Towards improving the future of manufacturing through digital twin and augmented reality technologies
Open accessAbstract
We are on the cusp of a technological revolution that will fundamentally change our relationships to others and the way we live and work. These changes, in their importance, scope, and complexity, is different than what humanity has known until now. We do not yet know what will happen, but one thing is certain: our response must be comprehensive and it must involve all stakeholders at the global level: the public sector, the private sector, the academic world and civil society. Applications for the industrial sector are already numerous: predictive maintenance, improved decision-making in real time, anticipation of stocks according to the progress of production, etc. So many improvements that optimize the production tools every day a little more, and point to possibilities for the future of Industry 4.0, the crossroads of an interconnected global world. This work comes to contribute as a part of this industrial evolution(Usine 4.0). In this paper we introduce a part of a collaboration between industry and research area in order to develop a DT and AR industrial solution as a part of a predictive maintenance framework. In this context, we elaborate a proof-of-concept that was developed in special industrial application.
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Seven articles in Table 1 developed ad-hoc systems to build DT.
“In Table 1 seven articles developed ' ad-hoc ' systems to build DT: for the management of a didactic production line [26], to construct a MES in Android systems [43], to build user guides for safety reason [60,62], and to evaluate the performances of a cloud-based DT [74,75];”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [60]
Section 4.2.1 (page unavailable) describes an ad-hoc augmented-reality application with configuration and maintenance guidance modules, and Section 4.1 describes the paper’s digital-twin proof of concept. The cited paper therefore fits as an example of a purpose-built system, but our copy does not say that its guides were built for safety reasons or that the guidance application was already integrated with the digital twin. The count of seven articles is the citing paper’s own Table 1 summary, not a finding attributed to this individual source. (LLM, conf=0.83)
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Aplicações da Realidade Aumentada na Manufatura: uma Revisão da Literatura
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Digital Twin and Extended Reality: Strategic Approach and Practical Implementation
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6G-BRICKS - An Insight into the Key Value Indicators (KVIs) Developed
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- Authors
- Rabah, S., Assila, A., Khouri, E., Maier, F., Ababsa, F., bourny, V., Maier, P., & Mérienne, F.
- Affiliations
- UniLaSalle Amiens (ESIEE-Amiens) (FR)
- Centre d'Etudes Superieures Industrielles (FR)
- Centre National de la Recherche Scientifique (FR)
- Études-Recherches-Matériaux (France) (FR)
- Corresponding author
- Rabah, Souad
- Assila, Ahlem
- Venue
- Procedia Manufacturing
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 17
- Issue
- Special issue on 28th International Conference on Flexible Automation and Intelligent Manufacturing
- Pages
- 460-467
- ISSN
- 2351-9789 (ISSN-L)
- OpenAlex
- W2901498066
- Cited by
- 86
- References
- 28
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 8 · 2023: 17 · 2022: 25 · 2021: 14 · 2020: 8 · 2019: 2
- FWCI
- 4.97
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Manufacturing (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion, other-oa)
- SAM, the Arts et Métiers ParisTech open access repository (Paris Institute of Technology) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, augmented reality, Industry 4.0, predictive maintenance, manufacturing industry, industry-research collaboration, proof of concept, real-time decision making
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