An optimization algorithm for automatic structural design

Ferro, Nicola; Micheletti, Stefano; Perotto, Simona

doi:10.1016/j.cma.2020.113335

Cited by 18 publications

(20 citation statements)

References 42 publications

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“…Filtering techniques and ad hoc choices for the discrete spaces represent standard solution to these drawbacks [15]. More recently, a combination of SIMP with anisotropic mesh adaptation has been proposed as a possible remedy to the staircase effect and to the generation of complex geometries [8,9,10,11,21,22]. We adopt this approach, which, in addition, allows us to employ a linear discretization for both density and displacement, choosing…”

Section: Topology Optimization At the Microscalementioning

confidence: 99%

Adaptive Topology Optimization for Innovative 3D Printed Metamaterials

Cristofaro¹,

Galimberti²,

Bianchi³

et al. 2021

14th WCCM-ECCOMAS Congress

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An adaptive method for designing the infill pattern of 3D printed objects is proposed. In particular, new unit cells for metamaterials are designed in order to match prescribed mechanical specifications. To this aim, we resort to topology optimization at the microscale driven by an inverse homogenization to guarantee the desired properties at the macroscale. The whole procedure is additionally enriched with an anisotropic adaptive generation of the computational mesh. The proposed algorithm is first numerically verified both in a mono-and in a multi-objective context. Then, a mechanical validation and 3D manufacturing through fused-model-deposition are carried out to assess the feasibility of the proposed design workflow. 1 MOTIVATIONS Recently, the developing of innovative manufacturing technologies has involved the industry in numerous fields (e.g., automotive, aerospace, medical industry) [4, 23]. Among the different innovations, the Additive Manufacturing (AM) has revolutionized the way to think the production [2]. In fact, the AM has allowed the production of objects with complex geometries in a simple way, overcoming the constraints

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Section: Topology Optimization At the Microscalementioning

confidence: 99%

Adaptive Topology Optimization for Innovative 3D Printed Metamaterials

Cristofaro¹,

Galimberti²,

Bianchi³

et al. 2021

14th WCCM-ECCOMAS Congress

View full text Add to dashboard Cite

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“…To enrich η with directional information, we modify the three contributions in ( 7) by including the anisotropic quantities {λ i,K , r i,K } 2 i=1 . In particular, to deal with η ∇,K , we resort to the procedure in [23,24,40], so that the anisotropic counterpart of the estimator in (7) 1 becomes…”

Section: An a Posteriori Anisotropic Error Estimatormentioning

confidence: 99%

“…As far as the domain tessellation is concerned, automatic adaptive mesh techniques are recognised as an ideal tool (see, e.g., [43]), unless the position of the solution's features is known a priori. In particular, anisotropic grids represent an optimal choice to strike a balance between solution accuracy and numerical efficiency, in the presence of highly directional phenomena (see, e.g., [4,12,18,20,24,37,42]). Anisotropic meshes allow the sharp detection of the solution's directionalities by properly tuning the size, the shape, and the orientation of the mesh elements, in contrast to an isotropic setting, where only the element size can be adjusted.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, the error estimator relies on recovering both the solution and its gradient. The corresponding recovered errors are successively enriched by directional information resorting to the approach in [23,24,40]. Numerical assessment shows that the resulting anisotropic meshes achieve the goal of preserving the expected order of convergence of the DG method in the presence of sharp solution features.…”

Section: Introductionmentioning

confidence: 99%

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An Anisotropic Recovery-Based Error Estimator for Adaptive Discontinuous Galerkin Methods

2021

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We present a new recovery-based anisotropic error estimator for discontinuous Galerkin finite element approximations of advection-diffusion problems. We propose a metric-based algorithm for mesh adaptation which is driven by this error estimator. Numerical verification on several test cases, both in the steady and in the unsteady setting, shows the effectiveness of the algorithm in capturing the intrinsic directionalities of the solution.

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“…Deng et al [24][25][26] applied an evolution algorithm to a complex optimization problem which had proved effective in solving an actual engineering optimization problem. Ferro et al [27] proposed a new algorithm to design lightweight and stiff structures that exhibited free-form features based on the coupling of geometric shape optimization with topology optimization.…”

Section: Introductionmentioning

confidence: 99%

Research on Torsional Property of Body-In-White Based on Square Box Model and Multiobjective Genetic Algorithm

Meng

Yuan

Zhao

et al. 2021

Mathematical Problems in Engineering

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In order to assess the performance of a vehicle in the conceptual design stage, a square box model was proposed to predict the torsional stiffness and the first-order torsional frequency of Body-in-White. The structure of Body-in-White was decomposed into eight simple structural surfaces, from which a square box model was constructed. Based on the finite element method, modified shear stiffness of each simple structure surface was calculated and the torsional stiffness was obtained. Then, simple structural surfaces of Body-in-White were constructed into an eight degree-of-freedom series spring system to calculate the first-order torsional frequency. Furthermore, a multiobjective genetic algorithm was used to determine the thickness and structural reinforcement of panels with small stiffness, so as to achieve the goal of increasing the stiffness while reducing the mass of the panel. The result shows that the optimal values of thickness are reduced by around 9.9 percent without affecting their performance by the proposed method. Compared to the prediction results obtained with the complicated numerical simulation, the relative error of the square box model in predicting the torsional stiffness is 6.04 percent and in predicting the first-order torsional frequency is 0.95 percent, indicating that the prediction model is effective.

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An optimization algorithm for automatic structural design

Cited by 18 publications

References 42 publications

Adaptive Topology Optimization for Innovative 3D Printed Metamaterials

Adaptive Topology Optimization for Innovative 3D Printed Metamaterials

An Anisotropic Recovery-Based Error Estimator for Adaptive Discontinuous Galerkin Methods

Research on Torsional Property of Body-In-White Based on Square Box Model and Multiobjective Genetic Algorithm

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