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Modular augmented reality platform for smart operator in production environment
Abstract
The trend of mass customization requires highly flexible production systems, which pose a new challenge for workers. Numerous concepts using augmented reality technologies have been developed to offer support in manual assembly processes. Newer AR devices are equipped with more and more functions, such as microphones, speakers or multiple camera systems, which can be used to help workers. However, wearable devices offer only limited computational power and limited flexibility of their interface platform. This poses a challenge to heavy data transactions from various different sources and restrict the use for real-time responses, which are often necessary for applications in a factory. To overcome this limitation, the author of this paper proposes a combination of wearable devices with edge devices located on the shop floor. An augmented reality platform was constructed with modularized services by using the digital twin of the machine components and machine learning algorithms for object recognition. This is integrated in the factory environment to synchronize real machine components with their digital twin.
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“- 7) In some cases, the DT is used for the 'Design' of a machine [68,72], of a laboratory environment [71], of a product or production environment [69,73] or for the redesign of the working environment [70].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [73]
In the citing paper
“The dataset description is showed only in nineteen articles: in four cases a description is done using Unified Modeling Language (UML) [25,51,63,73], in one case only with the Sequence Diagram (SD) [75].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [73]
In the citing paper
“in four cases a description is done using Unified Modeling Language (UML) [25,51,63,73]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [73]
In the citing paper
“Some DT applications in literature do not comply with one of the two mentioned groups, as they are developed specifically to offer one single service, such as only for system monitoring ([67,72]), or for performance optimization ([41,64,73]).”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [73]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 13
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Cited by
Sorted by citations
Review of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874
Metadata
- Authors
- Um, J., Popper, J., & Ruskowski, M.
- Affiliations
- SmartFactory (Germany) (DE)
- German Research Centre for Artificial Intelligence (DE)
- Venue
- 2018 IEEE Industrial Cyber-Physical Systems (ICPS)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 720-725
- OpenAlex
- W2810147626
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 10 · 2021: 2 · 2020: 4 · 2019: 2 · 2018: 2
- FWCI
- 0.75
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Keywords
- augmented reality, wearable devices, edge computing, modular software platform, digital twin, object recognition, machine learning, manual assembly, smart factory, mass customization, real-time data processing, shop floor
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- The paper
- Um, J., Popper, J., & Ruskowski, M. (2018). Modular augmented reality platform for smart operator in production environment. 2018 IEEE Industrial Cyber-Physical Systems (ICPS). https://doi.org/10.1109/icphys.2018.8390796
