Target Cascading of Tire Performances Using Machine Learning
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20266110
- 文献・情報種別
- 学術講演会予稿集(秋)
No.114-26
- 掲載ページ
- 1-4(Total 4 p)
- 発行年月
- 2026年 10月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2026年秋季大会
書誌事項
| 著者 | 1) Yeonsang Yoo, 2) Benjamin Schäfer, 3) Yongdae Kim, 4) Jinsil Kyeong |
|---|---|
| 著者(英) | 1) Yeonsang Yoo, 2) Benjamin Schäfer, 3) Yongdae Kim, 4) Jinsil Kyeong |
| 勤務先 | 1) Hyundai Motor, 2) RWTH Aachen University, 3) Hyundai Motor, 4) Hyundai Motor |
| 勤務先(英) | 1) Hyundai Motor, 2) RWTH Aachen University, 3) Hyundai Motor, 4) Hyundai Motor |
| 抄録(英) | 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. 翻訳 |