A Machine Learning Approach to Estimate Tire Block Force Spectrum for Road Noise Simulation
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- 形態
- 価格
- 一般価格(税込):¥1,100 会員価格(税込):¥880
- 文献番号
- 20265026
- 文献・情報種別
- 学術講演会予稿集(春)
No.6-26
- 掲載ページ
- 1-5(Total 5 p)
- 発行年月
- 2026年 5月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- 2026年春季大会
書誌事項
| 著者 | 1) Yonghun Kim, 2) Taeyoung Kim, 3) Hyunseok Kang |
|---|---|
| 著者(英) | 1) Yonghun Kim, 2) Taeyoung Kim, 3) Hyunseok Kang |
| 勤務先 | 1) Hankook Tire, 2) Hankook Tire, 3) Hankook Tire |
| 勤務先(英) | 1) Hankook Tire, 2) Hankook Tire, 3) Hankook Tire |
| 抄録(英) | To predict vehicle road noise using the Frequency Based Sub-structuring (FBS) method or to operate a virtual noise simulator, accurate tire block forces are essential. Prior to manufacturing a physical tire, these forces are commonly estimated through finite-element (FE) analysis; however, FE simulations typically require several days, making it difficult to rapidly assess the impact of design modifications on road-noise performance. To overcome this limitation, this study proposes a data-driven modeling framework capable of predicting the tire block-force spectrum directly from design specifications. The methodology, including feature construction, model training, and validation procedures, is described in detail, and the applicability of the proposed approach is demonstrated through prediction results. 翻訳 |