Shifting value stream patterns along the product lifecycle with digital twins

Schleich, Benjamin; Dittrich, Marc-André; Clausmeyer, Till; Damgrave, Roy; Erkoyuncu, John Ahmet; Haefner, Benjamin; Lange, J. de; Plakhotnik, Denys; Scheidel, Wieben; Wuest, Thorsten

doi:10.1016/j.procir.2020.01.049

Cited by 46 publications

(19 citation statements)

References 38 publications

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“…The importance of industry 4.0 and its related concepts and technologies has been widely acknowledged [6][7][8][9]. Between 2012 and 2016, IoT (or the similar 'internet of everything') was considered one of the ten most important technology trends of the year by Gartner, followed by DT as one of the five most important technology trends between 2017 and 2019 [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Digital Twin and Internet of Things—Current Standards Landscape

2020

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Industry 4.0 is revolutionizing industrial production by bridging the physical and the virtual worlds and further improving digitalization. Two essential building blocks in industry 4.0 are digital twins (DT) and the internet of things (IoT). While IoT is about connecting resources and collecting data about the physical world, DTs are the virtual representations of resources organizing and managing information and being tightly integrated with artificial intelligence, machine learning and cognitive services to further optimize and automate production. The concepts of DTs and IoT are overlapping when it comes to describing, discovering and accessing resources. Currently, there are multiple DT and IoT standards covering these overlapping aspects created by different organizations with different backgrounds and perspectives. With regard to interoperability, which is presumably the most important aspect of industry 4.0, this barrier needs to be overcome by consolidation of standards. The objective of this paper is to investigate current DT and IoT standards and provide insights to stimulate this consolidation. Overlapping aspects are identified and a classification scheme is created and applied to the standards. The results are compared, aspects with high similarity or divergence are identified and a proposal for stimulating consolidation is presented. Consensus between standards are found regarding the elements a resource should consist of and which serialization format(s) and network protocols to use. Controversial topics include which query language to use for discovery as well as if geo-spatial, temporal and historical data should be explicitly supported.

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Section: Introductionmentioning

confidence: 99%

Digital Twin and Internet of Things—Current Standards Landscape

2020

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“…A valid reference framework covering separate manufacturing levels should be defined in the future. For this purpose, interdisciplinary cooperation is indispensable [224].…”

Section: Challenges and Outlookmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

189

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…The Twin is built up by a Digital Master, which includes models to characterize the structure and behaviour of the physical counterpart, as well as by a Digital Shadow (Schuh et al, 2018), in which real time data is stored and evaluated in combination with specific models of the Digital Master. Beside these definitions, a plethora of papers aim at illustrating the potential application of digital twins for several purposes and tasks along the product life cycle, such as (Schleich et al, 2019) and (Tao et al, 2019b). In view of these different potential applications, still, many industry sectors are not able to apply a generic definition onto their product portfolio, as digital twins differ from each other to the same degree as physical products differ from each other.…”

Section: Digital Twinsmentioning

confidence: 99%

Digital Twins - Definitions, Classes and Business Scenarios for Different Industry Sectors

2021

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Over the recent years, several attempts were made to define the concept of the Digital Twin and to create a generic view for utilizing it within the industry. Still, many industry sectors are not able to transfer a generic definition into their product portfolio, as Digital Twins differ from each other to the same degree as physical products differ from each other. Hence, it is crucial to enlarge the definition towards a classification and business scenarios which enable sector specific views on the concept of the Digital Twin and help SME to utilize the concept towards their products. Future engineers will have to design physical products besides a digital counterpart and therefore have to identify interdependencies between these two products during the development. This paper discusses a generic definition of a Digital Twin that can be applied throughout different sectors as well as a classification for Digital Twins to enable the implementation of the concept on several maturity levels regarding the constraints of the product portfolio. In addition, these classes are viewed in different business scenarios and an outlook is given to further increase the usability of Digital Twins within new industry sectors.

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Shifting value stream patterns along the product lifecycle with digital twins

Cited by 46 publications

References 38 publications

Digital Twin and Internet of Things—Current Standards Landscape

Digital Twin and Internet of Things—Current Standards Landscape

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital Twins - Definitions, Classes and Business Scenarios for Different Industry Sectors

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