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Towards improving the future of manufacturing through digital twin and augmented reality technologies

Type
Articles dans des revues avec comité de lecture
Author
RABAH, Souad
300385 École Supérieure d'Ingénieurs en Électronique et Électrotechnique
ASSILA, Ahlem
470176 CESI : groupe d’Enseignement Supérieur et de Formation Professionnelle [CESI]
KHOURI, Elio
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
MAIER, Florian
300385 École Supérieure d'Ingénieurs en Électronique et Électrotechnique
ABABSA, Fakreddine
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
BOURNY, Valéry
39101 Laboratoire des technologies innovantes [LTI]
MAIER, Paul
553062 Société EREM [EREM]
MERIENNE, Frédéric
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]

URI
http://hdl.handle.net/10985/14459
DOI
10.1016/j.promfg.2018.10.070
Date
2018
Journal
Procedia Manufacturing

Abstract

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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