A Low-Cost AR Training System for Manual Assembly Operations

Traian Lavric1, 2, Emmanuel Bricard1, Marius Preda2 and Titus Zaharia2

  1. IP Paris - Telecom SudParis
    91011, Evry, France
    {traian.lavric, marius.preda, titus.zaharia}@telcom-sudparis.eu
    93700, Drancy, France
    {traian.lavric, emmanuel.bricard}@fr.bosch.com


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.

Key words

augmented reality, training, authoring tool, work instructions, industry 4.0, assembly

Digital Object Identifier (DOI)


Publication information

Volume 19, Issue 2 (June 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Lavric, T., Bricard, E., Preda, M., Zaharia, T.: A Low-Cost AR Training System for Manual Assembly Operations. Computer Science and Information Systems, Vol. 19, No. 2, 1047–1073. (2022), https://doi.org/10.2298/CSIS211123013L