Geodesic
A Low-Cost AR Training System for Manual Assembly Operations
Computer Science and Information Systems, Tome 19 (2022) no. 2.

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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},
     publisher = {mathdoc},
     volume = {19},
     number = {2},
     year = {2022},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2022_19_2_a25/}
}
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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/