Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.08830 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911201716338688 |
|---|---|
| author | Xu, Shuzhi Guo, Yifan Kawabe, Hiroki Yaji, Kentaro |
| author_facet | Xu, Shuzhi Guo, Yifan Kawabe, Hiroki Yaji, Kentaro |
| contents | Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-homogenization framework that couples MultiFidelity Topology Design (MFTD) with a phasor-based de-homogenization technique. The framework translates low-dimensional geometric descriptors into manufacturable high-resolution structures through a hybrid evolutionary algorithm integrating NSGA-II selection, VAE-enabled latent space crossover, and a novel image deformation-based mutation operator. This gradient-free approach achieves efficient optimization while ensuring geometric continuity. Numerical results confirm that the method effectively balances efficiency and design flexibility, offering a scalable pathway for fabrication-aware multiscale structural optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization Xu, Shuzhi Guo, Yifan Kawabe, Hiroki Yaji, Kentaro Optimization and Control Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-homogenization framework that couples MultiFidelity Topology Design (MFTD) with a phasor-based de-homogenization technique. The framework translates low-dimensional geometric descriptors into manufacturable high-resolution structures through a hybrid evolutionary algorithm integrating NSGA-II selection, VAE-enabled latent space crossover, and a novel image deformation-based mutation operator. This gradient-free approach achieves efficient optimization while ensuring geometric continuity. Numerical results confirm that the method effectively balances efficiency and design flexibility, offering a scalable pathway for fabrication-aware multiscale structural optimization. |
| title | Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.08830 |