Identifying boundaries of topology optimization results using basic parametric features

Yi, Guilian; Kim, Nam Hoon

doi:10.1007/s00158-016-1597-9

Cited by 28 publications

(13 citation statements)

References 43 publications

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“…Two test cases are illustrated hereafter. We also check the testcase with volf rac = 0.5 according to [25] with an initial good nodes placement. The optimizer converges locally then oscillates between two solutions.…”

Section: Toward Element Recognitionmentioning

confidence: 99%

“…Then a new costly design cycle must be relaunched: mesh creation from topology optimization results, and if validated, a new sizing optimization problem. Some researchers try then to extract directly from topology optimization results (image processing) the structure skeleton to relaunch easily the FE sizing process [25,8]. Moreover as demonstrated by [10], the number of design variables involved in implicit topology optimization approaches is relatively large especially for three dimensional problems.…”

Section: Introductionmentioning

confidence: 99%

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Explicit topology optimization through moving node approach: beam elements recognition

Raze,

Morlier

2021

Preprint

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Structural optimization (topology, shapes, sizing) is an important tool for facilitating the emergence of new concepts in structural design. Normally, topology optimization is carried out at the early stage of design and then shape and sizing design are performed sequentially. Unlike traditional topology optimization method, explicit methodologies have attracted a great deal of attention because of the advantages of shortcuting the costly CAD/CAE processes while dealing with low order number of design variables compared to implicit method (such as SIMP). This paper aims at presenting an adaptation of a flow-inspired approach so-called Moving Node Approach (MNA) in topology optimization. In this approach, the discretization is decoupled from the material distribution and the final objective is to recognize the best beam assembly while minimizing compliance. The paradigm has here changed and new design variables are used such as nodes location, elements length/orientation and size providing a lower number of design variables than pixels-based. The methodology is validated using 2 classical testcases in the field of Topology Optimization: the Cantilever beam and the L-Shape problem.

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Section: Toward Element Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Explicit topology optimization through moving node approach: beam elements recognition

Raze,

Morlier

2021

Preprint

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“…The SIMP method generates a gray‐scale color code to represent the quantity of material at each element, yielding images with irregular shapes not directly suitable for manufacturing 18 . There exist some approaches to improve the definition of the boundary.…”

Section: Introductionmentioning

confidence: 99%

“…The SIMP method generates a gray-scale color code to represent the quantity of material at each element, yielding images with irregular shapes not directly suitable for manufacturing. 18 There exist some approaches to improve the definition of the boundary. Among others, we emphasize the works in Reference 19, based on projection methods, and Reference 20, where the mesh is refined along the boundary defined by the TO algorithm, that is, along the regions where intermediate density values have been obtained.…”

Section: Introductionmentioning

confidence: 99%

Improvement in 3D topology optimization with h‐adaptive refinement using the Cartesian grid Finite Element Method

Muñoz

Albelda

Ródenas

et al. 2021

Numerical Meth Engineering

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The growing number of scientific publications on topology optimization (TO) shows the great interest that this technique has generated in recent years. Among the different methodologies for TO, this article focuses on the well‐known solid isotropic material penalization (SIMP) method, broadly used because of its simple formulation and efficiency. Even so, the SIMP method has certain drawbacks, namely: lack of precision in definition of the edges of the optimized geometry and final results strongly influenced by the discretization used for the finite element (FE) analyses. In this article, we propose a combination of techniques to limit the effect of these drawbacks and, thus, to improve the behavior of TO. All these techniques are based on the use of the Cartesian grid finite element method (cgFEM), an immersed boundary method whose Cartesian grid structure and hierarchical data structure makes it specially appropriate for TO. All the proposed techniques are framed under the concept of mesh refinement. First, we propose the use of two meshes, the FE analysis mesh, and a finer mesh for integration and evaluation of sensitivities, to improve the resolution of the final solution at a marginal computational cost. Then we propose two h‐adaptive mesh refinement strategies. The first one will tend to refine the elements having intermediate density values and will have the effect of sharpening the definition of the edges of the optimized geometry. We will clearly show that if the accuracy of the FE analyses is not taken into account, stress constrained TO will generate solutions that, once manufactured, will not satisfy the constraints. Hence, we also propose an h‐refinement strategy based on the estimation of the discretization error in energy norm.

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“…When considering the post-processing task of improving the manufacturability of an optimised model, it may prove difficult to create a set of parameters that work for both variable density solutions and binary solutions. This is especially true if an automated "clean-up" program is considered, in which some existing tools may work well for refining binary models but not necessarily for variable density models (and vice versa).This is evident in several examples, including Liu and Ma (2015), where only binary solutions are refined and Yi and Kim (2016), in which only VDM solutions can be refined (see Section 3 for further detail on these methods). Despite this, it is evident from several sources, including Yi and Kim (2016) and Nana et al (2016) that progress in the development of an automated post-processor which can correct geometry issues is being made.…”

Section: Introductionmentioning

confidence: 99%

Review of topology optimisation refinement processes for sheet metal manufacturing in the automotive industry

Sehmi

Christensen

Bastien

et al. 2018

Struct Multidisc Optim

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Identifying boundaries of topology optimization results using basic parametric features

Cited by 28 publications

References 43 publications

Explicit topology optimization through moving node approach: beam elements recognition

Explicit topology optimization through moving node approach: beam elements recognition

Improvement in 3D topology optimization with h‐adaptive refinement using the Cartesian grid Finite Element Method

Review of topology optimisation refinement processes for sheet metal manufacturing in the automotive industry

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