Implementation of a holistic digital twin solution for design prototyping and virtual commissioning

IF 2.5 Q2 ENGINEERING, INDUSTRIAL
Miriam Ugarte Querejeta, Miren Illarramendi Rezabal, Gorka Unamuno, Jose Luis Bellanco, Eneko Ugalde, Antonio Valor Valor
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引用次数: 5

Abstract

Industry 4.0 has ushered in a new era of digital manufacturing and in this context, digital twins are considered as the next wave of simulation technologies. The development and commissioning of Cyber Physical Systems (CPS) is taking advantage of these technologies to improve product quality while reducing costs and time to market. However, existing practices of virtual design prototyping and commissioning require the cooperation of domain specific engineering fields. This involves considerable effort as development is mostly carried out in different departments using vendor specific simulation tools. There is still no integrated simulation environment commercially available, in which all engineering disciplines can work collaboratively. This presents a major challenge when interlinking virtual models with their physical counterparts. This paper therefore addresses these challenges by implementing a holistic and vendor agnostic digital twin solution for design prototyping and commissioning practices. The solution was tested in an industrial use case, in which the digital twin effectively prototyped cost-efficient solar assembly lines.

Abstract Image

用于设计原型和虚拟调试的整体数字孪生解决方案的实现
工业4.0开启了数字化制造的新时代,在这种背景下,数字孪生被认为是下一波仿真技术。网络物理系统(CPS)的开发和调试正在利用这些技术来提高产品质量,同时降低成本和上市时间。然而,现有的虚拟设计原型和调试实践需要特定领域工程领域的合作。这涉及到相当大的工作量,因为开发主要是在不同的部门使用供应商特定的模拟工具进行的。目前还没有商业上可用的集成仿真环境,在其中所有的工程学科可以协同工作。这在将虚拟模型与物理模型相关联时提出了一个主要挑战。因此,本文通过为设计原型和调试实践实现一个整体的、与供应商无关的数字孪生解决方案来解决这些挑战。该解决方案在一个工业用例中进行了测试,其中数字孪生模型有效地建立了成本效益高的太阳能装配线原型。
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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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