Cross-resolution topology optimization for geometrical non-linearity by using deep learning

Li, Jicheng; Ye, Hongling; Yuan, Boshuai; Wei, Nan

doi:10.1007/s00158-022-03231-y

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…Ye et al [14] accelerated the topology optimization process by utilizing a pix2pix network to generate high-quality optimized designs. Li et al [15] developed a cross-resolution Pix2pix neural network, enabling efficient and accurate optimization of geometrically nonlinear systems.…”

Section: Open Access Edited Bymentioning

confidence: 99%

Isogeometric multi-patch topology optimization based on pix2pix

Meng

You

2023

Front. Phys.

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We present a novel approach that combines the power of pix2pix, an image-to-image translation framework, with the advanced capabilities of isogeometric multi-patch analysis for topology optimization. The proposed method adds the Nitsche’s methods into the advantages of Isogeometric analysis (IGA), thus gaining the ability to handle complex geometries by generating locally smooth and well-converged results. Additionally, the usage of generative adversarial network based pix2pix allows for a more efficient representation of the design space, reducing the computational cost of the optimization process. This approach has shown promising results in various numerical examples. This technique aims to improve the efficiency of conceptual design in complex engineering applications.

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Section: Open Access Edited Bymentioning

confidence: 99%

Isogeometric multi-patch topology optimization based on pix2pix

Meng

You

2023

Front. Phys.

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“…[36,37] GAN, which trained two neural network models simultaneously, namely the generator and the discriminator, [38] was implemented for composites, such as generating new structure with targeted mechanical properties, [39,40] predicting the microscale elastic strain field, [33] and topology optimization. [41] In this paper, an inverse structure design strategy is proposed through a DL approach for meta-fiber reinforced hydrogel composites with targeted mechanical field. The design process is schematically illustrated in Figure 1, including four phases, i.e., data generation, data preprocessing, training and validation, and designing phases.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

Liu,

Zhang,

Liu

et al. 2024

Advanced Science

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Fiber‐reinforced hydrogel composites are widely employed in many engineering applications, such as drug release, and flexible electronics, with more flexible mechanical properties than pure hydrogel materials. Comparing to the hydrogel strengthened by continuous fiber, the meta‐fiber reinforced hydrogel provides stronger individualized design ability of deformation patterns and tunable stiffness, especially for the elaborate applications in joint, cartilage, and organ. In this paper, a novel structure design strategy based on deep learning algorithm is proposed for hydrogel reinforced by meta‐fiber to achieve targeted mechanical properties, such as stress and displacement fields. A solid mechanic model for meta‐fiber reinforced hydrogel is first developed to construct the dataset of fiber distribution and the corresponding mechanical properties of the composite. Generative adversarial network (GAN) is then trained to characterize the relationship between stress or displacement field, and meta‐fiber distribution. The well‐trained GAN is implemented to design meta‐fiber reinforced hydrogel composite structure under specific operation conditions. The results show that the deep learning method may efficiently predict the structure of the hydrogel composite with satisfied confidence, and has great potential for applications in drug delivery and flexible electronics.

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“…The same-precision design and cross-precision design of material structures were completed by Li et al [47] through coupling GAN and super resolution GAN. Moreover, the Wasserstein GAN was trained to achieve cross-precision topology optimization design [48]. Although the GAN-based topological configuration might improve the optimization accuracy, the training process of GAN is unstable due to code collapse phenomenon.…”

Section: Introductionmentioning

confidence: 99%

ResUNet involved generative adversarial network-based topology optimization for design of 2D microstructure with extreme material properties

Li,

Ye,

Wei

et al. 2024

Mathematics and Mechanics of Solids

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Topology optimization is one of the most common methods for design of material distribution in mechanical metamaterials, but resulting in expensive computational cost due to iterative simulation of finite element method. In this work, a novel deep learning-based topology optimization method is proposed to design mechanical microstructure efficiently for metamaterials with extreme material properties, such as maximum bulk modulus, maximum shear modulus, or negative Poisson’s ratio. Large numbers of microstructures with various configurations are first simulated by modified solid isotropic material with penalization (SIMP), to construct the microstructure data set. Subsequently, the ResUNet involved generative and adversarial network (ResUNet-GAN) is developed for high-dimensional mapping between optimization parameters and corresponding microstructures to improve the design accuracy of ResUNet. By given optimization parameters, the well-trained ResUNet-GAN is successfully applied to the microstructure design of metamaterials with different optimization objectives under proper configurations. According to the simulation results, the proposed ResUNet-GAN-based topology optimization not only significantly reduces the computational duration for the optimization process, but also improves the structure precise and mechanical performance.

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Cross-resolution topology optimization for geometrical non-linearity by using deep learning

Cited by 11 publications

References 31 publications

Isogeometric multi-patch topology optimization based on pix2pix

Isogeometric multi-patch topology optimization based on pix2pix

Mechanical Field Guiding Structure Design Strategy for Meta‐Fiber Reinforced Hydrogel Composites by Deep Learning

ResUNet involved generative adversarial network-based topology optimization for design of 2D microstructure with extreme material properties

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