Target Cascading of Tire Performances Using Machine Learning
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.):¥1,100 Members (tax incl.):¥880
- Publication code
- 20266110
- Paper/Info type
- Proceedings (Autumn)
No.114-26
- Pages
- 1-4(Total 4 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- English
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) Yeonsang Yoo, 2) Benjamin Schäfer, 3) Yongdae Kim, 4) Jinsil Kyeong |
|---|---|
| Author(E) | 1) Yeonsang Yoo, 2) Benjamin Schäfer, 3) Yongdae Kim, 4) Jinsil Kyeong |
| Affiliation(J) | 1) Hyundai Motor, 2) RWTH Aachen University, 3) Hyundai Motor, 4) Hyundai Motor |
| Affiliation(E) | 1) Hyundai Motor, 2) RWTH Aachen University, 3) Hyundai Motor, 4) Hyundai Motor |
| Abstract(E) | The automotive industry is shifting towards Model-Based Systems Engineering (MBSE) to manage increasing complexity in development. However, virtual tire development has a significant limitation: setting tire performance targets in early stage is difficult without physical testing. To overcome this situation, this study proposes a Target Cascading (T/C) process to derive tire performance targets directly from vehicle performance requirements and validated the process. Previously developed performance prediction models were improved for refined T/C process. Additionally, a GUI was developed to integrate the prediction models and T/C process for user convenience. |