Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917853482975232 |
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| author | Xu, Pei Wu, Yunpeng Pilla, Srikanth Li, Gang Luo, Feng |
| author_facet | Xu, Pei Wu, Yunpeng Pilla, Srikanth Li, Gang Luo, Feng |
| contents | Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response can enable innovative applications across various industries. Currently, no efforts have achieved the inverse design of FRPCs that target the entire stress-strain curve. Here, we develop PC3D_Diffusion, a 3D spatial diffusion model designed for the inverse design of FRPCs. We generate 1.35 million FRPCs and calculate their stress-strain curves for training. Although the vanilla PC3D_Diffusion can generate visually appealing results, less than 10% of FRPCs generated by the vanilla model are collision-free, in which fibers do not intersect with each other. We then propose a loss-guided, learning-free approach to apply physical constraints during generation. As a result, PC3D_Diffusion can generate high-quality designs with tailored mechanical behaviors while guaranteeing to satisfy the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01321 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials Xu, Pei Wu, Yunpeng Pilla, Srikanth Li, Gang Luo, Feng Soft Condensed Matter Materials Science Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response can enable innovative applications across various industries. Currently, no efforts have achieved the inverse design of FRPCs that target the entire stress-strain curve. Here, we develop PC3D_Diffusion, a 3D spatial diffusion model designed for the inverse design of FRPCs. We generate 1.35 million FRPCs and calculate their stress-strain curves for training. Although the vanilla PC3D_Diffusion can generate visually appealing results, less than 10% of FRPCs generated by the vanilla model are collision-free, in which fibers do not intersect with each other. We then propose a loss-guided, learning-free approach to apply physical constraints during generation. As a result, PC3D_Diffusion can generate high-quality designs with tailored mechanical behaviors while guaranteeing to satisfy the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties. |
| title | Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials |
| topic | Soft Condensed Matter Materials Science |
| url | https://arxiv.org/abs/2412.01321 |