Representing adaptation options in experimentable digital twins of production systems

Delbrügger, Tim; Roßmann, Jürgen

doi:10.1080/0951192x.2019.1599433

Cited by 26 publications

(14 citation statements)

References 20 publications

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“… Digital twins . The development of digital twins of the production systems and processes includes modelling and simulation of objects and processes, organization of the collection, processing and storage of data, as well as identification and traceability of production objects [14]. In this sense, it is interesting to address that in [15] it was noted that a digital twin of a production system is understood as a multiscale digital layout that allows modelling processes to occur in a real system and collection and display of the real-time status of objects obtained from the PLC and sensors installed in the production system.…”

Section: Methodsmentioning

confidence: 99%

Development of an industrial cyber-physical platform for small series production using digital twins

Yablochnikov

Chukichev

Timofeeva

et al. 2021

Phil. Trans. R. Soc. A.

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The article describes an industrial cyber-physical platform for small series production using digital twins under development at ITMO University (Saint Petersburg, Russia). The platform is based on the following approaches: group technology, adaptive and selective assembling, and digital twin of production systems and processes. The article presents a mechanism for constructing a unified manufacturing process, and results of an integrated multiscale simulation of an injection moulding process. The issues of ensuring identification and monitoring of objects of the industrial cyber-physical platform are considered. Specific service applications required to implement the smart product concept are discussed. The combination of the considered technologies is used to create digital twins of production system objects. All humans that have different roles in the product value stream can interact with the industrial cyber-physical platform at the three levels, receiving support in performing their tasks. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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Section: Methodsmentioning

confidence: 99%

Development of an industrial cyber-physical platform for small series production using digital twins

Yablochnikov

Chukichev

Timofeeva

et al. 2021

Phil. Trans. R. Soc. A.

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show abstract

“…Machine learning has helped identify potential quality issues that would otherwise go unnoticed by less sophisticated methods. Various computer vision models have been used [26] to address quality issues and efficiency in the assembly of products. Decision trees, support vector machines, or artificial neural networks have been used to predict or detect deformations in production [27].…”

Section: Quality Controlmentioning

confidence: 99%

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

Malik

Rajaguru

Azzawi

2022

2022 8th International Conference on Information Technology Trends (ITT)

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Digital twin and artificial intelligence technologies have proliferated as crucial enablers for Industry 4.0. With a digital twin, companies can digitally test and validate a product before it exists in the real world. By digitally recreating the planned production process for real-world use, engineers can identify any potential process changes before they happen. This brief survey summarizes their general developments and the current state of AI integration in smart manufacturing and advanced robotics. This survey also covers industrial automation and emerging techniques, such as 3D printing.

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“…Deep learning (DL) computer vision models, including residual and convolutional neural networks were deployed to recognize eventual quality issues during the automatic production and machining features of parts [ 103 , 104 ], which could be further utilized to enhance the quality and efficiency of assembly processes [ 108 ] ( E -factor), or retraced to the production planning stage in order to support decision making on the basis of historical production knowledge [ 109 ], as a “smart expert” in a collaborative environment ( SG -factor). Following the general concept of integrating ML methods into the digital production twins [ 110 ], DTs of production systems in combination with MBSE can be modeled and adapted modularly as a virtual testbed, which in turn could provide a runtime environment for simulation-based optimization [ 111 , 112 ].…”

Section: Sustainable Resilient Manufacturingmentioning

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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Representing adaptation options in experimentable digital twins of production systems

Cited by 26 publications

References 20 publications

Development of an industrial cyber-physical platform for small series production using digital twins

Development of an industrial cyber-physical platform for small series production using digital twins

Smart Manufacturing with Artificial Intelligence and Digital Twin: A Brief Review

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

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