TY - JOUR
T1 - Development of an edge computing-based cyber-physical machine tool
AU - Zhang, Jian
AU - Deng, Changyi
AU - Zheng, Pai
AU - Xu, Xun
AU - Ma, Zhentao
N1 - Funding Information:
This work was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (grant no. 2019ZX04014001-004). The authors would like to thank Dr. Chao Liu from the University of Auckland for supporting the experimental work. The authors are also grateful to the researchers at the Laboratory for Industry 4.0 Smart Manufacturing Systems, the University of Auckland. Finally, we wish to thank the editor and the anonymous reviewers for helping to strengthen this paper.
Funding Information:
This work was supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (grant no. 2019ZX04014001-004 ). The authors would like to thank Dr. Chao Liu from the University of Auckland for supporting the experimental work. The authors are also grateful to the researchers at the Laboratory for Industry 4.0 Smart Manufacturing Systems, the University of Auckland. Finally, we wish to thank the editor and the anonymous reviewers for helping to strengthen this paper.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/2
Y1 - 2021/2
N2 - Digital twin is a virtual model that represents physical entities in a digital manner. By leveraging means of data to simulate the behavior of physical entities in the real environment, the functions of physical entities can be optimized and expanded, through virtual and real interaction feedback, data fusion, decision making, and optimization. Despite numerous researches on digital twin concept and its applications, scarcely any discusses about the computation efficiency of the twin established. In order to shorten the latency of mapping and reduce the high computation workload in the cloud, this paper develops a cyber-physical machine tool based on edge computing techniques, to realize remote sensing, real-time monitoring and scalable high-performance digital twin application. Furthermore, a novel edge computing algorithm is proposed to detect the abnormality of the edge data from two aspects: the unary outliers of the edge data itself and the multivariate parameter correlation among edge devices. The effectiveness of the application platform of the cyber-physical machine tool developed is verified by the prototype system and edge algorithm experiment.
AB - Digital twin is a virtual model that represents physical entities in a digital manner. By leveraging means of data to simulate the behavior of physical entities in the real environment, the functions of physical entities can be optimized and expanded, through virtual and real interaction feedback, data fusion, decision making, and optimization. Despite numerous researches on digital twin concept and its applications, scarcely any discusses about the computation efficiency of the twin established. In order to shorten the latency of mapping and reduce the high computation workload in the cloud, this paper develops a cyber-physical machine tool based on edge computing techniques, to realize remote sensing, real-time monitoring and scalable high-performance digital twin application. Furthermore, a novel edge computing algorithm is proposed to detect the abnormality of the edge data from two aspects: the unary outliers of the edge data itself and the multivariate parameter correlation among edge devices. The effectiveness of the application platform of the cyber-physical machine tool developed is verified by the prototype system and edge algorithm experiment.
KW - Digital twin
KW - Edge computing
KW - Machine tool
KW - Mapping
UR - http://www.scopus.com/inward/record.url?scp=85088824345&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2020.102042
DO - 10.1016/j.rcim.2020.102042
M3 - Journal article
AN - SCOPUS:85088824345
SN - 0736-5845
VL - 67
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102042
ER -