Evaluation of simplified 3D CAD data for conveying industrial assembly instructions via Augmented reality - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

Evaluation of simplified 3D CAD data for conveying industrial assembly instructions via Augmented reality

Résumé

Augmented Reality (AR) based training is gaining momentum in industrial sectors, particularly in assembly and maintenance. Generally, the media contents used to create AR assembly instructions include audio, video, images, text, signs, 3D data, and animations. Literature suggests that 3D CAD-based AR instructions spatially registered with the real-world environment are more effective and produce better training results. However, storing, processing, and rendering 3D data can be challenging even for state-of-the-art AR devices like HoloLens2, particularly in industrial usage. To overcome these concerns, heavy 3D models can be simplified to a certain extent with a minimal impact on the user experience, that is, the quality of visualization in AR. In the present paper, we evaluate the usability of a set of simplified 3D CAD models used to convey manual assembly information to novice operators. The experiment included 14 participants, six assembly operations, and two sets of 3D CAD models (i.e., originals and simplified) and was conducted in a laboratory setting. To simulate as much as possible a real-world assembly scenario, the components, and the original corresponding 3D CAD models were obtained from a real-world industrial setup. The present paper confirms that simplified 3D CAD models can replace the original 3D CAD models within AR applications without affecting the user experience with the help of subjective evaluations.

Dates et versions

hal-03930240 , version 1 (09-01-2023)

Identifiants

Citer

Abhaya Dhathri Arige, Traian Lavric, Marius Preda, Titus Zaharia. Evaluation of simplified 3D CAD data for conveying industrial assembly instructions via Augmented reality. Web3D '22: The 27th International Conference on 3D Web Technology, Nov 2022, Evry-Courcouronnes France, France. pp.1-6, ⟨10.1145/3564533.3564568⟩. ⟨hal-03930240⟩
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