Augmented instructions : analysis of performance and efficiency of assembly tasks

Augmented Reality (AR) technology makes it possible to present information in the user’s line of sight, right at the point of use. This brings the capability to visualise complex information for industrial maintenance applications in an effective manner, which typically rely on paper instructions and tacit knowledge developed over time. Existing research in AR instruction manuals has already shown its potential to reduce the time taken to complete assembly tasks, as well as improving accuracy [1–3]. In this study, the outcomes of several aspects of AR instructions are explored and their effects on the chosen Key Performance Indicators (KPIs) of task completion time, error rate, cognitive effort and usability are assessed. A standardised AR assembly task is also described for performance comparison, and a novel AR experimental tool is presented, which takes advantage of the flexibility of internet connected peripherals, to explore various different aspects of AR app design to isolate their effects. Results of the experiments are given here, providing insight into the most effective way of delivering information and promoting interaction between user and computer, in terms of user performance and acceptance

Preliminary Design of Reluctance Motors for Light Electric Vehicles Driving

The paper presents the aspects regarding FEM analysis of a reluctant motor for direct driving of the light electric vehicles. The reluctant motor take into study is of special construction suitable for direct drive of a light electric vehicle. It is an inverse radial reluctant motor, with a fixed stator mounted on front wheel shaft and an external toothed rotor fixed on the front wheel itself. A short presentation of preliminary design is continued with the FEM analysis in order to provide the optimal geometry of the motor and adequate windings