Unified structural optimization method using topology optimization and genetic algorithms

This paper presents a new structural design framework that incorporates the concept of topology optimization and genetic algorithms to improve the manufacturability and structural robustness of the optimal structure. The level set function is employed as a topological design variable to obtain clear...

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Autores principales:	DoeYoung HUR, Sunghoon LIM, Kazuhiro IZUI, Shinji NISHIWAKI
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2021
Materias:	manufacturability genetic algorithm topology optimization geometric constraint design optimization Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/269ce59711954b6e957711f9e5cbfa6f
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spelling	oai:doaj.org-article:269ce59711954b6e957711f9e5cbfa6f2021-11-29T06:07:03ZUnified structural optimization method using topology optimization and genetic algorithms2187-974510.1299/mej.21-00052https://doaj.org/article/269ce59711954b6e957711f9e5cbfa6f2021-06-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/8/3/8_21-00052/_pdf/-char/enhttps://doaj.org/toc/2187-9745This paper presents a new structural design framework that incorporates the concept of topology optimization and genetic algorithms to improve the manufacturability and structural robustness of the optimal structure. The level set function is employed as a topological design variable to obtain clear structural boundaries, and the manufacturability of the structure is mathematically defined based on the manufacturing directions and the fictitious heat fluxes. To gain the manufacturable structure design, the optimization problem is formulated to find both the optimal shape of the structure and the optimal directions of the adjustable manufacturing tools. A level set-based optimization regarding manufacturability has been studied in previous papers, however, due to the influence of the manufacturing directions, the objective value tends to be captured in local optima as the structure becomes more complex. To cope with this issue, we decided to adapt a heuristic based approach, genetic algorithm, to the optimization method. Simultaneously, to reduce the computation time, we applied Design of Experiments for the initial population of the genetic algorithm. The initial population of the manufacturing directions is installed using Latin Hypercube sampling for both a good representation and computational efficiency. To demonstrate the effectiveness of the proposed method, several design examples are provided, and the differences from the optimal solution, derived by a previous gradient-based optimization scheme, are mentioned.DoeYoung HURSunghoon LIMKazuhiro IZUIShinji NISHIWAKIThe Japan Society of Mechanical Engineersarticlemanufacturabilitygenetic algorithmtopology optimizationgeometric constraintdesign optimizationMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 8, Iss 3, Pp 21-00052-21-00052 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	manufacturability genetic algorithm topology optimization geometric constraint design optimization Mechanical engineering and machinery TJ1-1570
spellingShingle	manufacturability genetic algorithm topology optimization geometric constraint design optimization Mechanical engineering and machinery TJ1-1570 DoeYoung HUR Sunghoon LIM Kazuhiro IZUI Shinji NISHIWAKI Unified structural optimization method using topology optimization and genetic algorithms
description	This paper presents a new structural design framework that incorporates the concept of topology optimization and genetic algorithms to improve the manufacturability and structural robustness of the optimal structure. The level set function is employed as a topological design variable to obtain clear structural boundaries, and the manufacturability of the structure is mathematically defined based on the manufacturing directions and the fictitious heat fluxes. To gain the manufacturable structure design, the optimization problem is formulated to find both the optimal shape of the structure and the optimal directions of the adjustable manufacturing tools. A level set-based optimization regarding manufacturability has been studied in previous papers, however, due to the influence of the manufacturing directions, the objective value tends to be captured in local optima as the structure becomes more complex. To cope with this issue, we decided to adapt a heuristic based approach, genetic algorithm, to the optimization method. Simultaneously, to reduce the computation time, we applied Design of Experiments for the initial population of the genetic algorithm. The initial population of the manufacturing directions is installed using Latin Hypercube sampling for both a good representation and computational efficiency. To demonstrate the effectiveness of the proposed method, several design examples are provided, and the differences from the optimal solution, derived by a previous gradient-based optimization scheme, are mentioned.
format	article
author	DoeYoung HUR Sunghoon LIM Kazuhiro IZUI Shinji NISHIWAKI
author_facet	DoeYoung HUR Sunghoon LIM Kazuhiro IZUI Shinji NISHIWAKI
author_sort	DoeYoung HUR
title	Unified structural optimization method using topology optimization and genetic algorithms
title_short	Unified structural optimization method using topology optimization and genetic algorithms
title_full	Unified structural optimization method using topology optimization and genetic algorithms
title_fullStr	Unified structural optimization method using topology optimization and genetic algorithms
title_full_unstemmed	Unified structural optimization method using topology optimization and genetic algorithms
title_sort	unified structural optimization method using topology optimization and genetic algorithms
publisher	The Japan Society of Mechanical Engineers
publishDate	2021
url	https://doaj.org/article/269ce59711954b6e957711f9e5cbfa6f
work_keys_str_mv	AT doeyounghur unifiedstructuraloptimizationmethodusingtopologyoptimizationandgeneticalgorithms AT sunghoonlim unifiedstructuraloptimizationmethodusingtopologyoptimizationandgeneticalgorithms AT kazuhiroizui unifiedstructuraloptimizationmethodusingtopologyoptimizationandgeneticalgorithms AT shinjinishiwaki unifiedstructuraloptimizationmethodusingtopologyoptimizationandgeneticalgorithms
_version_	1718407575165206528

Unified structural optimization method using topology optimization and genetic algorithms

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