数控系统数字孪生体系结构及应用研究

doi:10.16182/j.issn1004731x.joss.23-1004

摘要/Abstract

摘要：

针对生产制造领域中对于数控系统虚拟调试、性能评估和加工质量优化的智能化、数字化需求，基于数字孪生技术构建了虚实结合的数控系统五维数字孪生体系。并结合新一代信息技术，对该数字孪生体系进行了多领域建模，包括信息模型、机理模型和数字线程等，以实现对物理实体和加工过程较为全面的模拟与分析。研究还验证了OPC UA数据传输架构在数控系统与数字孪生应用之间的数据传输可行性，确保数控系统虚拟加工过程信息交换的可靠性和高效性。在此基础上开发了数控加工性能评价孪生系统和数控加工轨迹优化孪生应用，将数字孪生技术与数控系统相结合，实现了数控加工过程的虚拟化模拟以及加工性能评价和工艺优化，证明了所提出的数控系统数字孪生体系的合理性和适用性，从而提升数控系统的智能化、数字化水平。

关键词: 数字孪生, 数控加工, 虚实结合, OPC UA, 轨迹数字孪生

Abstract:

In response to the intelligent and digital requirements for virtual debugging, performance evaluation, and machining quality optimization of CNC systems in the field of production and manufacturing, a five dimensional digital twin system of CNC systems combining virtual and real is constructed based on digital twin technology. And combined with relevant new generation information technology, the digital twin system is modeled in multiple fields, including information models, mechanism models, and digital threads, to achieve comprehensive simulation and analysis of physical entities and processing processes. The study also verifies the feasibility of data transmission between CNC systems and digital twin applications using the OPC UA data transmission architecture, ensuring the reliability and efficiency of information exchange during virtual machining processes in CNC systems. Specifically, based on this, a twin system for evaluating CNC machining performance and a twin application for optimizing CNC machining trajectory have been developed. By combining digital twin technology with CNC systems, virtual simulation of CNC machining processes, machining performance evaluation, and process optimization have been achieved, proving the rationality and applicability of the proposed digital twin system for CNC systems, thereby improving the intelligence and digitization level of CNC systems.

Key words: digital twin, CNC machining, combination of virtualness and reality, OPC UA, trajectory digital twin

中图分类号:

TP391.9

张曦阳,林旭升,周瑞等 . 数控系统数字孪生体系结构及应用研究[J]. 系统仿真学报, 2025, 37(1): 183-198.

Zhang Xiyang,Lin Xusheng,Zhou Rui,et al . Research on the Digital Twin Architecture and Application of CNC System[J]. Journal of System Simulation, 2025, 37(1): 183-198.

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参考文献 24

1	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.
2	Schleich Benjamin, Anwer Nabil, Luc Mathieu, et al. Shaping the Digital Twin for Design and Production Engineering[J]. CIRP Annals, 2017, 66(1): 141-144.
3	王金江, 牛晓彤, 黄祖广, 等. 数字孪生驱动的数控机床虚拟调试技术研究[J]. 制造技术与机床, 2022(10): 127-132.
	Wang Jinjiang, Niu Xiaotong, Huang Zuguang, et al. Digital Twin-driven CNC Machine Tool Virtual Commissioning Technology Study[J]. Manufacturing Technology & Machine Tool, 2022(10): 127-132.
4	Tao Fei, Zhang Meng, Liu Yushan, et al. Digital Twin Driven Prognostics and Health Management for Complex Equipment[J]. CIRP Annals, 2018, 67(1): 169-172.
5	陆新时, 马嵩华, 胡天亮. 基于数字孪生的力能控制式压力机虚拟调试[J]. 小型微型计算机系统, 2022, 43(7): 1356-1361.
	Lu Xinshi, Ma Songhua, Hu Tianliang. Virtual Commissioning of Force-power Controlled Press Machine Based on Digital Twin[J]. Journal of Chinese Computer Systems, 2022, 43(7): 1356-1361.
6	国家市场监督管理总局, 国家标准化管理委员会. 数控装备互联互通及互操作第3部分: 面向实现的模型映射: [S]. 北京: 中国标准出版社, 2020.
	State Administration for Market Regulation, National Standardization Management Committee. Interconnection and Interoperation of Numerical Control Equipment-part 3: Implementation Oriented Model Mapping: [S]. Beijing: Standards Press of China, 2020.
7	谭力恒, 陈刚, 蒋秉川, 等. 新工科背景下三维计算机图形学新形态课程建设研究[J]. 测绘通报, 2022(增1): 142-148.
	Tan Liheng, Chen Gang, Jiang Bingchuan, et al. Research of New Curriculum Construction in Three-dimensional Computer Graphics Course Under the Background of New Engineering[J]. Bulletin of Surveying and Mapping, 2022(S1): 142-148.
8	Shakoor Modesar. FEMS-a Mechanics-oriented Finite Element Modeling Software[J]. Computer Physics Communications, 2021, 260: 107729.
9	陈孟雨, 贾敏智. 雾计算中延迟优化的服务动态部署方法仿真[J]. 计算机仿真, 2023, 40(4): 480-485.
	Chen Mengyu, Jia Minzhi. Simulation of Services Dynamic Deployment Method with Delay Optimization in Fog Computing[J]. Computer Simulation, 2023, 40(4): 480-485.
10	施巍松, 张星洲, 王一帆, 等. 边缘计算: 现状与展望[J]. 计算机研究与发展, 2019, 56(1): 69-89.
	Shi Weisong, Zhang Xingzhou, Wang Yifan, et al. Edge Computing: State-of-the-art and Future Directions[J]. Journal of Computer Research and Development, 2019, 56(1): 69-89.
11	Liu Bufan, Zhang Yingfeng, Zhang Geng, et al. Edge-cloud Orchestration Driven Industrial Smart Product-service Systems Solution Design Based on CPS and IIoT[J]. Advanced Engineering Informatics, 2019, 42: 100984.
12	国家市场监督管理总局, 国家标准化管理委员会. 数控装备互联互通及互操作第2部分: 设备描述模型: [S]. 北京: 中国标准出版社, 2020
	State Administration for Market Regulation, National Standardization Management Committee. Interconnection and Interoperation of Numerical Control Equipment-part 2: Device Description Model: [S]. Beijing: Standards Press of China, 2020.
13	国家市场监督管理总局, 国家标准化管理委员会. 数控装备互联互通及互操作第4部分:数控机床对象字典: [S]. 北京: 中国标准出版社, 2020.
	State Administration for Market Regulation, National Standardization Management Committee. Interconnection and Interoperation of Numerical Control Equipment-part 4: Object Dictionary of Numerical Control Machine Tools: [S]. Beijing: Standards Press of China, 2020.
14	王剑, 王好臣, 李学伟, 等. 基于OPC UA的数字孪生车间信息物理融合系统[J]. 现代制造工程, 2023(4): 43-50.
	Wang Jian, Wang Haochen, Li Xuewei, et al. Digital Twin Workshop Information Physical Fusion System Based on OPC UA[J]. Modern Manufacturing Engineering, 2023(4): 43-50.
15	Schleipen Miriam, Gilani Syed-Shiraz, Bischoff Tino, et al. OPC UA & Industrie 4.0-enabling Technology with High Diversity and Variability[J]. Procedia CIRP, 2016, 57: 315-320.
16	Jürgen Jasperneite, Neumann Arne, Pethig Florian. OPC UA Versus MTConnect. S2 2015 [S]. [S.l.:s.n.], 2015: 16-21.
17	Martins André, Lucas João, Costelha Hugo, et al. Developing an OPC UA Server for CNC Machines[J]. Procedia Computer Science, 2021, 180: 561-570.
18	丁国龙, 汤明俊, 邱兆祥, 等. 基于西门子840Dsl的轧辊磨削软件开发[J]. 机床与液压, 2021, 49(3): 91-95.
	Ding Guolong, Tang Mingjun, Qiu Zhaoxiang, et al. Roller Grinding Software Development Based on Sinumerik 840Dsl[J]. Machine Tool & Hydraulics, 2021, 49(3): 91-95.
19	叶佩青, 张勇, 张辉. 数控技术发展状况及策略综述[J]. 机械工程学报, 2015, 51(21): 113-120.
	Ye Peiqing, Zhang Yong, Zhang Hui. Review on the Development and Strategies of CNC Technology[J]. Journal of Mechanical Engineering, 2015, 51(21): 113-120.
20	王立平, 张兆坤, 邵珠峰, 等. 机床制造加工数字化车间信息模型及其应用研究[J]. 机械工程学报, 2019, 55(9): 154-165.
	Wang Liping, Zhang Zhaokun, Shao Zhufeng, et al. Research on the Information Model of Digital Machining Workshop for Machine Tools and Its Applications[J]. Journal of Mechanical Engineering, 2019, 55(9): 154-165.
21	Vishnu V S, Kiran George Varghese, Gurumoorthy B. A Data-driven Digital Twin of CNC Machining Processes for Predicting Surface Roughness[J]. Procedia CIRP, 2021, 104: 1065-1070.
22	Walter B, Marschner S R, Li Hongsong, et al. Microfacet Models for Refraction Through Rough Surfaces[C]//Proceedings of the 18th Eurographics Conference on Rendering Techniques. Goslar: Eurographics Association, 2007: 195-206.
23	汪方斌, 伊龙, 王峰, 等. 基于漫反射优化的金属表面偏振双向反射分布函数[J]. 光学学报, 2021, 41(11): 209-215.
	Wang Fangbin, Yi Long, Wang Feng, et al. Polarization Bidirectional Reflection Distribution Function of Metal Surfaces Based on Diffuse Reflection Optimization[J]. Acta Optica Sinica, 2021, 41(11): 209-215.
24	Summerfield Mark. Rapid GUI Programming with Python and QT: The Definitive Guide to PyQt Programming[M]. Upper Saddle River: Prentice Hall Press, 2007.

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