An Augmented Reality inspection tool to support workers in Industry 4.0 environments

• Published in: Computers in industry 2021-05, Vol.127, p.103412, Article 103412
• Main Authors: Marino, Emanuele, Barbieri, Loris, Colacino, Biagio, Fleri, Anna Kum, Bruno, Fabio
• Format: Article
• Language: English
• Subjects: Augmented Reality; Google ARCore; Industry 4.0; Marker-based tracking; Operator 4.0; Usability studies
• ISSN: 0166-3615; 1872-6194
• DOI: 10.1016/j.compind.2021.103412

•AR inspection tool to support workers at the workplace in Industry 4.0 contexts.•AR tool based on Goole ARCore to perform hybrid-tracking and run on Android devices.•Users can easily detect design discrepancies and add 3D notes to 3D models.•End-users’ evaluation on a real case study through SUS and NASA TLX questionnaires. Among the key technologies of Industry 4.0, Augmented Reality (AR) is one of the most promising and enabling technologies for supporting factory workers and engineers at the workplace. To this end, the paper proposes a novel AR tool to assist operators during the inspection activities for the detection of production and assembly errors. In fact, thanks to the superimposition of the 3D models, as designed by the technical office, a worker can easily detect the presence of design discrepancies on the final physical assembled product and report them by adding 3D annotations directly on virtual models. This AR tool has been developed by using ARCore™ libraries to ensure, in the first place, its compatibility with commonly used devices for which workers are already trained and, secondly, to take advantage of the hybrid-tracking techniques that combine vision- and sensor-based methods to improve the reliability of the AR visualization. Nevertheless, the proposed AR tool adopts multiple markers to minimize tracking errors and therefore to provide greater freedom of movement to the user, who can use the tool also for the assessment of large-size products. Field experimentations have been carried out on a real case study with end-users in order to assess its usability and perceived mental workload through the SUS (System Usability Scale) and NASA-TLX (Task Load Index) standard questionnaires, respectively. The usability study was performed taking into account also objective metrics, i.e., by analysing user performance in target acquisition tasks while interacting with the AR tool. Statistical analysis proved that the adoption of this AR tool requires low mental demand, and its usability has reached a high level of satisfaction both by the factory workers and engineers involved in the user study.