R，et al. Development of a small CNC machining center for physical implementation and a digital twin[J]. Applied Sciences，2025，15(10)：5549.
[68] YU H，YU D，WANG C，et al. Edge intelligence-driven digital twin of CNC system：Architecture and deployment[J]. Robotics and Computer-Integrated Manufacturing，2023，79：102418.
[69] 卓越，刘建康，富宏亚，等. 基于微服务架构的智能数控系统[J]. 航空制造技术，2020，63(23/24)：56-60. ZHUO yue，LIU Jiankang，FU Hongya，et al. Intelligent CNC system based on microservices architecture[J]. Aviation Manufacturing Technology，2020，63(23/24)：56-60.
[70] 武鑫磊，刘永红，亓梁，等. 电火花辅助电弧高效铣削技术智能化数控系统设计[J]. 电加工与模具，2022(6)：21-24. WU Xinlei，LIU Yonghong，QI Liang，et al. Design of an intelligent CNC system for high-efficiency milling technology using electro-discharge machining-assisted arc[J]. Electro-Processing and Mould，2022(6)：21-24.
[71] ZHANG H，QI Q，JI W，et al. An update method for digital twin multi-dimension models[J]. Robotics and Computer-Integrated Manufacturing，2023，80：102481.
[72] ZHOU T，ZHANG M，HU T，et al. Research on the online monitoring method of cutting tool wear based on the mechanism-data fusion concept of digital twin[J]. Journal of Manufacturing Processes，2025，148：386-407.
[73] WU L，SHA K，TAO Y，et al. A hybrid deep learning model as the digital twin of ultra-precision diamond cutting for in-process prediction of cutting-tool wear[J]. Applied Sciences，2023，13(11)：6675.
[74] QIAO Q，WANG J，YE L，et al. Digital twin for machining tool condition prediction[J]. Procedia CIRP，2019，81：1388-1393.
[75] ZHUANG K，SHI Z，SUN Y，et al. Digital twin-driven tool wear monitoring and predicting method for the turning process[J]. Symmetry，2021，13(8)：1438.
[76] STRÖBEL R，BOTT A，WORTMANN A，et al. Monitoring of tool and component wear for self-adaptive digital twins：A multi-stage approach through anomaly detection and wear cycle analysis[J]. Machines，2023，11(11)：1032.
[77] LIU Z，LANG Z，GUI Y，et al. Digital twin-based anomaly detection for real-time tool condition monitoring in machining[J]. Journal of Manufacturing Systems，2024，75：163-173.
[78] SUN J，WANG D，LIU Z，et al. Tool digital twin based on knowledge embedding for precision CNC machine tools：Wear prediction for collaborative multi-tool[J]. Journal of Manufacturing Systems，2025，80：157-175.
[79] CHRISTIAND，KISWANTO G，BASKORO A，et al. Digital twin of micro-milling process for micro-tool wear[J]. International Journal of Computer Integrated Manufacturing，2025：1-26.
[80] 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 Congbo，SUN Xin，HOU Xiaobo，et al. On-line monitoring method of CNC milling tool wear driven by digital twins[J]. China Mechanical Engineering，2022，33(1)：78-87.
[81] CHRISTIAND，KISWANTO G，BASKORO A. The digital twin application for micro-tool wear monitoring with open-source CAD system[C]//KIM D Y，VON CIEMINSKI G，ROMERO D. Advances in Production Management Systems. Smart Manufacturing and Logistics Systems：Turning Ideas into Action. Cham：Springer Nature Switzerland，2022：11-18.
[82] DOMINGUEZ-CABALLERO J，AYVAR-SOBERANIS S，CURTIS D. Intelligent real-time tool life prediction for a digital twin framework[J]. Journal of Intelligent Manufacturing，2025：1-21.
[83] GUO M，FANG X，HU Z，et al. Design and research of digital twin machine tool simulation and monitoring system[J]. The International Journal of Advanced Manufacturing Technology，2023，124(11)：4253-4268.
[84] ZI X，GAO S，XIE Y. An online monitoring method of milling cutter wear condition driven by digital twin[J]. Scientific Reports，2024，14(1)：4956.
[85] ZHANG J，LI C，DENG C，et al. Toward digital twins for intelligence manufacturing：Self-adaptive control in assisted equipment through multi-sensor fusion smart tool real-time machine condition monitoring[J]. Journal of Manufacturing Systems，2025，82：301-318.
[86] LUO W，HU T，YE Y，et al. A hybrid predictive maintenance approach for CNC machine tool driven by digital twin[J]. Robotics and Computer-Integrated Manufacturing，2020，65：101974.
[87] CHEN J，AN Q，CHEN M，et al. Digital twin dynamic force-thermal physics sub-cell for CFRP drilling process[J]. Composites Part B：Engineering，2025，292：112057.
[88] 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 Huibin，PAN Junlin，ZHANG Jiduo，et al. Tool digital twin model for cutting processes[J]. Computer-Integrated Manufacturing Systems，2019，25(6)：1474-1480.
[89] 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 Minghao，YUE Caixu，XIA Wei，et al. Real-time monitoring of milling cutter status based on digital twin technology[J]. Computer-Integrated Manufacturing Systems，2023，29(6)：2118-2129.
[90] KURTH R，ALALUSS M，WAGNER M，et al. Process monitoring using digital twin-based sensors integrated in tool clamping surfaces[C]//IOP Conference Series：Materials Science and Engineering. IOP Publishing，2023，1284(1)：012016.
[91] BAKHSHANDEH P，MOHAMMADI Y，Altintas Y，et al. Digital twin assisted intelligent machining process monitoring and control[J]. CIRP Journal of Manufacturing Science and Technology，2024，49：180-190.
[92] ZHANG L，LIU J，WU X，et al. Digital twin-based dynamic prediction of thermomechanical coupling for skiving process[J]. The International Journal of Advanced Manufacturing Technology，2024，131(11)：5471-5488.
[93] HEO E，YOO N. Numerical control machine optimization technologies through analysis of machining history data using digital twin[J]. Applied Sciences，2021，11(7)：3259.
[94] LU Y，YUE C，LIU X，et al. Research on digital twin monitoring system during milling of large parts[J]. Journal of Manufacturing Systems，2024，77：834-847.
[95] SUNDARAM S，PUTHENVEETIL F，NAIR V S，et al. The influence of material stiffness and damping on machining stability in boring tool–workpiece systems using finite element simulation to implement digital twin[J]. International Journal on Interactive Design and Manufacturing，2024，18(9)：6765-6784.
[96] LI Z，SONG Q，GONG J，et al. Self-sensing sliding mode control of workpiece chatter based on accurate prediction of machining vibration[J]. Journal of Sound and Vibration，2025，600：118887.
[97] TONG X，LIU Q，ZHOU Y，et al. A digital twin-driven cutting force adaptive control approach for milling process[J]. Journal of Intelligent Manufacturing，2025，36(1)：551-568.
[98] KIM S，IM C，LEE Y，et al. Machine history based data learning in CNC machining[C]//International Conference on Flexible Automation and Intelligent Manufacturing. Cham：Springer Nature Switzerland，2024：92-100.
[99] 李奕霖，左颖，张永平，等. 面向服役性能的数控机床数字孪生模型构建[J]. 哈尔滨理工大学学报，2025，30(1)：12-22. LI Yilin，ZUO Ying，ZHANG Yongping，et al. Construction of digital twin models for CNC machine tools focusing on service performance[J]. Journal of Harbin University of Science and Technology，2025，30(1)：12-22.
[100] MEI S，XIE Y，LIU J，et al. Physics-based modeling and intelligent optimal decision method for digital twin system towards sustainable CNC equipment[J]. Robotics and Computer-Integrated Manufacturing，2025，95：103028.
[101] HUANG Z，FEY M，LIU C，et al. Hybrid learning-based digital twin for manufacturing process：Modeling framework and implementation[J]. Robotics and Computer-Integrated Manufacturing，2023，82：102545.
[102] SA G，SUN J，HOU M，et al. A digital twin synchronous evolution method of CNC machine tools associated with dynamic and static errors[J]. The International Journal of Advanced Manufacturing Technology，2024，134(5)：2753-2763.
[103] IRINO N，KOBAYASHI A，SHINBA Y，et al. Digital twin based accuracy compensation[J]. CIRP Annals，2023，72(1)：345-348.
[104] PANTAZIS D，GOODALL P，PEASE S，et al. Predicting electrical power consumption of end milling using a virtual machining energy toolkit[J]. Computers in Industry，2023，150：103943.
[105] 杜柳青，吕发良，余永维. 数字孪生下基于DACS-MFAC的数控机床热误差自适应预测方法[J]. 仪器仪表学报，2024，45(4)：248-257. DU Liuqing，LÜ Falang，YU Yongwei. Adaptive prediction method for thermal errors in CNC machine tools based on DACS-MFAC under digital Twin[J]. Chinese Journal of Instrument and Measurement，2024，45(4)：248-257.
[106] PLAKHOTNIK D，CURUTIU A，ZHULAVSKYI A Y，et al. Framework for coupled digital twins in digital machining[J]. MM Science Journal，2021(5)：5093-5097.
[107] SULITKA M，KOLÁŘ P，ŠVÊDA J，et al. Strategy for implementating predictive process-oriented machine tool digital twins[J]. MM Science Journal，2022，28(10)：2022121
[108] LI H，YAN P，ZHOU H，et al. A multi-scenario model fusion and verification method for digital twin machine tool[J]. Journal of Industrial Information Integration，2025，46：100859.
[109] SUN G，FAN K，YANG J. Digital-driven in-situ monitoring for thermally-induced volumetric errors of CNC machine tools[J]. Journal of Manufacturing Processes，2024，131：2000-2015.
[110] MAZURKIEWICZ D，SOBECKI P，ŻABIŃSKI T，et al. Virtual tomography as a novel method for segmenting machining process phases with the use of machine learning-supported measurement[J]. Expert Systems with Applications，2024，250：123945.
[111] ANBALAGAN A，ZANHAR W，GEORGE S，et al. Lightweight CNC digital process twin framework：IIoT integration with open62541 OPC UA protocol[J]. Production & Manufacturing Research，2025，13(1)：2544981.
[112] SICARD B，WU Y，GADSDEN S. Digital twin enabled asset management of machine tools[C]//2024 IEEE International Conference on Prognostics and Health Management. IEEE，2024：285-292.
[113] LENG J，LIU Q，YE S，et al. Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model[J]. Robotics and Computer-Integrated Manufacturing，2020，63：101895.
[114] ZHANG L，MA C，LIU J. Enhancing four-axis machining center accuracy through interactive fusion of spatiotemporal graph convolutional networks and an error-controlled digital twin system[J]. Journal of Manufacturing Processes，2024，112：14-31.
[115] PANTELIDAKIS M，MYKONIATIS K. Extending the digital twin ecosystem：A real-time digital twin of a LinuxCNC-controlled subtractive manufacturing machine[J]. Journal of Manufacturing Systems，2024，74：1057-1066.
[116] TAO F，ZHANG M，LIU Y，et al. Digital twin driven prognostics and health management for complex equipment[J]. CIRP Annals. 2018，67：169-172.
[117] SUN Z，LIU H，YAN B，et al. CNC machine tool operation status monitoring system based on digital twins[C]//2025 International Conference on Electrical Automation and Artificial Intelligence. IEEE，2025：381-385.
[118] XUE R，ZHANG P，HUANG Z，et al. Digital twin-driven fault diagnosis for CNC machine tool[J]. The International Journal of Advanced Manufacturing Technology，2024，131：5457-5470.
[119] FENG Y，FAN K. Digital twin-driven causal diagnosis mechanism for life health of high-speed spindle system[J]. The International Journal of Advanced Manufacturing Technology，2024，134(3)：1077-1089.
[120] GUO J，YANG Z，CHEN C，et al. Real-time prediction of remaining useful life and preventive maintenance strategy based on digital twin[J]. Journal of Computing and Information Science in Engineering，2021，21(3): 031003.
[121] DONG Y，LI Y. Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning[J]. Diagnostyka，2024，25(4)：1-12.
[122] 郭伟超，王景琪，张松伟，等. 数字孪生与迁移学习结合的工作台进给系统故障诊断方法[J]. 哈尔滨理工大学学报，2025，30(1)：138-147. GUO Weichao，WANG Jingqi，ZHANG Songwei，et al. A workstation feed system fault diagnosis method for digital twin and transfer learning integration[J]. Journal of Harbin University of Science and Technology，2025，30(1)：138-147.
[123] CAI Y，STARLY B，COHEN P，et al. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing[J]. Procedia Manufacturing，2017，10：1031-1042.
[124] LIU J，YU D，HU Y，et al. CNC machine tool fault diagnosis integrated rescheduling approach supported by digital twin-driven interaction and cooperation framework[J]. IEEE Access，2021，9：118801-118814.
[125] LV J，LI X，SUN Y，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.
[126] 肖铁忠，袁秀坤，冯锦春，等. 面向航空叶片加工的高档数控机床研制[J]. 制造技术与机床，2025(10)：121-126. XIAO Tiezhong，YUAN Xiukun，FENG Jinchun，et al. Development of high-end CNC machine tools for aviation blade processing[J]. Manufacturing Technology and Machine Tools，2025(10)：121-126.
[127] 赵彤，卞鹏锡，王永飞，等. 机匣类工件五轴铣削工艺系统的刚度建模技术[J]. 清华大学学报，2024，64(8)：1435-1444. ZHAO Tong，BIAN Pengxi，WANG Yongfei，et al. Stiffness modeling technology for five-axis milling systems in machine housing workpieces[J]. Journal of Tsinghua University，2024，64(8)：1435-1444.
[128] 宋碧芸. 螺旋锥齿轮全工序法磨齿加工数字孪生建模研究[D]. 长沙：中南大学，2022. SONG Biyun. Digital twin modeling research for duplex helical method grinding of aviation spiral bevel gear[D]. Changsha：Central South University，2022.
[129] 贺茂聪. 球头刀非均匀几何磨损在机测量与在线补偿[D]. 西安：西安工业大学，2025. HE Maocong. Measurement and online compensation of non-uniform geometric wear in the head of a striker[D]. Xi 'an：Xi' an Technological University，2025.
[130] 路平，陈洪军. 高精度航空薄壁件加工技术体系：从材料特性到机床装备的迭代升级[J]. 世界制造技术与装备市场，2025(2)：74-81. LU Ping，CHEN Hongjun. High-precision aviation thin-walled component machining technology system：From material characteristics to machine tool equipment evolution[J]. World Manufacturing Technology & Equipment Market，2025(2)：74-81.
[131] CLARK B，SCHOOP J，POONAWALA H. Digital process twins：A modular approach for surface conditioning and process optimization[J]. Production Engineering，2024，18(2)：367-380.
[132] ZHAO X，ZHENG L，WANG Y，et al. Services-oriented intelligent milling for thin-walled parts based on time-varying information model of machining system[J]. International Journal of Mechanical Sciences，2022，219：107125.
[133] WARD R，SUN C，DOMINGUEZ-CABALLERO J，et al. Machining digital twin using real-time model-based simulations and lookahead function for closed loop machining control[J]. The International Journal of Advanced Manufacturing Technology，2021，117(11-12)：3615-3629.
[134] 刘蔚然，陶飞，程江峰，等. 数字孪生卫星：概念、关键技术及应用[J]. 计算机集成制造系统，2020，26(3)：565-588. LIU Weiran，TAO Fei，CHENG Jiangfeng，et al. Digital twin satellite：Concept，key technologies and applications[J]. Computer Integrated Manufacturing Systems，2020，26(3)：565-588.
[135] 刘冬. 运载火箭箭体制造关键装备与技术现状及发展[J]. 制造技术与机床，2023(3)：85-93. Liu Dong. Current status and development of key equipment and technologies for launch vehicle body manufacturing[J]. Manufacturing Technology & Machine Tools，2023(3)：85-93.
[136] 杨凯，刘汉良，胡兴平，等. 薄壁壳体结构狭小空间组合加工技术[J]. 制造技术与机床，2022(7)：34-39. YANG Kai，LIU Hanliang，HU Xingping，et al. Thin-walled shell structure compact space assembly machining technology[J]. Manufacturing Technology and Machine Tools，2022(7)：34-39.
[137] LI Z，ZENG Z，YANG Y，et al. Research progress in machining technology of aerospace thin-walled components[J]. Journal of Manufacturing Processes，2024，119：463-482.
[138] LI X，NI J，LIU X，et al. Chatter-free milling of aerospace thin-walled parts[J]. Journal of Materials Processing Technology，2025，341：118903.
[139] BOMBIŃSKI S，KOSSAKOWSKA J，NEJMAN M，et al. Needs，requirements and a concept of a tool condition monitoring system for the aerospace industry[J]. Sensors，2021，21(15)：5086.
[140] 沈慧. 铣削加工数字孪生模型的迁移更新方法与应用研究[D]. 上海：东华大学，2023. SHEN Hui. Research on migration and update methods of digital twin models for machining and grinding[D]. Shanghai：Donghua University，2023.
[141] 卢山雨. 基于AR的航天结构件加工虚实模型融合与交互技术[D]. 上海：东华大学，2022. LU Shanyu. AR-based integration and interaction technology of virtual-real modeling for aerospace structural components[D]. Shanghai：Donghua University，2022.
[142] 霍洪旭，康广毅，战福操. 船用柴油机大型曲轴主轴颈加工专用机床设计与研究[J]. 制造技术与机床，2025(4)：135-141. HUO Hongxu，KANG Guangyi，ZHAN Fucao. Design and research of：1 specialized machine tools for processing main journal of large diesel engine cranks in marine applications[J]. Manufacturing Technology & Machine Tools，2025(4)35-141.
[143] 赵建林. 大型船用螺旋桨四轴曲面数控加工技术[J]. 舰船科学技术，2022，44(21)：182-185. ZHAO Jianlin. Large-scale ship propeller four-axis curved surface CNC machining technology[J]. Ship Science and Technology，2022，44(21)：182-185.
[144] LIU J，WEN X，ZHOU H，et al. Digital twin-enabled machining process modeling[J]. Advanced Engineering Informatics，2022，54：101737.
[145] 唐建福. 用于大型龙门五轴机床静特性与动特性测试的加载装置设计与实现[D]. 成都：电子科技大学，2025. TANG Jianfu. Design and implementation of a loading device for static and dynamic characteristic testing of large gantry five-axis machine tools[D]. Chengdu：University of Electronic Science and Technology of China，2025.
[146] 卓越. 薄壁件铣削颤振辨识及表面形貌预测研究[D].哈尔滨：哈尔滨工业大学，2025. ZHUO Yue. Research on vibration identification and surface morphology prediction of thin wall components milling[D]. Harbin：Harbin Institute of Technology，2025.
[147] ZHOU Y，XING T，SONG Y，et al. Digital-twin-driven geometric optimization of centrifugal impeller with free-form blades for five-axis flank milling[J]. Journal of Manufacturing Systems，2021，58：22-35.
[148] CHENG D，ZHANG J，HU Z T，et al. A digital twin-driven approach for on-line controlling quality of marine diesel engine critical parts[J]. International Journal of Precision Engineering and Manufacturing，2020，21(10)：1821-1841.
[149] 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 Xifeng，ZHANG Jie，CHENG Dejun，et al. Quality control method for key components in marine diesel engine processing driven by digital twin[J]. Mechanical Design and Manufacturing，2023(3)：46-52.
[150] 李欣星，肖铁忠，黄娟. 汽车发动机缸体多轴加工专用机床设计[J]. 制造技术与机床，2018(10)：152-155. LI Xinxing，XIAO Tiezhong，Huang Juan. Design of multi-axis machining machine tools for automobile engine blocks[J]. Manufacturing Technology & Machine Tools，2018(10)：152-155.
[151] 通用集团沈阳机床. 通用技术集团沈阳机床挺进乘用车高端市场[J]. 世界制造技术与装备市场，2020(2)：44. Genertec Shenyang Machine Tool Co.，Ltd. Genertec Shenyang Machine Tool enters the high-end passenger vehicle market[J]. World Manufacturing Technology and Equipment Market，2020(2)：44.
[152] 李松. 数控机床在汽车底盘制造中的应用[J]. 汽车测试报告，2024(20)：74-76. LI Song. Application of CNC machine tools in automotive chassis manufacturing[J]. Automotive Testing Report，2024(20)：74-76.
[153] 徐超. 数字孪生技术在新能源汽车一体压铸生产中的创新应用[J]. 金属加工，2024(8)：8-12. XU Chao. Innovative application of digital twin technology in integrated die-casting production for new energy vehicles[J]. Metal Processing，2024(8)：8-12.
[154] 韩笑. 再制造发动机缸孔珩磨自动化控制与实时监测技术研究[D]. 上海：上海电机学院，2024. HAN Xiao. Research on automation control and real- time monitoring technology of engine cylinder hone machining in remanufacturing[D]. Shanghai：Shanghai Dianji University，2024.
[155] 夏弋涵. 基于数字孪生的曲轴内铣监测系统构建与研究[D]. 十堰：湖北汽车工业学院，2025. XIA Yihan. Construction and research of a digital twin-based monitoring system for internal milling of crankshafts[D]. Shiyan：Hubei University of Automotive Technology，2025.
[156] ARMENDIA M，CUGNON F，BERGLIND L，et al. Evaluation of machine tool digital twin for machining operations in industrial environment[J]. Procedia CIRP，2019，82：231-236.
[157] 王小东，张云，陈志同，等. 涡轮叶片榫头侧边圆角阵列加工工艺研究[J]. 航空制造技术，2020，63(7)：92-95，101. WANG Xiaodong，ZHANG Yun，CHEN Zhitong，et al. Study on the processing technology of side corner