Stress-constrained concurrent two-scale topology optimization of functionally graded cellular structures using level set-based trimmed quadrilateral meshes

Ho-Nguyen-Tan, Thuan; Kim, Hyun‐Gyu

doi:10.1007/s00158-023-03572-2

Cited by 8 publications

(1 citation statement)

References 54 publications

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“…During the optimization process, the reaction-diffusion equation is solved by utilizing topological sensitivity fields, which are employed to update the level set functions. At the zero-surface (in 3D) or zero-contour (in 2D) of level set functions, one of the authors used the cutting method [42,43] to generate optimal designs with clear structural boundaries. Using the triangulation scheme, these configurations are directly translated into STL models, which are compatible with the printing process.…”

Section: Introductionmentioning

confidence: 99%

Coupling design and fabrication of continuous carbon fiber-reinforced composite structures using two-material topology optimization and additive manufacturing

Ho,

Nguyen,

et al. 2024

Int J Adv Manuf Technol

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This study presents an integration of the level set-based two-material topology optimization method and the additive manufacturing technique for the design and fabrication of continuous carbon fiber (CCF)-reinforced composite structures. Firstly, optimal configurations of the resin material and fiber reinforcement are obtained to maximize the structural stiffness under desired volume constraints using the two-material topological optimization. After that, the level set-based cutting mesh method and triangulation scheme are employed to interpret these 1 ManuscriptClick here to access/download;Manuscript;IJAMT.tex Click here to view linked References topological designs into stereolithography (STL) models with clear structural boundaries for the manufacturing. A customized pre-processing strategy is used to accurately determine the fiber placement regions from the optimal designs. Topological results are then fabricated using the CCF-based 3D printing method with prepreg carbon fibers. Subsequently, the performance of printed CCF-reinforced composite structures is investigated, using different resin materials: Esun polylactic acid (EPLA) and polyamide 12 with 10% carbon fiber (PA12 10CF).Experimental results indicate a significant increase in stiffness and strength of composite structures with fiber reinforcements for all resin materials, with an increase of 315% for EPLA and 234% for PA12 10CF. Additionally, the CCFreinforced composite structures made of PA12 10CF exhibit superior stiffness compared to those made of EPLA with a double increment. The microstructural characteristics of damaged regions are examined using scanning electron microscope (SEM) images, which provide valuable insights into the behavior of resin and fiber materials.

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Section: Introductionmentioning

confidence: 99%

Coupling design and fabrication of continuous carbon fiber-reinforced composite structures using two-material topology optimization and additive manufacturing

Ho,

Nguyen,

et al. 2024

Int J Adv Manuf Technol

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Fiber‐Reinforced Composite and Topology Optimization in Additive Manufacturing

Hoang,

Du Nguyen

2024

Advances in Additive Manufacturing

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Stress-constrained optimization of multiscale structures with parameterized microarchitectures using machine learning

Black,

Najafi

2024

Struct Multidisc Optim

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A multiscale topology optimization framework for stress-constrained design is presented. Spatially varying microstructures are distributed in the macroscale where their material properties are estimated using a neural network surrogate model for homogenized constitutive relations. Meanwhile, the local stress state of each microstructure is evaluated with another neural network trained to emulate second-order homogenization. This combination of two surrogate models — one for effective properties, one for local stress evaluation — is shown to accurately and efficiently predict relevant stress values in structures with spatially varying microstructures. An augmented lagrangian approach to stress-constrained optimization is then implemented to minimize the volume of multiscale structures subjected to stress constraints in each microstructure. Several examples show that the approach can produce designs with varied microarchitectures that respect local stress constraints. As expected, the distributed microstructures cannot surpass density-based topology optimization designs in canonical volume minimization problems. Despite this, the stress-constrained design of hierarchical structures remains an important component in the development of multiphysics and multifunctional design. This work presents an effective approach to multiscale optimization where a machine learning approach to local analysis has increased the information exchange between micro- and macroscales.

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Stress-constrained concurrent two-scale topology optimization of functionally graded cellular structures using level set-based trimmed quadrilateral meshes

Cited by 8 publications

References 54 publications

Coupling design and fabrication of continuous carbon fiber-reinforced composite structures using two-material topology optimization and additive manufacturing

Coupling design and fabrication of continuous carbon fiber-reinforced composite structures using two-material topology optimization and additive manufacturing

Fiber‐Reinforced Composite and Topology Optimization in Additive Manufacturing

Stress-constrained optimization of multiscale structures with parameterized microarchitectures using machine learning

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