## 1 Introduction
### Research Objectives  
The application of AR technology for railway vehicle maintenance is currently limited to key mechanical equipment such as the bogie frame and braking system ([PERSON] et al., 2020; [PERSON] et al., 2020). Existing AR content utilizes touchscreen gestures to control position, size, and rotation, achieving intuitive learning effects by developing algorithms to represent pneumatic flow diagrams related to compressed air systems, which are challenging to identify with the naked eye. However, when developing AR for air springs, the use of existing gestures was limited in usability due to the characteristics of the axisymmetric model. Additionally, while the existing Stream Line Matching Variable Calculation (SLMVC) algorithm and Continuously Emission Property Correction (CEPC) algorithm can represent compressed air system flow, these algorithms struggle to depict diffusion phenomena such as leakage ([PERSON] et al., 2020). This study aims to develop AR training content for maintenance, connected to a database and storyboard, centered on the 3D model of an air spring device that enhances vehicle ride comfort by adjusting air pressure. Additionally, the study aims to facilitate onsite maintenance of air spring devices and deliver efficient training by accomplishing the following objectives:  
First, it is necessary to develop MAR content that includes specifications, detailed structures, actual work procedures, and supplementary photos and videos to assist with onsite tasks. This will replace the inefficient paper-based theoretical and practical training. The content should take into account the restricted maintenance environment of the railroad vehicle air springs installed under the vehicle and ensure easy portability and visibility. Second, to improve user visibility and facilitate maintenance tasks, it is crucial to utilize the current gesture method and create a 3D object auto-rotation algorithm. Furthermore, new algorithms should be developed by integrating existing ones to replicate fluid flow and visualize air leakage. This will enhance maintenance support and training efficiency. Third, the maintenance process of the air spring should be analyzed to include visual inspection, height measurement, leakage inspection, and repair. The essential maintenance tools should be readily available during maintenance tasks, and procedures should be presented in an animated format to enhance the practical maintenance training scope. The usability of AR content can be evaluated through various methods. Types of surveys include SUS, CSUQ, UMUX, and Questionnaire for User Interface Satisfaction (QUIS). Previous studies have used UMUX and CSUQ survey methods for user experience evaluation to objectively assess the effectiveness, efficiency, and usability of AR systems developed across various fields. The UMUX and CSUQ are widely used metrics for evaluating the usability of systems, including educational content developed using AR. UMUX provides a quick assessment of perceived usability, while CSUQ offers a more detailed evaluation of system usefulness, information quality, and interface quality. Combined, these tools offer a comprehensive framework for assessing the usability of AR-based educational products and can serve as objective measures in usability evaluation ([PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020; [PERSON] et al., 2020). This study similarly employs UMUX and CSUQ surveys to conduct an objective evaluation of user experience [20; 21; 22; 23]. The usability of the developed air spring AR content and document manual was evaluated by surveying 80 professionals involved in railway vehicle maintenance using the UMUX and a 19-question CSUQ tool.