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Reinforcement learning based automatic block decomposition of solid models for hexahedral meshing
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DOI：10.1016/j.cad.2025.103850.png)
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Generating high-quality meshes for CAD models is a crucial preprocessing task for numerical simulation. Although mesh generation techniques are well-established, automatic hexahedral meshing remains challenging, particularly for complex geometries. Conventional methods often require manual intervention to decompose solid models into simpler, meshable blocks, which is labor-intensive and demands expert knowledge. To address the challenge of automating the block decomposition of solid models for hexahedral meshing, we propose a novel reinforcement learning (RL) framework. This framework enables an agent to learn optimal decomposition strategies by interacting with a CAD modeling environment. Key contributions include a network-friendly method for representing and learning the environment's state and the agent's actions-3D geometric shapes and the corresponding block decomposition operations; a two-step training strategy that integrates imitation learning with reinforcement learning to improve training efficiency. Experimental results demonstrate that our RL-based method achieves amore effective automatic block decomposition of complex 3D solid models for generating high-quality hexahedral meshes.
Keyword:
CAD/CAE
Block decomposition
Reinforcement learning
Hexahedral mesh generation
期刊
C
IF:
3.1
论文数:
3.2K
被引数:
6.4K
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