Study on Strength Prediction of 6000 Series Aluminum Alloys Using Machine Learning and its Application to Crash Simulation (Second Report)
機械学習を用いた6000系アルミ合金のBH後の耐力予測に関する研究（第2報）
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20266194
- Paper/Info type
- Proceedings (Autumn)
No.133-26
- 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) 永井 潤哉, 2) 青野 健太郎, 3) 根川 亮佑, 4) 高見 大輝 |
|---|---|
| Author(E) | 1) Junya Nagai, 2) Kentarou Aono, 3) Ryousuke Negawa, 4) Hiroki Takami |
| Affiliation(J) | 1) SUBARU, 2) SUBARU, 3) SUBARU, 4) SUBARU |
| Affiliation(E) | 1) SUBARU, 2) SUBARU, 3) SUBARU, 4) SUBARU |
| Abstract(J) | 自動車パネルに使用される6000系アルミ合金は成形ひずみや塗装焼付時に硬化=ベークハード（BH）し，各条件の組み合わせで耐力が決まる．第1報でBH後耐力の予測技術確立を試み，今回はCAEモデルへの適用と予測精度への影響を調査した． Translation |
| Abstract(E) | The purpose of this research is to use machine learning to predict the strength of an actual vehicle after 6000 series aluminum alloy, which has age-hardening characteristics, has hardened due to heat during the paint baking process, and to apply this to a crash simulation to improve the accuracy of performance predictions by making the model more accurate to the actual vehicle. |
