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CHALLENGES OF USING AUGMENTED REALITY TO SUPPORT AN EFFICIENT AND ERROR-FREE ASSEMBLY IN COMPLEX VARIANT ENVIRONMENTS

Published online by Cambridge University Press:  19 June 2023

Valesko Cedrik Dausch ,
Daniel Roth ,
Matthias Kreimeyer  and
Sebastian Bohr
Valesko Cedrik Dausch*
Affiliation:
University of Stuttgart;
Daniel Roth
Affiliation:
University of Stuttgart;
Matthias Kreimeyer
Affiliation:
University of Stuttgart;
Sebastian Bohr
Affiliation:
MAN Truck & Bus SE
*
Dausch, Valesko, Cedrik University of Stuttgart, Germany, valesko.dausch@iktd.uni-stuttgart.de

Abstract

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As part of the digital transformation towards Industry 4.0, the tasks of staff on the shop floor are changing. Despite increasing automation, complex assembly steps still have to be carried out by humans, especially when it comes to complex products rich in variants, whose assembly cannpt be fully automated for various reasons. Due to increasing individualization and the steadily growing complexity of products, providing the right information at the right time and in the right place is becoming more important. In this context, the visualization of information via novel technologies such as augmented reality plays a crucial role towards an efficient and error-free production process. This paper compiles existing challenges when using augmented reality as a visualization form for an assistance system. On the one hand, the challenges found originate from a systematic literature review and are organized according to predefined categories. On the other hand, these challenges are complemented and compared through findings gained from expert interviews, which are conducted with employees of two European commercial vehicle manufacturers in the field of production. The analysis of the two methods highlights the need for further research.

Keywords

VisualisationIndustry 4.0Augmented RealityAssemblyInformation management
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 857 - 866
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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