Stress-based topology optimization of compliant mechanisms design using geometrical and material nonlinearities

Leon, Daniel M. De; Gonçalves, Juliano F.; Souza, Carlos Eduardo de

doi:10.1007/s00158-019-02484-4

Cited by 22 publications

(11 citation statements)

References 43 publications

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“…The study of De Leon et al (2020) is the most challenging because the proposed method considers both the geometric nonlinearity and the maximum stress. However, in their method, the maximum stress is approximately evaluated using the global p-norm, similar to Allaire and Jouve (2008), Holmberg et al (2013), andDe Leon et al (2015).…”

Section: Examplementioning

confidence: 99%

“…Similar to example 2, we use conforming meshes to structural boundaries to accurately compute the stress. In addition, we completely deactivate the elements in the void domain because weak materials set in the void domain may cause numerical instabilities in the geometric nonlinear analysis (De Leon et al 2020). That is, we extract the structural boundary in the design domain and conduct a geometric nonlinear analysis using only the material domain.…”

Section: Examplementioning

confidence: 99%

“…That is, it is hard to directly solve some types of topology optimization problems because of their nonlinearity. Representative problems are flow channel design problems under a turbulent flow (Kontoleontos et al 2013;Dilgen et al 2018), and compliant mechanism design problems taking the geometric nonlinearity and the maximum stress into account (De Leon et al 2020). Although a limited number of studies address solving these problems using sensitivity-based mathematical programming, it is also valuable to research alternative approaches.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven topology design using a deep generative model

Yamasaki

Yaji

Fujita

2021

Struct Multidisc Optim

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In this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from initially given material distributions in a given design domain. Its basic idea is to iterate the following processes: (i) selecting material distributions from a dataset of material distributions according to eliteness, (ii) generating new material distributions using a deep generative model trained with the selected elite material distributions, and (iii) merging the generated material distributions with the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inherit features of the training data, that is, the elite material distributions. Therefore, it is expected that some of the generated material distributions are superior to the current elite material distributions, and by merging the generated material distributions with the dataset, the performances of the newly selected elite material distributions are improved. The performances are further improved by iterating the above processes. The usefulness of data-driven topology design is demonstrated through numerical examples.

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Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-driven topology design using a deep generative model

Yamasaki

Yaji

Fujita

2021

Struct Multidisc Optim

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“…Among them, there are works that focus on developing more efficient and accurate ways to handle the stress constraints, [8][9][10][11][12][13][14][15][16] and works that focus on novel applications, [17][18][19][20] as the problem of compliant mechanisms with stress constraints. [21][22][23][24][25][26][27] Although not novel, stress-constrained topology optimization has been the subject of intensive research in the literature up to the present day.…”

Section: Introductionmentioning

confidence: 99%

“…Since the seminal paper by Duysinx and Bendsøe, 7 several works addressing topology optimization with stress constraints have been developed. Among them, there are works that focus on developing more efficient and accurate ways to handle the stress constraints, 8‐16 and works that focus on novel applications, 17‐20 as the problem of compliant mechanisms with stress constraints 21‐27 . Although not novel, stress‐constrained topology optimization has been the subject of intensive research in the literature up to the present day.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional manufacturing tolerant topology optimization with hundreds of millions of local stress constraints

Silva

Aage

Beck

et al. 2020

Numerical Meth Engineering

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In topology optimization, the treatment of stress constraints for very large scale problems (more than 100 million elements and more than 600 million stress constraints) has so far not been tractable due to the failure of robust agglomeration methods, that is, their inability to accurately handle the locality of the stress constraints. This article presents a three-dimensional design methodology that alleviates this shortcoming using both deterministic and robust problem formulations. The robust formulation, based on the three-field density projection approach, is extended and proved necessary to handle manufacturing uncertainty in three-dimensional stress-constrained problems. Several numerical examples are solved and further postprocessed with body-fitted meshes using commercial software. The numerical investigations demonstrate that: (1) the employed solution approach based on the augmented Lagrangian method is able to handle very large problems, with hundreds of millions of stress constraints; (2) three-dimensional stress-based results are extremely sensitive to slight manufacturing variations; (3) if appropriate interpolation parameters are adopted, voxel-based (fixed grid) models can be used to compute von Mises stresses with excellent accuracy; and (4) in order to ensure manufacturing tolerance in three-dimensional stress-constrained topology optimization, a combination of double filtering and more than three density field realizations may be required.

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Topology Optimization Design of Cartwheel Hinge Based on Response Surface Method

Chen,

Chen

et al. 2024

Mechanisms and Machine Science

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Stress-based topology optimization of compliant mechanisms design using geometrical and material nonlinearities

Cited by 22 publications

References 43 publications

Data-driven topology design using a deep generative model

Data-driven topology design using a deep generative model

Three‐dimensional manufacturing tolerant topology optimization with hundreds of millions of local stress constraints

Topology Optimization Design of Cartwheel Hinge Based on Response Surface Method

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