Geodesic
A Low-Cost AR Training System for Manual Assembly Operations
Computer Science and Information Systems, Tome 19 (2022) no. 2 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

Voir la notice de l'article

This research work aims to provide an AR training system adapted to industry, by addressing key challenges identified during a long-term case study conducted in a boiler-manufacturing factory. The proposed system relies on low-cost visual assets (i.e. text, image, video and predefined auxiliary content) and requires solely a head-mounted display (HMD) device (i.e. Hololens 2) for both authoring and training. We evaluate our proposal in a real-world use case by conducting a field study and two field experiments, involving 5 assembly workstations and 30 participants divided into 2 groups: (i) low-cost group (G-LA) and (ii) computer-aided design (CAD)-based group (G-CAD). The most significant findings are as follows. The error rate of 2.2% reported by G-LA during the first assembly cycle (WEC) suggests that low-cost visual assets are sufficient for effectively delivering manual assembly expertise via AR to novice workers. Our comparative evaluation shows that CAD-based AR instructions lead to faster assembly (-7%, -18% and -24% over 3 assembly cycles) but persuade lower user attentiveness, eventually leading to higher error rates (+38% during the WEC). The overall decrease of the instructions reading time by 47% and by 35% in the 2 nd and 3 rd assembly cycles, respectively, suggest that participants become less dependent on the AR instructions rapidly. By considering these findings, we question the worthiness of authoring CAD-based AR instructions in similar industrial use cases.
Keywords: augmented reality, training, authoring tool, work instructions, industry 4.0, assembly 
@article{CSIS_2022_19_2_a25,
     author = {Traian Lavric and Emmanuel Bricard and Marius Preda and Titus Zaharia},
     title = {A {Low-Cost} {AR} {Training} {System} for {Manual} {Assembly} {Operations}},
     journal = {Computer Science and Information Systems},
     year = {2022},
     volume = {19},
     number = {2},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a25/}
}
TY  - JOUR
AU  - Traian Lavric
AU  - Emmanuel Bricard
AU  - Marius Preda
AU  - Titus Zaharia
TI  - A Low-Cost AR Training System for Manual Assembly Operations
JO  - Computer Science and Information Systems
PY  - 2022
VL  - 19
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a25/
ID  - CSIS_2022_19_2_a25
ER  - 
%0 Journal Article
%A Traian Lavric
%A Emmanuel Bricard
%A Marius Preda
%A Titus Zaharia
%T A Low-Cost AR Training System for Manual Assembly Operations
%J Computer Science and Information Systems
%D 2022
%V 19
%N 2
%U http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a25/
%F CSIS_2022_19_2_a25
Traian Lavric; Emmanuel Bricard; Marius Preda; Titus Zaharia. A Low-Cost AR Training System for Manual Assembly Operations. Computer Science and Information Systems, Tome 19 (2022) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a25/