Minimal AR: visual asset optimization for the authoring of augmented reality work instructions in manufacturing
Open accessAbstract
This work investigates the possibility of using a novel "minimal AR" authoring approach to optimize the visual assets used in augmented reality (AR) interfaces to convey work instructions in manufacturing. In the literature, there are no widely supported guidelines for the optimal choice of visual assets (e.g., CAD models, drawings, and videos). Therefore, to avoid the risk of having AR technical documentation based only on the author's preference, our work proposes a novel authoring approach that enforces the minimal amount of information to accomplish a task. Minimal AR was tested through a simulated AR LEGO-based assembly task. The performance (completion time, mental workload, errors) of 40 users was evaluated with 4 combinations of visual assets in 4 tasks with an increasing amount of information needed. The main result is that visual assets with an excess of information do not significantly increase performance. Therefore, the location of a specified object should be "minimally" authored by an auxiliary model (e.g., a circle and an arrow). For identifying an object within a couple, color coding is preferred to using additional visual assets. If more than two objects must be identified, a drawing visual asset is also needed. Only when the orientation of a selected object must be conveyed, animated product models are required. These insights could be helpful for an optimal design of AR work instructions in a wide range of industrial fields.Supplementary information The online version contains supplementary material available at 10.1007/s00170-021-08449-6.
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Considering the manufacturing and operations management fields, the Metaverse applications can substantially change the dynamics of all stages of the production, commercialization, and related operations.
“Considering the manufacturing and operations management fields, the Metaverse applications can substantially change the dynamics of all stages of the production, commercialization, and related operations (Laviola et al., 2022;Mozumder et al., 2022).”
From Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2022) · cites this paper as Laviola et al., 2022
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A Collaborative AR/VR Platform for Social Manufacturing
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Digital Transformation in the Digital World the Metaverse
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Self-citationFrom Lab to Reality: Optimization of Industrial Augmented Reality Interfaces
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Impact and Limitations of AR-Based Guidance for Assembly Workers
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- Authors
- Laviola, E., Gattullo, M., Manghisi, V. M., Fiorentino, M., & Uva, A. E.
- Affiliations
- Polytechnic University of Bari (IT)
- Corresponding author
- Gattullo, Michele
- Manghisi, Vito Modesto
- Fiorentino, Michele
- Uva, Antonio Emmanuele
- ORCID
- Venue
- The International Journal of Advanced Manufacturing Technology
- Published
- 2021-11-30
- Volume
- 119
- Issue
- 3-4
- Pages
- 1769-1784
- ISSN
- 0268-3768 (ISSN-L)
- 1433-3015
- PMC
- PMC8629731
- OpenAlex
- W3216800902
- Cited by
- 79
- References
- 79
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 23 · 2023: 15 · 2022: 20
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The International Journal of Advanced Manufacturing Technology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- ministero dell’istruzione, dell’università e della ricerca (CUP - D94I18000260001)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Interactive and Immersive Displays
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Manufacturing Process and Optimization
- Keywords
- augmented reality, CAD models, color coding, assembly tasks, mental workload, object identification, information overload
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