Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation
Lightweight structures are ubiquitous in nature and extensively applied in high-end industries for load-bearing purpose. Parametric design is superior for its rapid generation of complex geometries controlled by user-specified parameters, thus has been increasingly explored in structural optimizatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127524009043 |
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| author | Canhui Lin Ke Xu Yingguang Li Xu Liu Chenli Zhou |
| author_facet | Canhui Lin Ke Xu Yingguang Li Xu Liu Chenli Zhou |
| author_sort | Canhui Lin |
| collection | DOAJ |
| description | Lightweight structures are ubiquitous in nature and extensively applied in high-end industries for load-bearing purpose. Parametric design is superior for its rapid generation of complex geometries controlled by user-specified parameters, thus has been increasingly explored in structural optimization. Conventional parametric design approaches for lightweight structure are primarily focus on size and shape, while the topology still requires lengthy iterative process. The major issues that hinder parametric control of topological design include singularity of the stress field and the lack of proper operators for discretized geometry. This paper introduced an implicit neural representation for parametric design of stress-oriented cellular structure, which exhibited great potential to resolve the aforementioned issues by harnessing the universal approximation and resolution invariant capability of a neural network. The structure was implicitly trained to smoothly align with the principal stress field of the input geometry under arbitrary loading condition. A tailored wave projection function together with morphological operators were employed for parametric control of the output cellular structure. The proposed framework enables the parametric design and fabrication of various cellular structures, resulting in enhanced load-bearing capacity compared to structures designed using conventional methods. This advancement opens up new possibilities for the effective design of lightweight structures. |
| format | Article |
| id | doaj-art-b05e420a77464a9895f3e24a104f6288 |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-b05e420a77464a9895f3e24a104f62882025-01-09T06:12:18ZengElsevierMaterials & Design0264-12752025-01-01249113529Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representationCanhui Lin0Ke Xu1Yingguang Li2Xu Liu3Chenli Zhou4Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaNanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaNanjing University of Aeronautics and Astronautics, Nanjing 210016, China; Corresponding author.Nanjing Tech University, Nanjing 210016, ChinaNanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaLightweight structures are ubiquitous in nature and extensively applied in high-end industries for load-bearing purpose. Parametric design is superior for its rapid generation of complex geometries controlled by user-specified parameters, thus has been increasingly explored in structural optimization. Conventional parametric design approaches for lightweight structure are primarily focus on size and shape, while the topology still requires lengthy iterative process. The major issues that hinder parametric control of topological design include singularity of the stress field and the lack of proper operators for discretized geometry. This paper introduced an implicit neural representation for parametric design of stress-oriented cellular structure, which exhibited great potential to resolve the aforementioned issues by harnessing the universal approximation and resolution invariant capability of a neural network. The structure was implicitly trained to smoothly align with the principal stress field of the input geometry under arbitrary loading condition. A tailored wave projection function together with morphological operators were employed for parametric control of the output cellular structure. The proposed framework enables the parametric design and fabrication of various cellular structures, resulting in enhanced load-bearing capacity compared to structures designed using conventional methods. This advancement opens up new possibilities for the effective design of lightweight structures.http://www.sciencedirect.com/science/article/pii/S0264127524009043Structural optimizationParametric designImplicit neural representationImage processingWave projection function |
| spellingShingle | Canhui Lin Ke Xu Yingguang Li Xu Liu Chenli Zhou Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation Materials & Design Structural optimization Parametric design Implicit neural representation Image processing Wave projection function |
| title | Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation |
| title_full | Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation |
| title_fullStr | Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation |
| title_full_unstemmed | Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation |
| title_short | Parametric design and manufacturing of stress-oriented lightweight cellular structure with implicit neural representation |
| title_sort | parametric design and manufacturing of stress oriented lightweight cellular structure with implicit neural representation |
| topic | Structural optimization Parametric design Implicit neural representation Image processing Wave projection function |
| url | http://www.sciencedirect.com/science/article/pii/S0264127524009043 |
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