ACO topology optimization

The geometrical constraint by learning overlaid optimal ants route

Nanami Hoshi, Hiroshi Hasegawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017
PublisherCAL-TEK S.r.l.
Pages68-73
Number of pages6
ISBN (Electronic)9781510847705
Publication statusPublished - 2017 Jan 1
Event16th International Conference on Modeling and Applied Simulation, MAS 2017 - Barcelona, Spain
Duration: 2017 Sep 182017 Sep 20

Other

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

Fingerprint

Shape optimization
Stiffness

Keywords

  • Ant colony optimization
  • Mechanical structure design
  • Principal stress vector
  • Topology optimization

ASJC Scopus subject areas

  • Modelling and Simulation

Cite this

Hoshi, N., & Hasegawa, H. (2017). ACO topology optimization: The geometrical constraint by learning overlaid optimal ants route. In 16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017 (pp. 68-73). CAL-TEK S.r.l..

ACO topology optimization : The geometrical constraint by learning overlaid optimal ants route. / Hoshi, Nanami; Hasegawa, Hiroshi.

16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017. CAL-TEK S.r.l., 2017. p. 68-73.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hoshi, N & Hasegawa, H 2017, ACO topology optimization: The geometrical constraint by learning overlaid optimal ants route. in 16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017. CAL-TEK S.r.l., pp. 68-73, 16th International Conference on Modeling and Applied Simulation, MAS 2017, Barcelona, Spain, 17/9/18.
Hoshi N, Hasegawa H. ACO topology optimization: The geometrical constraint by learning overlaid optimal ants route. In 16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017. CAL-TEK S.r.l. 2017. p. 68-73
Hoshi, Nanami ; Hasegawa, Hiroshi. / ACO topology optimization : The geometrical constraint by learning overlaid optimal ants route. 16th International Conference on Modeling and Applied Simulation, MAS 2017, Held at the International Multidisciplinary Modeling and Simulation Multiconference, I3M 2017. CAL-TEK S.r.l., 2017. pp. 68-73
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