A new non-gradient-based topology optimization algorithm with black–white density and manufacturability constraints

Goto, Tiago G.; Najafabadi, Hossein R.; Falheiro, Mizael; Moura, Rafael Traldi; Driemeier, Larissa; Barari, Ahmad; Tsuzuki, Marcos de Sales Guerra; Martins, Thiago C.

doi:10.1016/j.istruc.2022.12.001

Cited by 6 publications

(2 citation statements)

References 55 publications

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“…Later, the upcoming moving morphable component (MMC) method, part of the low-dimensional explicit-based TO group, was subjected to comparative studies with almost variant MMCs, as seen in [21]. Very recent work in the non-gradient-based field has proposed to improve the checkerboard problem by checking the connectivity of neighboring elements and penalizing it [22]. A new TO named proportional topology optimization (PTO) has been proposed to increase its performance, making it more comparable to GBTO [23].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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This research proposes an efficient technique for reliability-based topology optimization (RBTO), which deals with uncertainty and employs proportional topology optimization (PTO) to achieve the optimal reliability structure. The recent technique, called proportional topology optimization for reliability (PTOr), uses Latin hypercube sampling (LHS) for uncertainty quantification. The difficulty of the double-loop nested problem in uncertainty quantification (UQ) with LHS can be alleviated by the power of PTO, enabling RBTO to be performed easily. The rigorous advantage of PTOr is its ability to accomplish topology optimization (TO) without gradient information, making it faster than TO with evolutionary algorithms. Particularly, for reliability-based topology design, evolutionary techniques often fail to achieve satisfactory results compared to gradient-based techniques. Unlike recent PTOr advancement, which enhances the RBTO performance, this achievement was previously unattainable. Test problems, including an aircraft pylon, reveal its performances. Furthermore, the proposed efficient framework facilitates easy integration with other uncertainty quantification techniques, increasing its performance in uncertainty quantification. Lastly, this research provides computer programs for the newcomer studying cutting-edge knowledge in engineering design, including UQ, TO, and RBTO, in a simple manner.

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Section: Introductionmentioning

confidence: 99%

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Kumkam,

Sleesongsom

2024

Aerospace

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“…The main advantage of this method in comparison to adding connectivity criteria to the objective function is the ability to search only in the feasible design. Any set of constraints can be checked this way by the TO with SA and converge to a feasible solution [101]. In addition, this method does not need a very high number of iterations.…”

Section: Binary Topology Optimizationmentioning

confidence: 99%

Design of surface micro-machined inertial MEMS sensor with meta-heuristic topology optimization.

Najafabadi¹

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Os sistemas microeletromecânicos (MEMS) estão se tornando populares para uma variedade de aplicações devido ao seu pequeno tamanho, baixo custo e bom desempenho. Os dispositivos MEMS são projetados combinando formas simples com base na experiência de casos anteriores. O uso de métodos de projeto matemático, como a Otimização de Topologia (TO), ajuda a encontrar o melhor projeto possível para um aplicativo específico. Em uma aplicação de monitoramento de deslizamentos de terra, acelerômetros MEMS de alta sensibilidade são necessários para prever com precisão a ocorrência de deslizamentos de terra. Mas os métodos TO disponíveis são incapazes de encontrar um projeto com sensibilidade máxima, condição de carregamento variável e sujeito a várias restrições. As restrições são devidas aos limites de fabricação na fabricação de MEMS e aos limites de desempenho do problema físico. O objetivo desta tese é desenvolver um método TO meta-heurístico usando Simulated Annealing (SA) para resolver problemas TO não convexos e multi-restrições sem informação de gradiente. Este TO utiliza fatores de cristalização para melhorar a convergência e reduzir os custos computacionais no TO. O método proposto é validado com problemas de referência na literatura e é usado com sucesso para os acelerômetros TO MEMS. A análise dos parâmetros de otimização neste projeto fornece algumas informações úteis sobre convergência e exclusividade da solução ótima. Os designs otimizados são então comparados aos designs disponíveis para vários parâmetros de desempenho. Além disso, alguns métodos de filtragem e pós-processamento são desenvolvidos para aplicar os limites de fabricação no processo de litografia.

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Enhancing topology optimization with adaptive deep learning

Zhang,

Jia,

Liu

et al. 2024

Computers & Structures

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A new non-gradient-based topology optimization algorithm with black–white density and manufacturability constraints

Cited by 6 publications

References 55 publications

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Reliability-Based Topology Optimization with a Proportional Topology for Reliability

Design of surface micro-machined inertial MEMS sensor with meta-heuristic topology optimization.

Enhancing topology optimization with adaptive deep learning

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