## 8 Conclusions  
In this study, AR content was developed to maximize the efficiency of maintenance personnel training for air springs. A screen rotation algorithm was used to enhance the visibility of the content, while the FDG algorithm provided interaction between the user and the leakage test. The user experience evaluation of the developed content was conducted through task performance assessments and post-survey tools. The conclusions drawn from the research are as follows:  
1. For practical training, a document-based maintenance procedure for an air spring, which is not easily disassembled and maintained, was developed into highly detailed and immersive content on mobile devices using the general-purpose Unity program. This content was designed to be easily implemented offline, closely resembling actual maintenance work. The content was created to allow feedback on the maintenance process and evaluation. It was also designed with an MAR system considering the maintenance work environment, ensuring that there are no restrictions in the railway vehicle maintenance setting. 2. To simulate the rubber air spring's operation driven by compressed air, the previously developed SLMVC and CEPC algorithms were supplemented with the newly added FDG and OAR algorithms. The FDG algorithm is used to implement the effects of air spring damage and leakage inspection, while the OAR algorithm is employed to enhance visibility for maintenance tasks, such as visual inspection of damaged areas. 3. After subdividing the air spring maintenance work by creating a mind map, a storyboard was created, and based on this scenario, an external condition inspection, air spring height inspection, and leakage test were produced. First, the maintenance tools required for maintenance work were displayed through images, and each maintenance step was implemented as an animation, with highlights applied to areas that required inspection or were for worker safety. In addition, standard dimensions or contents for maintenance explanations were visualized and delivered in text. 4. To analyze the effectiveness of the content incorporating these algorithms, two surveys were conducted targeting railway vehicle maintenance personnel. As a result, the UMUX survey received an average score of 81.56 across four items, with the most positive response being for U1, \"This AR content's capabilities meet my requirements\" (85.00). This indicates that not only the touchscreen gesture method but also the OAR algorithm improved the ease of object manipulation. 5. Additionally, the CSUQ received an average score of 80.83 across the 19 items in the four categories, corresponding to a grade of A. Among the categories, \"System Usefulness\" had the highest score of 84.95. This reflects that the combined use of the newly added FDG and OAR algorithms with the existing SLMVC and CEPC algorithms provides an efficient, comfortable, and quick way to enhance productivity during tasks. 6. The UMUX and CSUQ survey results indicate that the AR content significantly improves learning efficiency and usability compared to the traditional document manual. Group B, using the AR content, showed a 10% improvement in learning efficiency and a 16% enhancement in overall usability. However, while the AR system demonstrated clear advantages in ease of use, system usefulness, and information quality, there are areas needing improvement, particularly in information recovery and interface functionality. These findings suggest that the AR content is highly effective, though further refinement is needed to fully meet user expectations. In conclusion, the developed content represents an innovative educational system that deepens and visualizes the specific characteristics of railway vehicle maintenance beyond conventional manuals. Future research should focus on preserving critical physical properties and structures to enhance the robustness of numerical methods.