A Self-Evolving Design Exploration System with Performance-Sensitivity-Guided Latent Space Expansion
性能感度に基づいて潜在空間拡張する自己成長AIの開発とブラケット設計への適用
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266107
- Paper/Info type
- Proceedings (Autumn)
No.114-26
- Pages
- 1-4(Total 4 p)
- Date of publication
- Oct 2026
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2026 JSAE Annual Congress (Autumn)
Detailed Information
| Author(J) | 1) 河村 樹人, 2) 寺部 俊紀, 3) 神庭 千佳, 4) 大河 誠, 5) 佐野 拓生 |
|---|---|
| Author(E) | 1) Mikito Kawamura, 2) Toshiki Terabe, 3) Chika Kanba, 4) Makoto Okawa, 5) Takumi Sano |
| Affiliation(J) | 1) トヨタ自動車, 2) トヨタ自動車, 3) トヨタ自動車, 4) トヨタ自動車, 5) トヨタ自動車 |
| Affiliation(E) | 1) Toyota Motor, 2) Toyota Motor, 3) Toyota Motor, 4) Toyota Motor, 5) Toyota Motor |
| Abstract(J) | ブラケット設計ではNV，熱応力等の複数性能を同時に満たしつつ，設計期間を短縮する必要がある．しかし従来のCAEを用いた設計手法は試行回数に依存するため設計期間短縮は難しい．本研究ではサロゲート，形状生成AIをCAEと組み合わせ，エキマニ用ブラケット設計の探索を高速化し，その効果と運用課題を報告する． Translation |
| Abstract(E) | In bracket design, reducing the development cycle while simultaneously satisfying multiple performance criteria such as noise and vibration (NV) and thermal stress is essential. Conventional CAE-driven design workflows, however, depend heavily on iterative trial-and-error evaluations and thus provide limited scope for time reduction. This study expedites the exploration of exhaust manifold bracket designs by integrating surrogate modeling and generative shape optimization with CAE simulations. We demonstrate that the combined approach significantly accelerates design space exploration and improves solution discovery, and we discuss the method’s practical benefits, limitations, and operational challenges encountered during implementation. |
