Artificial intelligence is beginning to reduce the manual effort in the CAD-to-mesh pipeline. Written for meshing and geometry practitioners with limited AI background, this survey organizes recent work by workflow step. We cover part classification and segmentation, mesh quality prediction, and defeaturing. We review AI guidance for unstructured meshing, block-structured meshing in 2D and 3D, and volumetric parameterization, including reconstruction from implicit or sampled geometry. We also discuss parallel mesh generation and scripting automation via reinforcement learning and large language models. Across these topics, AI complements established geometry and meshing algorithms rather than replacing them. We conclude with practical lessons and open challenges in data, benchmarks, and trustworthy integration.

翻译：人工智能正开始减少CAD到网格流程中的人工工作量。本综述面向网格与几何处理从业者而撰写，假定其人工智能背景有限，并按工作流步骤对近期研究进行系统梳理。我们涵盖部件分类与分割、网格质量预测以及特征简化。我们回顾了非结构化网格生成、二维与三维块结构化网格生成以及体参数化中的人工智能引导方法，包括从隐式或采样几何进行重建的技术。同时探讨了通过强化学习与大语言模型实现的并行网格生成与脚本自动化。在这些主题中，人工智能主要对成熟的几何与网格算法形成补充而非替代。最后，我们总结了在数据、基准测试及可信集成方面的实践经验和开放挑战。