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arXiv · 2412.01321

Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials

Pei Xu·Yunpeng Wu·Srikanth Pilla·Gang Li·Feng Luo
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Abstract

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.

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Pei Xu, Yunpeng Wu, Srikanth Pilla, Gang Li, Feng Luo. 2024-12-02. Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials. https://arxiv.org/abs/2412.01321

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