Correspondence measure: a review for the digital twin standardization

被引:6
作者
Khan, Tarique Hasan [1 ]
Noh, Chiho [2 ]
Han, Soonhung [3 ]
机构
[1] New Jersey Inst Technol, Newark, NJ 07102 USA
[2] Korea STEP Ctr, Daejeon, South Korea
[3] Korea Adv Inst Sci & Technol KAIST, Mech Engn Dept, Daejeon, South Korea
关键词
Digital twin; Standardization; Spatiotemporal similarity measure; CAD; SIMILARITY MEASURE; FRAMEWORK; CAD; 3D; OPTIMIZATION; MODELS; SYSTEM;
D O I
10.1007/s00170-023-12019-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study delves into the critical issue of Digital Twin (DT) technology standardization, focusing on introducing a novel approach called "correspondence measure." We draw upon extensive literature review and case study analyses to investigate existing standardization methods, identifying sector-specific and problem-specific strategies. We also underline the importance of interoperability, data privacy and security, real-time synchronization, and accuracy and fidelity in the standardization of DTs. Unveiling the intricacies of the "correspondence measure," we elucidate its potential in enhancing the standardization process by providing a standardized measure of the accuracy and reliability of a digital twin concerning its physical counterpart. Furthermore, we discuss the applications of our proposed method in various sectors such as manufacturing, healthcare, aerospace, maritime and shipping, and city management. Our findings suggest that the "correspondence measure" can significantly contribute to the existing standardization approaches by facilitating a better understanding of the DT's behavior, thereby fostering trust in these digital replications. This paper not only offers a theoretical contribution to the literature on DT standardization but also provides practical insights for the stakeholders involved in developing, implementing, and managing digital twins.
引用
收藏
页码:1907 / 1927
页数:21
相关论文
共 74 条
[41]  
Malkauthekar M., 2013, 3 INT C COMPUTATIONA, P503, DOI DOI 10.1049/CP.2013.2636
[42]  
Mandery C, 2016, 2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), P355
[43]   Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry [J].
Min, Qingfei ;
Lu, Yangguang ;
Liu, Zhiyong ;
Su, Chao ;
Wang, Bo .
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2019, 49 :502-519
[44]   Cyber-physical production systems: Roots, expectations and R&D challenges [J].
Monostori, Laszlo .
VARIETY MANAGEMENT IN MANUFACTURING: PROCEEDINGS OF THE 47TH CIRP CONFERENCE ON MANUFACTURING SYSTEMS, 2014, 17 :9-13
[45]   Shape-similarity search of three-dimensional models using parameterized statistics [J].
Ohbuchi, R ;
Otagiri, T ;
Ibato, M ;
Takei, T .
10TH PACIFIC CONFERENCE ON COMPUTER GRAPHICS AND APPLICATIONS, PROCEEDINGS, 2002, :265-274
[46]  
Oliveira P.P., 2020, J. Airp. Manag., V14, P246
[47]   Matching 3D models with shape distributions [J].
Osada, R ;
Funkhouser, T ;
Chazelle, B ;
Dobkin, D .
INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS, PROCEEDING, 2001, :154-+
[48]  
Pires F, 2019, IEEE INTL CONF IND I, P721, DOI [10.1109/indin41052.2019.8972134, 10.1109/INDIN41052.2019.8972134]
[49]   File- and API-based interoperability of digital twins by model transformation: An IIoT case study using asset administration shell [J].
Platenius-Mohr, Marie ;
Malakuti, Somayeh ;
Gruener, Sten ;
Schmitt, Johannes ;
Goldschmidt, Thomas .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 113 :94-105
[50]   Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison [J].
Qi, Qinglin ;
Tao, Fei .
IEEE ACCESS, 2018, 6 :3585-3593