IEEE Access (Jan 2023)
Development and Evaluation of Augmented Reality Learning Content for Pneumatic Flow: Case Study on Brake Operating Unit of Railway Vehicle
Abstract
The Brake operating unit (BOU) of a railway vehicle is one of the important systems for controlling the braking of the train. Because this system uses compressed air, it is difficult to understand and train the system. The existing education method involves learning the pneumatic flow of various control air in a 2D pneumatic circuit diagram based on a maintenance manual. However, in the actual braking system, it was difficult to learn effectively because the air flows in 3D. In order to solve these problems, the improvement of the training technique using the new 3D augmented reality (AR) was performed. In this study, to increase the learning effect of air brake flow, a technique for simultaneously displaying the pneumatic flow in 2D circuit diagram and 3D model was proposed. First, the distance ratio for simultaneous display can be determined using the proposed streamline matching variable calculation algorithm (SLMVC) that uses position and animation duration as input variables. Second, to avoid the complexity of using the 24 variables of the Particle System module in Unity, an existing universal 3D platform, a continuously emission property correction algorithm (CEPC) that can output particle objects as a streamline using only 4 properties (e.g., start lifetime, start speed, emission rate over time, start delay). As a result, the following 6 different types of BOU air pressure could be simultaneously displayed in 2D and 3D (e.g., AC, BC, SR, SBR, AS1, AS2). Therefore, maintenance staff can effectively learn complex pneumatic flow. To verify the usability of the developed content, a survey using the NASA-TLX technique was conducted targeting 60 maintenance staff. As a result of the comparison between Group A using the existing maintenance manual and Group B using the developed AR content, the perceived workload decreased by 28%. In particular, the frustration part decreased by 64% and the performance part decreased by 62%, indicating that the usability of AR content was very good.
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