Artificial intelligence is beginning to ease long-standing bottlenecks in the CAD-to-mesh pipeline. This survey reviews recent advances where machine learning aids part classification, mesh quality prediction, and defeaturing. We explore methods that improve unstructured and block-structured meshing, support volumetric parameterizations, and accelerate parallel mesh generation. We also examine emerging tools for scripting automation, including reinforcement learning and large language models. Across these efforts, AI acts as an assistive technology, extending the capabilities of traditional geometry and meshing tools. The survey highlights representative methods, practical deployments, and key research challenges that will shape the next generation of data-driven meshing workflows.

翻译：人工智能正开始缓解CAD到网格流程中长期存在的瓶颈。本综述回顾了机器学习在部件分类、网格质量预测与特征简化方面的最新进展。我们探讨了改进非结构化与块结构化网格生成、支持体积参数化以及加速并行网格生成的方法。同时分析了脚本自动化领域的新兴工具，包括强化学习与大语言模型。在这些研究中，AI作为辅助技术延伸了传统几何与网格工具的能力。本综述重点阐述了具有代表性的方法、实际应用案例以及将塑造下一代数据驱动网格工作流程的关键研究挑战。