Moving morphable curved components framework of topology optimization based on the concept of time series

Li, Zonghao; Hu, Xueyan; Chen, Weiqiu

doi:10.1007/s00158-022-03472-x

Cited by 6 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, various kinds of complicated curved components can be built with NURBS curves or surfaces. Li et al [ 18 ] demonstrated a new topology optimization approach derived from the MMC framework by replacing the straight components with the curved ones so as to enhance the geometric arbitrariness. The skeleton of the modified component is described by the NURBS curve, and the concept of time series is then proposed to directly generate the curved component from the 1D skeleton curve.…”

Section: Introductionmentioning

confidence: 99%

Moving morphable component (MMC) topology optimization with different void structure scaling factors

Li,

Xu,

Zhang

2024

PLoS ONE

View full text Add to dashboard Cite

The explicit topology optimization method based on moving morphable component (MMC) has attracted more and more attention, and components are the basic building blocks of the implementation of MMC method. In the present work, a MMC topology optimization method based on component with void structure is followed with interest. On the basis of analyzing the characteristics of components used by MMC method, the topology description function for component with void structure is presented, where a quantitative scaling factor is introduced without increasing the number of design variables. Taking the minimum flexibility as the optimization objective, an example of short beam is analyzed with different void structure scaling factors. The results show that different scaling factors have a greater impact on the final topology optimization structure, and an ideal topology structure can be obtained with an appropriate scaling factor. Finally, some problems in the optimization process are analyzed and indicate that appropriate mesh density should be chose for component with void structure in order to achieve good optimization results.

show abstract

Section: Introductionmentioning

confidence: 99%

Moving morphable component (MMC) topology optimization with different void structure scaling factors

Li,

Xu,

Zhang

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

Stabilized time‐series moving morphable components method for topology optimization

Hu,

Li,

Bao

et al. 2024

Numerical Meth Engineering

View full text Add to dashboard Cite

The moving morphable components (MMC) method has been widely used for topology optimization due to its ability to provide an explicit description of topology. However, the MMC method may encounter the instability issue during iteration. Specifically, the iteration history is highly sensitive to parameters of the optimizer, that is, the move limits in the method of moving asymptotes (MMA). Additionally, the final topology obtained from the MMC method usually depends on the initial values. To address these issues and improve the stability of the MMC method in practical applications, this article introduces two strategies. The first strategy is based on the time‐series MMC (TSMMC) method, which proposes a unified description of curved components. However, the use of control‐points‐based design variables may introduce instability into the iteration process due to the strong locality associated with these variables. To mitigate this, global design variables have been incorporated into the formulation. Numerical examples demonstrate that this mixed formulation, combining global and local design variables, can enhance stability significantly. To further enhance stability, the second strategy involves using the trust region‐based moving asymptotes (TRMA) method as the optimizer instead of MMA. The TRMA method incorporates an accuracy control mechanism, resulting in stable and fast convergence behavior, as demonstrated in the numerical examples.

show abstract

Towards closed-walled designs in topology optimization using selective penalization

Rieser

Zimmermann

2023

Struct Multidisc Optim

View full text Add to dashboard Cite

Abstract3D topology optimization using a density approach with penalization usually produces truss-like, open-walled structures. The coarser the mesh, the smaller the volume fraction, and the faster the penalization is increased, the more pronounced this effect tends to be. However, closed-walled designs are often more efficient and have other beneficial properties. For instance, closed walls can contribute to achieving self-supporting designs for additive manufacturing that potentially require fewer sacrificial support structures than truss-like designs. This paper presents a two-step optimization procedure for generating closed-walled designs using coarse meshes. The first step takes the usual Eulerian approach of performing a SIMP-based topology optimization on a fixed mesh. To keep thin geometrical features, like walls with a thickness below element size, penalization is switched off deliberately where the formation of such features is detected. Adopting a Lagrangian description, intermediate densities still present in the optimized design are subsequently eliminated in a second step by shrinking each element according to its density. By analogy with volumetric thermal contraction, this is accomplished by solving a fictitious thermo-elastic problem where the temperature has been replaced by a density expression. The outcome is a morphed mesh with a somewhat smoothed surface and a volume close to the specified material volume limit. This body-fitted representation of the design considerably simplifies the final conversion into a manufacturable CAD-type geometry. The two-step optimization procedure is applied to a cantilever, a torsion rod, and a disk reinforcement benchmark problem. Optimized designs are closed-walled and show very good agreement to those found for much finer meshes. Problem-specific stiffness improvements over truss-like designs between $$6\%$$ 6 % and almost $$30\%$$ 30 % were achieved and confirmed the findings previously reported by other authors.

show abstract

Moving morphable curved components framework of topology optimization based on the concept of time series

Cited by 6 publications

References 30 publications

Moving morphable component (MMC) topology optimization with different void structure scaling factors

Moving morphable component (MMC) topology optimization with different void structure scaling factors

Stabilized time‐series moving morphable components method for topology optimization

Towards closed-walled designs in topology optimization using selective penalization

Contact Info

Product

Resources

About