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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
Auteur
LOIZEAU, Quentin
ABABSA, Fakhreddine
ccMERIENNE, Frédéric
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
ccDANGLADE, Florence

URI
http://hdl.handle.net/10985/20022
DOI
10.3389/frvir.2020.603189
Date
2021
Journal
Frontiers in Virtual Reality

Résumé

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 high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. However, assessments of the actual impact of AR on industrial processes are limited due to a lack of methods and tools to assist in the integration and evaluation of AR tools in the field. This paper presents a method for deploying AR tools adapted to maintenance workers and for selecting relevant evaluation criteria of the impact in an industrial context. This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on productivity and user satisfaction for all worker profiles. Further work aims to generalize the results to the whole maintenance process in the aeronautical industry. The use of the collected data should enable the prediction of the impact of AR for related maintenance activities.

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

Documents liés

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

  • 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 ...
  • Geometrically-driven generation of mechanical designs through deep convolutional GANs 
    Article dans une revue avec comité de lecture
    ALMASRI, Waad; ccBETTEBGHOR, Dimitri; ADJED, Faouzi; ccDANGLADE, Florence; ABABSA, Fakhreddine (Informa UK Limited, 2022-12-16)
    Despite the freedom Additive Manufacturing (AM) offers when manufacturing organic shapes, it still requires some geometrical criteria to avoid a part's collapse during printing. The most synergetic design approach to AM ...
  • 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 for Additive Manufacturing-driven Topology Optimization 
    Article dans une revue avec comité de lecture
    ALMASRI, Waad; BETTEBGHOR, Dimitri; ADJED, Faouzi; ABABSA, Fakhreddine; ccDANGLADE, Florence (Elsevier BV, 2022-06-21)
    This paper investigates the potential of Deep Learning (DL) for data-driven topology optimization (TO). Unlike the rest of the literature that mainly applies DL to TO from a mechanical perspective, we developed an original ...
  • 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 ...

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