高端数控机床数智孪生工程

doi:10.3901/JME.260473

机械工程学报 ›› 2026, Vol. 62 ›› Issue (13): 241-269.doi: 10.3901/JME.260473

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高端数控机床数智孪生工程

陶飞^1,2, 安奕达², 李奕霖², 兰文锴², 张辰源¹, 何珏², 张永平², 左颖², 宋鲁凯³, 薛瑞娟⁴, 黄祖广⁴, 胡天亮⁵, 姬帅⁵, 孙铮⁶, 刘晓军⁷, 杨兴凯⁸, 任莹晖⁸, 冯凯⁸

1. 北京航空航天大学国际前沿交叉科学研究院数字孪生国际研究中心北京 100191;
2. 北京航空航天大学自动化科学与电气工程学院北京 100191;
3. 北京科技大学机械工程学院北京 100083;
4. 通用技术集团机床工程研究院有限公司北京 100102;
5. 山东大学机械工程学院济南 250002;
6. 西安交通大学精密微纳制造技术全国重点实验室西安 710049;
7. 东南大学机械工程学院南京 210096;
8. 湖南大学国家高效磨削工程技术研究中心长沙 410082

收稿日期:2025-10-28 修回日期:2026-03-24 发布日期:2026-08-28
作者简介:陶飞(通信作者)，男，1981年出生，博士，教授，博士研究生导师。主要研究方向为数字孪生与数智工程、智能制造、制造工业软件等。E-mail：ftao@buaa.edu.cn;安奕达，男，2002年出生，博士研究生。主要研究方向为数字孪生、数字孪生机床等。E-mail：anyida@buaa.edu.cn;李奕霖，男，1996年出生，博士研究生。主要研究方向为数字孪生机床、数控机床的服役性能建模等;兰文锴，男，2002年出生，博士研究生。主要研究方向为数字孪生、数字孪生机床等;张辰源，男，1995年出生，博士研究生。主要研究方向为数字孪生、数字孪生车间等;何珏，男，2003年出生，博士研究生。主要研究方向为数字孪生、智能制造等;张永平，女，1989年出生，博士。主要研究方向为数字孪生、智造服务协作优化等;左颖，男，1986年出生，博士，硕士研究生导师。主要研究方向为数字孪生、绿色可持续制造等;宋鲁凯，男，1990年出生，博士，副教授，硕士研究生导师。主要研究方向为物理智能、可靠性数字孪生;薛瑞娟，女，1987年出生，博士，正高级工程师。主要研究方向为工业母机、智能制造、数字孪生测试与标准技术等;黄祖广，男，1974年出生，博士，教授级高级工程师，博士研究生导师。主要研究方向为工业母机、智能制造共性技术及标准研究等;胡天亮，男，1981年出生，博士，教授，博士研究生导师，主要研究方向为智能制造、数字孪生技术等;姬帅，男，1987年出生，博士，研究员，博士研究生导师。主要研究方向为机床数控技术，机器人运动控制等;孙铮，男，1981年出生，博士，副研究员，硕士研究生导师。主要研究方向为数控系统核心算法、高精密直线电机的设计与控制等;刘晓军，男，1980年出生，博士，教授，博士研究生导师。主要研究方向为数字化设计、制造与先进制造技术等;杨兴凯，男，1992年出生，博士，助理教授，硕士研究生导师。主要研究方向为机械装备智能设计与运维、数字孪生等;任莹晖，女，1979年出生，博士，教授，博士研究生导师。主要研究方向为难加工材料精密超精密加工技术与装备开发等;冯凯，男，1982年出生，博士，教授，博士研究生导师。主要研究方向为超高速超精密关键部件与装备开发等。
基金资助:
国家自然科学基金(U24B2064)、北京高等学校卓越青年科学家计划和科学探索奖资助项目。

Smart Digital Twin Engineering for Advanced CNC Machine Tools

TAO Fei^1,2, AN Yida², LI Yilin², LAN Wenkai², ZHANG Chenyuan¹, HE Jue², ZHANG Yongping², ZUO Ying², SONG Lukai³, XUE Ruijuan⁴, HUANG Zuguang⁴, HU Tianliang⁵, JI Shuai⁵, SUN Zheng⁶, LIU Xiaojun⁷, YANG Xingkai⁸, REN Yinghui⁸, FENG Kai⁸

1. Digital Twin International Research Center, International Institute for Interdisciplinary and Frontiers, Beihang University, Beijing 100191;
2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191;
3. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083;
4. Genertec Machine Tool Engineering Research Institute Co., Ltd., Beijing 100102;
5. School of Mechanical Engineering, Shandong University, Jinan 250002;
6. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049;
7. School of Mechanical Engineering, Southeast University, Nanjing 210096;
8. National Engineering Research Center for High Efficiency Grinding, Hunan University, Changsha 410082

Received:2025-10-28 Revised:2026-03-24 Published:2026-08-28

摘要/Abstract

摘要： 高端数控机床是制造业“工业母机”，面向新一代智能制造发展需求，亟需突破高性能智能机床关键技术，全面增强高端数控机床研发制造、高效运行与智能维护能力。为此，基于作者团队前期提出的数字孪生五维模型和数字孪生“理论-技术-工具-标准”体系，首先：① 分析并提出了高端数控机床数智化未来发展的16个需求；② 系统性回顾了数字孪生在高端数控机床全生命周期中的研用现状，以及数字孪生赋能的高端数控机床在“三航一车一机”典型场景中的研用现状，从数字化、智能化、应用、标准、软件等方面总结了现有工作的局限与不足；③ 研究提出了高端数控机床数智孪生工程的体系架构，包括生命周期工程、模型工程、数据工程、智能工程、软件工程、数实交互融合工程；④ 提出了高端数控机床数智孪生工程的八类关键技术，包括智能生成式设计与正向设计技术、高性能智造技术、数实融合试验测试验证技术、智能运维技术、全生命周期模型构建技术、全生命周期数据融合技术、专用软件平台开发技术、基础支撑技术；⑤ 结合作者团队前期在数字孪生和数字工程等方面的实践工作与经验，针对高端数控机床数智孪生工程的发展与实施提出了若干思考与建议。期望该工作为高端数控机床数智化升级提供参考，助力我国从制造大国成长为智造强国。

关键词: 高端数控机床, 数智孪生工程, 数字孪生, 数字工程, 人工智能, 数智孪生

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

中图分类号:

I253

陶飞, 安奕达, 李奕霖, 兰文锴, 张辰源, 何珏, 张永平, 左颖, 宋鲁凯, 薛瑞娟, 黄祖广, 胡天亮, 姬帅, 孙铮, 刘晓军, 杨兴凯, 任莹晖, 冯凯. 高端数控机床数智孪生工程[J]. 机械工程学报, 2026, 62(13): 241-269.

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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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260473

http://www.cjmenet.com.cn/CN/Y2026/V62/I13/241

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高端数控机床数智孪生工程

Smart Digital Twin Engineering for Advanced CNC Machine Tools

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