Citation

Sugano C, Tamura Y, Komamura T. Trans. Soc. Automot. Eng. Jpn. 2022; 53(2): 258-263.

Vernacular Title

衝突解析におけるロバスト設計手法の開発 －パターン認識と機械学習を活用した良品条件の可視化－

Copyright

(Copyright © 2022, Society of Automotive Engineers of Japan)

DOI

10.11351/jsaeronbun.53.258

PMID

unavailable

Abstract

This paper describes a methodology for robust design in crash analysis. We organized a series of robust design processes for crash analysis and showed that it is possible to classify/predict deformation patterns, judge whether they are good or not, identify contributing parameters by applying statistical/machine learning methods in each process. By using a prediction model as a surrogate model, we achieved an extensive search in the design space in a short time.

===

衝突解析における一連のロバスト設計プロセスを整理し，統計/機械学習を用いた変形パターンの分類/予測，良否判定，寄与因子の特定が可能であることを示した．

Language: ja

Keywords

collision safety; machine learning; rear end collision; robust design; vehicle development