用于协作生态系统的可信和安全组合数字孪生体系结构

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2022-11-24 DOI:10.1049/cim2.12070
Pasindu Manisha Kuruppuarachchi, Susan Rea, Alan McGibney
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引用次数: 2

摘要

数字化为企业在内部和外部实施和管理协作生态系统创造了新的机会。数字孪生(DT)是一种迅速兴起的技术,可用于促进新的交互和信息共享模型。DT是物理过程或资产的数字版本,可用于建模、管理和优化其物理对应物。连接多个dt对于提供跨复杂生态系统的整体集成和视图至关重要。要创建基于区块链的协作生态系统架构,需要解决以下问题。信任是一个基本要求,因为多方将作为复合DT的一部分共同工作。互操作性是必不可少的,因为来自不同领域的dt将需要相互连接和无缝操作。最后,治理具有挑战性,因为不同的场景需要不同的机制和治理结构。本研究提出了一个架构,以实现多个基于3d打印的协作生态系统,并通过示例用例场景来展示其在协同制造中的适用性。
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Trusted and secure composite digital twin architecture for collaborative ecosystems

Digitalisation creates new opportunities for businesses to implement and manage collaborative ecosystems both internally and externally. Digital twin (DT) is a rapidly emerging technology that can be used to facilitate new models of interaction and sharing of information. DT is the digital version of a physical process or asset that can be used to model, manage, and optimise its physical counterpart. Connecting multiple DTs is vital to provide a holistic integration and view across complex ecosystems. To create a DT-based collaborative ecosystem architecture, the following concerns need to be addressed. Trust is a fundamental requirement because multiple parties will work together as part of a composite DT. Interoperability is essential, as DTs from various domains will be required to interconnect and operate seamlessly. Finally, the governance is challenging as different scenarios require various mechanisms and governance structures. This study presents an architecture to enable multiple DT-based collaborative ecosystems, and example use case scenarios to demonstrate its applicability in collaborative manufacturing.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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