https:\/\/webconf.imt.fr\/frontend\/tit-1wi-wex-o6m<\/a><\/p>\n\n\n\nMembres du jury :<\/strong><\/p>\n\n\n\nM. Titus ZAHARIA<\/strong>, Professeur, T\u00e9l\u00e9com SudParis, FRANCE – Directeur de th\u00e8se
M. Marius PREDA<\/strong>, Ma\u00eetre de conf\u00e9rences, Telecom Sudparis, FRANCE – Co-encadrant de th\u00e8se
M. Jean-Yves DIDIER<\/strong>, Ma\u00eetre de conf\u00e9rences, Universit\u00e9 d’Evry-val d’Essonne – Universit\u00e9 Paris-Saclay, FRANCE – Rapporteur
M. Thierry DUVAL <\/strong>, Professeur, IMT Atlantique, FRANCE – Rapporteur
M. Fabien DUMON<\/strong>, Lead of Airbus Holographic Academy – Defence & Space, Airbus Defence & Space, FRANCE – Examinateur
Mme Laurence NIGAY<\/strong>, Professeure des universit\u00e9s, Universit\u00e9 Grenoble Alpes, FRANCE – Examinatrice<\/p>\n\n\n\n
R\u00e9sum\u00e9 :<\/strong><\/p>\n\n\n\nR\u00e9sum\u00e9 : Pour rester comp\u00e9titifs dans la r\u00e9volution industrielle en cours, c’est-\u00e0-dire l’industrie 4.0, les secteurs manufacturiers doivent assurer une grande flexibilit\u00e9 au niveau de la production, un besoin mieux satisfait par des travailleurs qualifi\u00e9s. Alors que la formation traditionnelle devient de plus en plus inefficace, trouver une meilleure fa\u00e7on de former les travailleurs novices devient une exigence critique. La litt\u00e9rature sugg\u00e8re que la r\u00e9alit\u00e9 augment\u00e9e (RA), une technologie \u00e9mergente propos\u00e9e par l’industrie 4.0, peut potentiellement r\u00e9pondre \u00e0 cette question. Les avantages des outils de partage des connaissances bas\u00e9s sur la RA ont \u00e9t\u00e9 d\u00e9montr\u00e9s dans divers domaines, y compris l’industrie, de la fabrication \u00e0 la validation et \u00e0 la maintenance. Cependant, malgr\u00e9 les progr\u00e8s de la RA ces derni\u00e8res ann\u00e9es, aucune perc\u00e9e industrielle significative n’est \u00e0 noter. Nous avons constat\u00e9 que la plupart des syst\u00e8mes RA sont \u00e9labor\u00e9s et \u00e9valu\u00e9s dans des environnements contr\u00f4l\u00e9s, sans l’implication des \u00e9ventuels utilisateurs finaux. Guid\u00e9s par les recommandations de la litt\u00e9rature, nous avons men\u00e9 une \u00e9tude de cas au long cours dans une usine d’assemblage manuel, afin d’identifier les besoins et les attentes auxquels un syst\u00e8me de formation en RA devrait r\u00e9pondre, pour adresser de mani\u00e8re optimale le secteur industriel consid\u00e9r\u00e9. De plus, nous avons men\u00e9 une analyse approfondie de la repr\u00e9sentation et de la transmission de l’information dans la RA, en ce qui concerne les implications cognitives et les efforts de cr\u00e9ation de contenu. Nous avons \u00e9galement explor\u00e9 les paradigmes d’interaction homme-machine pour identifier les principes et les lignes directrices de conception pour l’\u00e9laboration d’un outil de RA d\u00e9di\u00e9 au contexte atelier. Nous avons constat\u00e9 que la repr\u00e9sentation visuelle de l’expertise d’assemblage en RA peut s’appuyer sur des \u00e9l\u00e9ments visuels peu co\u00fbteux (c’est-\u00e0-dire texte, photo, vid\u00e9o et donn\u00e9es auxiliaires pr\u00e9d\u00e9finies), tandis qu’une conception centr\u00e9e sur l’humain devrait \u00eatre adopt\u00e9e lors de l’\u00e9laboration du syst\u00e8me, en privil\u00e9giant l’utilisabilit\u00e9 et l’utilit\u00e9 plut\u00f4t que la performance. Nous avons d\u00e9fini une repr\u00e9sentation visuelle formalis\u00e9e des op\u00e9rations d’assemblage en RA, qui prend en compte les probl\u00e9matiques de cr\u00e9ation et soutient les performances de formation. Nous avons propos\u00e9 une approche de cr\u00e9ation immersive qui permet de capturer son expertise d’assemblage in-situ, lors de l’assemblage. La cr\u00e9ation des instructions en RA est un processus en une \u00e9tape, ne s’appuie pas sur des donn\u00e9es existantes ou des services externes et ne n\u00e9cessite pas d’expertise AR ou technique, de pr\u00e9- ou post-traitement des donn\u00e9es. Pendant la formation, les informations d’assemblage sont transmises en suivant le principe 2W1H, con\u00e7u pour guider les novices de mani\u00e8re naturelle et non intrusive, en minimisant les entr\u00e9es de l’utilisateur et l’encombrement de l’interface utilisateur, et en visant \u00e0 maximiser la compr\u00e9hension et l’apprentissage. Nous avons \u00e9valu\u00e9 notre proposition en menant plusieurs \u00e9tudes. La premi\u00e8re, men\u00e9e sur un poste de travail d’assemblage r\u00e9el, a confirm\u00e9 l’hypoth\u00e8se selon laquelle des \u00e9l\u00e9ments visuels \u00e0 faible co\u00fbt enregistr\u00e9s dans l’espace peuvent transmettre efficacement l’expertise d’assemblage manuel aux travailleurs novices dans une configuration industrielle. Les r\u00e9sultats de la deuxi\u00e8me \u00e9tude ont soutenu l’hypoth\u00e8se selon laquelle la valeur de la cr\u00e9ation d’instructions AR bas\u00e9es sur la CAO dans un contexte industriel similaire est discutable. Une derni\u00e8re \u00e9tude a prouv\u00e9 que le syst\u00e8me RA propos\u00e9, comprenant \u00e0 la fois la proc\u00e9dure de cr\u00e9ation et de formation, peut \u00eatre utilis\u00e9 efficacement par des novices en quelques minutes. Dans l’ensemble, les r\u00e9sultats rapport\u00e9s ont d\u00e9montr\u00e9 la usabilit\u00e9 et l’efficacit\u00e9 de l’approche de formation en RA propos\u00e9e, indiquant qu’une mise en \u0153uvre de syst\u00e8me similaire pourrait \u00eatre adopt\u00e9e avec succ\u00e8s dans les secteurs manufacturiers. Les travaux futurs devraient valider les r\u00e9sultats exp\u00e9rimentaux rapport\u00e9s dans des \u00e9valuations industrielles \u00e0 grande \u00e9chelle et proposer des modules \u00ab\u00a0intelligents\u00a0\u00bb (par exemple, validation d’assemblage) pour mieux aider les novices pendant leur formation et optimiser \u00e9galement la proc\u00e9dure de cr\u00e9ation.<\/p>\n\n\n\n
Abstract : \u00ab\u00a0Methodologies and tools for expert knowledge sharing in manual assembly industries by using augmented reality \u00ab\u00a0<\/strong><\/p>\n\n\n\nAbstract: To remain competitive in the ongoing industrial revolution (i.e., Industry 4.0), manufacturing sectors must ensure high flexibility at the production level, a need best addressed by skilled human workforce. As traditional training becomes increasingly inefficient, finding a better way of training novice workers becomes a critical requirement. Literature suggests that augmented reality (AR), an emerging technology proposed by Industry 4.0, can potentially address this concern. The benefits of AR-based knowledge sharing tools have been demonstrated in a variety of domains, including industry, from manufacturing to validation and maintenance. However, despite the progress of AR in recent years, no significant industrial breakthrough can be noted. We found that most AR systems are elaborated and evaluated under controlled settings, without the implication of the eventual end users. Guided by literature recommendations, we conducted a long-term case study in a manual assembly factory, to identify needs and expectations that an AR training system should meet, to optimally address the considered industrial sector. Further, we conducted an in-depth analysis on information representation and conveyance in AR, with respect to cognitive implications and content authoring efforts. We explored as well human-computer interaction paradigms to identify principles and design guidelines for elaborating an AR tool dedicated to the shop floor context. We found that the visual representation of the assembly expertise in AR can rely on spatially registered low-cost visual assets (i.e., text, photo, video, and predefined auxiliary data), while a human-centered design should be adopted during the elaboration of the AR system, prioritizing usability and usefulness rather than performance. We defined a formalized visual representation (i.e., 2W1H principle) of assembly operations in AR, that considers authoring concerns and supports training performance. We proposed an HMD-only immersive authoring that allows one to capture his assembly expertise in-situ, during the assembly itself. The authoring is a one-step process, does not rely on existing data or external services and does not require AR or technical expertise, pre- or post-processing of data. During training, the assembly information is conveyed via AR by following the 2W1H principle, designed to guide novice workers in a natural, non-intrusive manner, minimizing user input and UI clutter, and aiming to optimize comprehension and learning. We evaluated our proposal by conducting several experiments. The first, conducted on a real-world assembly workstation, confirmed the hypothesis that spatially registered low-cost visual assets can effectively convey manual assembly expertise to novice workers via AR in an industrial setup. The findings of the second experiment supported the assumption that the worthiness of authoring CAD-based AR instructions in similar industrial context is questionable. A final experiment proved that the proposed AR system, including both authoring and training procedures, can be used effectively by novices in a matter of minutes. The overall reported feedback demonstrated the usability and efficiency of the proposed AR training approach, indicating that a similar system implementation could be successfully adopted in shop floor environments. Future work should validate the reported experimental findings in large-scale industrial evaluations and propose reliable \u201cintelligent\u201d modules (e.g., assembly validation and feedback) to better assist novice workers during training and optimize the authoring procedure as well.<\/p>\n","protected":false},"excerpt":{"rendered":"
L’Ecole doctorale : Ecole Doctorale de l’Institut Polytechnique de Pariset le Laboratoire de recherche SAMOVAR – Services r\u00e9partis, Architectures, MOd\u00e9lisation, Validation, Administration des R\u00e9seaux pr\u00e9sentent l\u2019AVIS DE SOUTENANCE de Monsieur Traian LAVRIC Autoris\u00e9 \u00e0 pr\u00e9senter ses travaux en vue de l\u2019obtention du Doctorat de l’Institut Polytechnique de Paris, pr\u00e9par\u00e9 \u00e0 T\u00e9l\u00e9com SudParis en : Signal, 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