Towards Augmented Reality Guiding Systems: An Engineering Design of an Immersive System for Complex 3D Printing Repair Process

Bozzi, Luca O. Solari; Samson, Kerr D. G.; Tadeja, Sławomir K.; Bohné, Thomas; Pattinson, Sebastian W.

doi:10.1109/vrw58643.2023.00084

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…AR assembly is used before or after robotic assembly to handle complex assemblies unsuitable for robots. AR guides train users to assemble the required components or repair machines [35]. The robotic assembly utilises a monorail to move trays between stations with Fanuc SCARA and LR Mate robots to perform different assembly actions.…”

Section: Developmentmentioning

confidence: 99%

Design of a Demonstrator Environment for Investigating Multi-Factory Production and Operation Challenges

Brooks,

McFarlane,

Thorne

et al. 2023

Advances in Transdisciplinary Engineering

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Demonstrators, testbeds and learning factories enable researchers to investigate important manufacturing challenges and to trial solutions without disrupting industrial production facilities. In this way, solutions and systems can be developed close to a ‘production-ready’ state prior to industrial deployment. This paper reviews demonstrators and testbeds developed for smart manufacturing in the last 20 years. A key observation is that such demonstrators have predominantly focused on emulating single or multiple closely connected operations. Such developments reflect the activities of a single production facility and/or organisation. In contrast, there are few reports on demonstrators which seek to replicate the behaviour and challenges associated with multi-site factories or integration with existing legacy factory systems. To address this gap, a multi-operation demonstrator has been created. The demonstrator aims to replicate coordinated production between multiple small manufacturing sites and provides a testbed to investigate operational challenges. The current demonstrator, the research investigated, and the direction of future research proposed are outlined.

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Section: Developmentmentioning

confidence: 99%

Design of a Demonstrator Environment for Investigating Multi-Factory Production and Operation Challenges

Brooks,

McFarlane,

Thorne

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

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Educating Manufacturing Operators by Extending Reality with AI

Zuercher,

Schimpf,

Tadeja

et al. 2024

Intelligent Systems Reference Library

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Decision support for augmented reality-based assistance systems deployment in industrial settings

Bock,

Bohné,

Tadeja

2024

Multimed Tools Appl

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The successful deployment of augmented reality (AR) in the industry for on-the-job guidance depends heavily on factors such as the availability of required expertise, existing digital content and other deployment-related criteria such as a task’s error-proneness or complexity. Particularly in idiosyncratic manufacturing situations involving customised products and diverse complex and non-complex products and its variants, the applicability and attractiveness of AR as a worker assistance system is often unclear and difficult to gauge for decision-makers. To address this gap, we developed a decision support tool to help prepare customised deployment strategies for AR-based assistance systems utilising manual assembly as the main example. Consequently, we report results from an interview study with sixteen domain experts. Furthermore, when analysing captured expert knowledge, we found significant differences in criteria weighting based on task complexity and other factors, such as the effort required to obtain data.

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Towards Augmented Reality Guiding Systems: An Engineering Design of an Immersive System for Complex 3D Printing Repair Process

Cited by 8 publications

References 26 publications

Design of a Demonstrator Environment for Investigating Multi-Factory Production and Operation Challenges

Design of a Demonstrator Environment for Investigating Multi-Factory Production and Operation Challenges

Educating Manufacturing Operators by Extending Reality with AI

Decision support for augmented reality-based assistance systems deployment in industrial settings

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