Machining process-oriented monitoring method based on digital twin via augmented reality

被引:54
作者
Liu, Shimin [1 ]
Lu, Shanyu [1 ]
Li, Jie [1 ]
Sun, Xuemin [1 ]
Lu, Yuqian [2 ]
Bao, Jinsong [1 ]
机构
[1] Donghua Univ, Coll Mech Engn, Shanghai 201620, Peoples R China
[2] Univ Auckland, Dept Mech Engn, Auckland 1010, New Zealand
关键词
Digital twin; Augmented reality; Machining; Machining process monitoring;
D O I
10.1007/s00170-021-06838-5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The change of size, surface roughness, residual stress, and so on profoundly influence the final machining quality of complex mechanical products. Digital twin machining technology can ensure machining quality by observing the machining process in real time. However, the current digital twin systems mainly adopt the display method of virtual-real separation. It leads to transmitting the useful processing information to the on-site technicians ineffectively, limiting the digital twin system to help field processing. The monitoring technology on the machining process by augmented reality based on the digital twin machining system is proposed to deal with this problem. Firstly, the augmented reality dynamic multi-view is constructed based on multi-source heterogeneous data. Secondly, the augmented reality is integrated into the real-time monitoring of the intermediate process of complex products to promote cooperation among the operators and the digital twin machining system. It can avoid irreparable errors when the finished product is nearly completed. Finally, the effectiveness and feasibility of the proposed method will be verified by a monitoring application case.
引用
收藏
页码:3491 / 3508
页数:18
相关论文
共 42 条
[1]   Digital twin as enabler for an innovative digital shopfloor management system in the ESB Logistics Learning Factory at Reutlingen - University [J].
Brenner, Beate ;
Hummel, Vera .
7TH CONFERENCE ON LEARNING FACTORIES (CLF 2017), 2017, 9 :198-205
[2]   Digital Twin-oriented real-time cutting simulation for intelligent computer numerical control machining [J].
Cao, Xian ;
Zhao, Gang ;
Xiao, Wenlei .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2022, 236 (1-2) :5-15
[3]   Augmented vision and interactive monitoring in 3D printing process [J].
Ceruti A. ;
Liverani A. ;
Bombardi T. .
International Journal on Interactive Design and Manufacturing, 2017, 11 (02) :385-395
[4]  
[程云 Cheng Yun], 2019, [计算机辅助设计与图形学学报, Journal of Computer-Aided Design & Computer Graphics], V31, P859
[5]   Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system [J].
Coronado, Pedro Daniel Urbina ;
Lynn, Roby ;
Louhichi, Wafa ;
Parto, Mahmoud ;
Wescoat, Ethan ;
Kurfess, Thomas .
JOURNAL OF MANUFACTURING SYSTEMS, 2018, 48 :25-33
[6]  
Fang, 2020, COMPUT INTEGR MANUF, P1
[7]  
Grieves M, 2014, White Paper, V1, P1, DOI DOI 10.5281/ZENODO.1493930
[8]  
Grieves M., 2017, Transdisciplinary Perspectives on Complex Systems, P85, DOI [DOI 10.1007/978-3-319-38756-74, 10.1007/978-3-319-38756-74]
[9]   A time-varying geometry modeling method for parts with deformation during machining process [J].
Hao, Xiaozhong ;
Li, Yingguang ;
Cheng, Yinghao ;
Liu, Changqing ;
Xu, Ke ;
Tang, Kai .
JOURNAL OF MANUFACTURING SYSTEMS, 2020, 55 :15-29
[10]   Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network [J].
Jin, Yanrui ;
Qin, Chengjin ;
Huang, Yixiang ;
Liu, Chengliang .
MEASUREMENT, 2021, 173