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Allying topology and shape optimization through machine learning algorithms

Article dans une revue avec comité de lecture
Author
MUÑOZ, D.
300772 Universitat Politècnica de València [UPV]
NADAL, E.
300772 Universitat Politècnica de València [UPV]
ALBELDA, J.
300772 Universitat Politècnica de València [UPV]
CHINESTA, F.
86289 Laboratoire Procédés et Ingénierie en Mécanique et Matériaux [PIMM]
RÓDENAS, J.J.
300772 Universitat Politècnica de València [UPV]

URI
http://hdl.handle.net/10985/22239
DOI
10.1016/j.finel.2021.103719
Date
2022-07
Journal
Finite Elements in Analysis and Design

Abstract

Structural optimization is part of the mechanical engineering field and, in most cases, tries to minimize the overall weight of a given design domain, subjected to functionality constraints given in terms of stresses of displacements. The most relevant techniques are topology and shape optimization. Topology optimization provides the optimal material distribution layout into a given, static, design domain. On the other hand, shape optimization provides the optimal combination of the parameters that define the required parametrization of the domain's boundary. Both techniques have strengths and weaknesses, thus a hybrid optimization approach that combines the former techniques will define a more general structural optimization framework that will take advantage of their synergistic combination. The difficulty arises when communicating both techniques for which, in this paper, we propose a machine learning-based methodology.

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