Efficient Machine Learning Optimization of Gear Micro Geometry and Comparison with Manually Designed Gears
- 提供方法
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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
- 20265027
- 文献・情報種別
- 学術講演会予稿集(春)
No.6-26
- 掲載ページ
- 1-8(Total 8 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) Andrew Wild, 2) Simon Terry, 3) Paul Langlois, 4) Jaekwon Lee, 5) Shinya Sudo |
|---|---|
| 著者(英) | 1) Andrew Wild, 2) Simon Terry, 3) Paul Langlois, 4) Jaekwon Lee, 5) Shinya Sudo |
| 勤務先 | 1) SMT, 2) SMT, 3) SMT, 4) Hino Motors, 5) Hino Motors |
| 勤務先(英) | 1) SMT, 2) SMT, 3) SMT, 4) Hino Motors, 5) Hino Motors |
| 抄録(英) | Optimisation of gear micro geometry guided by machine-learnt surrogate models is compared to a past engineer-led optimisation and proven to be a highly efficient technique. Subject to engineer-defined objectives, the optimiser identifies high-performing micro geometries with no further human intervention, with all predictions automatically validated by a hybrid Hertzian and FE-based loaded tooth contact analysis. Going beyond the scope of the original manual optimisation, the optimiser is used to identify micro geometries that are torque robust or retain similar performance to the specification without using different micro geometries on the two input gears, thereby theoretically reducing manufacturing costs. 翻訳 |