ACO topology optimization: The geometrical constraint by learning overlaid optimal ants route

Nanami Hoshi, Hiroshi Hasegawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Topology optimization commonly has performed minimization of the mean compliance under a volume constraint. On the other hand, mechanical product designers are considering "a weight minimization under a stress constraint" as an objective and constraints for generating new optimal structure. Moreover, for obtaining this objective, a mechanical structure design has performed to minimize weight of its structure by checking the principal stress vectors as the force's flow, and speculating its desirable structure under maintaining its stiffness, iteratively. These design processes' difference has generated mismatch between actual design practice and the conventional topology optimization theory. Therefore, we have proposed ACO using the principal stress vector for overcoming mismatch of the topology optimization theory. In this paper, ACO Topology Optimization with Geometrical Constraint (ACTO with GC) is proposed to improve unnecessary structures elements problem. Our proposal is new geometrical constraint method which overlays obtained optimal ants route as the shape feature pattern, learns it for next optimization process.

本文言語English
ホスト出版物のタイトル16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017
出版社CAL-TEK S.r.l.
ページ68-73
ページ数6
ISBN(電子版)9781510847705
出版ステータスPublished - 2017 1 1
イベント16th International Conference on Modeling and Applied Simulation, MAS 2017 - Barcelona, Spain
継続期間: 2017 9 182017 9 20

Other

Other16th International Conference on Modeling and Applied Simulation, MAS 2017
国/地域Spain
CityBarcelona
Period17/9/1817/9/20

ASJC Scopus subject areas

  • モデリングとシミュレーション

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