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Summary & Details

Development of rupture prediction method for dissimilar material joints using machine learning

機械学習を利用した異種材接合の破断予測手法の開発

Delivery: Available on this site

Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20264079

Paper/Info type: Symposium Text

No.13-25

Pages: 110-119(Total 10 p)

Date of publication: Jan 2026

Publisher: JSAE

Language: Japanese

Event: JSAE Symposium 2025

Detailed Information

Category(J)	PPT資料 Translation
Category(E)	PPT slides
Author(J)	1) 伊藤里沙子, 2) 佐藤浩史, 3) 石橋慶輝, 4) 井原久, 5) 登坂泰浩
Author(E)	1) Risako Ito, 2) Hiroshi Sato, 3) Yoshiteru Ishibashi, 4) Hisashi Ihara, 5) Yasuhiro Tosaka
Affiliation(J)	1) 日産自動車株式会社, 2) 日産自動車株式会社, 3) 日産自動車株式会社, 4) 日産自動車株式会社, 5) 日産自動車株式会社
Affiliation(E)	1) Nissan Motor Co., Ltd., 2) Nissan Motor Co., Ltd., 3) Nissan Motor Co., Ltd., 4) Nissan Motor Co., Ltd., 5) Nissan Motor Co., Ltd.
Abstract(J)	FDS接合は、アルミ材同士の接合やアルミ―鉄鋼材などの異材接合も可能なため採用例が増している。本報は、CAEでの衝突現象の再現を目的にテストピース試験結果を機械学習したモデルを作成し、破断荷重・破壊モード推定に利用・精度の検証をした事例を報告する。 Translation
Abstract(E)	FDS joint is increasingly being used because it can be used to joint aluminum materials to each other and to joint different materials such as aluminum and steel materials.In this report, we report a case study in which a machine learning model was created based on the result of test piece for the purpose of reproducing the collision phenomenon in CAE, and the accuracy was verified for estimating the breaking load and failure mode.

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