• français
    • English
    English
  • Ouvrir une session
Aide
Voir le document 
  •   Accueil de SAM
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • Voir le document
  • Accueil de SAM
  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)
  • Voir le document
JavaScript is disabled for your browser. Some features of this site may not work without it.

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

Article dans une revue avec comité de lecture
Auteur
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
MAIER, Florian
300385 École Supérieure d'Ingénieurs en Électronique et Électrotechnique
ABABSA, Fakhreddine
BOURNY, Valéry
39101 Laboratoire des technologies innovantes - UR UPJV 3899 [LTI]
MAIER, Paul
553062 Société EREM [EREM]
ccMERIENNE, 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

Résumé

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.

Fichier(s) constituant cette publication

Nom:
LE2I_PROCMANUFACTURING_2018_AB ...
Taille:
845.0Ko
Format:
PDF
Voir/Ouvrir

Cette publication figure dans le(s) laboratoire(s) suivant(s)

  • Laboratoire d’Ingénierie des Systèmes Physiques Et Numériques (LISPEN)

Documents liés

Visualiser des documents liés par titre, auteur, créateur et sujet.

  • Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry 
    Article dans une revue avec comité de lecture
    LOIZEAU, Quentin; ABABSA, Fakhreddine; ccMERIENNE, Frédéric; ccDANGLADE, Florence (Frontiers, 2021)
    Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various ...
  • Evaluating Added Value of Augmented Reality to Assist Aeronautical Maintenance Workers - Experimentation on On-Field Use Case 
    Communication avec acte
    LOIZEAU, Quentin; ABABSA, Fakhreddine; ccMERIENNE, Frédéric; ccDANGLADE, Florence (2019)
    Augmented Reality (AR) technology facilitates interactions with information and understanding of complex situations. Aeronautical Maintenance combines complexity induced by the variety of products and constraints associated ...
  • Defining an Indicator for Navigation Performance Measurement in VE Based on ISO/IEC15939 
    Communication avec acte
    ASSILA, Ahlem; ccPLOUZEAU, Jeremy; ERFANIAN, Aïda; HU, Yaoping; ccMERIENNE, Frédéric (Springer, 2017)
    Navigation is a key factor for immersion and exploration in virtual environment (VE). Nevertheless, measuring navigation performance is not an easy task, especially when analyzing and interpreting heterogeneous results of ...
  • GMCAD: an original Synthetic Dataset of 2D Designs along their Geometrical and Mechanical Conditions 
    Article dans une revue avec comité de lecture
    ALMASRI, Waad; BETTEBGHOR, Dimitri; ADJED, Faouzi; ABABSA, Fakhreddine; ccDANGLADE, Florence (Elsevier BV, 2022)
    We build an original synthetic dataset of 2D mechanical designs alongside their mechanical and geometric constraints, GMCAD. Such a dataset allows training Deep Learning (DL) models for Design for Additive Manufacturing ...
  • Deep Learning Architecture for Topological Optimized Mechanical Design Generation with Complex Shape Criterion 
    Communication avec acte
    ALMASRI, Waad; BETTEBGHOR, Dimitri; ABABSA, Fakhreddine; ADJED, Faouzi; ccDANGLADE, Florence (Springer International Publishing, 2021)
    Topology optimization is a powerful tool for producing an optimal free-form design from input mechanical constraints. However, in its traditional-density-based approach, it does not feature a proper definition for the ...

Parcourir

Tout SAMLaboratoiresAuteursDates de publicationCampus/InstitutsCe LaboratoireAuteursDates de publicationCampus/Instituts

Lettre Diffuser la Science

Dernière lettreVoir plus

Statistiques de consultation

Publications les plus consultéesStatistiques par paysAuteurs les plus consultés

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales

ÉCOLE NATIONALE SUPERIEURE D'ARTS ET METIERS

  • Contact
  • Mentions légales