| 제목 |
AI Analysis Using the Collision Test Data of the Urban Railroad Vehicle’s
Rubber Shock Absorber |
| 분야 |
기타 |
| 언어 |
English |
| 저자 |
임성현(기술보증기금, 한국철도기술연구원), 송창희(서울대학교), 이태형(한국철도기술연구원), 박영일(서울과학기술대학교), 지용현(서울과학기술대학교) |
| Key Words |
Artificial intelligence, Collision, Modeling, Railroad vehicle, Rubber absorber |
| 초록 |
Railway vehicles are characterized by a device that connects the locomotive and the carriages. Thus, it is necessary to withstand the possible impact of this connector, and protect the passengers. To absorb the impact energy, a damping system was applied to the railroad car connector. Actual experiments are most important in confirming the performance of the connector. However, since it is costly and risky, we simulated it. In this study, the degree of impact is calculated by inputting the impact test data of the shock absorber by using artificial intelligence. Based on the results of the collision test of the connector for railroad vehicles that was performed at the crash test site, we will proceed with the modeling for each unit, and develop an artificial intelligence model for predicting the degree of impact through artificial intelligence. The data that must be dealt with in this study include the rubber buffer(EGF3). They are extracted from the previous crash test, and include information on the weight of the vehicle, the speed of the collision, and the displacement of the shock absorber that occurs during a collision. Based on the analysis, it was confirmed that the target for the validation data and the prediction related to it showed similar tendencies. This is an initial study in which artificial intelligence is applied to the rubber shock absorber model of railroad cars. |
| 원문(PDF) |
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