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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 to aeronautic sector and the environment of maintenance. AR tools seem well indicated to solve constraints of productivity and quality on the aeronautical maintenance activities by simplifying data interactions for the workers. However, few evaluations of AR have been done in real processes due to the difficulty of integrating the technology without proper tools for deployment and assessing the results. This paper proposes a method to select suitable criteria for AR evaluation in industrial environment and to deploy AR solutions suited to assist maintenance workers. These are used to set up on-field experiments that demonstrate benefits of AR on process and user point of view for different profiles of workers. Further work will consist on using these elements to extend results to AR evaluation on the whole aeronautical maintenance process. A classification of maintenance activities linked to workers specific needs will lead to prediction of the value that augmented reality would bring to each activity.

International audienceNumerical simulations play more and more important role in product development cycles and are increasingly complex, realistic and varied. CAD models must be adapted to each simulation case to ensure the quality and reliability of the results. The defeaturing is one of the key steps for preparing digital model to a simulation. It requires a great skill and a deep expertise to foresee which features have to be preserved and which features can be simplified. This expertise is often not well developed and strongly depends of the simulation context. In this paper, we propose an approach that uses machine learning techniques to identify rules driving the defeaturing step. The expertise knowledge is supposed to be embedded in a set of configurations that form the basis to develop the processes and find the rules. For this, we propose a method to define the appropriate data models used as inputs and outputs of the learning techniques

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Virtual reality (VR) is a computer-based technology that can be used by professionals of many different fields to simulate an environment with a high feeling of presence and immersion. Nonetheless, one main issue when designing such environments is to provide user interactions that are adapted to the tasks performed by the users. Thus, we propose here a task-centred methodology to design and evaluate these user interactions. Our methodology allows for the determination of user interaction designs based on previous VR studies, and for user evaluations based on a task-related computation of usability. Here, we applied it on the hazard identification case study, since VR can be used in a preventive approach to improve worksite safety. Once this task and its related user interactions were analysed with our methodology, we obtained two possible designs of interaction techniques for the worksite exploration subtask. About their usability evaluation, we proposed in this study to compare our task-centred evaluation approach to a non-task-centred one. Our hypothesis was that our approach could lead to different interpretations of user study results than a non-task-centred one. Our results confirmed our hypothesis by comparing weighted usability scores from our task-centred approach to unweighted ones for our two interaction techniques.

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.

The ability to interact with virtual objects using gestures would allow users to improve their experience in Mixed Reality (MR) environments, especially when they use AR headsets. Today, MR head-mounted displays like the HoloLens integrate hand gesture based interaction allowing users to take actions in MR environments. However, the proposed interactions remain limited. In this paper, we propose to combine a Leap Motion Controller (LMC) with a HoloLens in order to improve gesture interaction with virtual objects. Two main issues are presented: an interactive calibration procedure for the coupled HoloLens-LMC device and an intuitive hand-based interaction approach using LMC data in the HoloLens environment. A set of first experiments was carried out to evaluate the accuracy and the usability of the proposed approach.