基于数据驱动的机器学习方法通过构建材料特征参数与目标性能间的复杂映射关系，突破传统研发模式 在时间与成本上的限制，为球墨铸铁研发提供了全新范式。 通过对基于机器学习的球墨铸铁研发过程中的应用进展进 行系统梳理，在阐释机器学习数据收集、数据预处理、模型构建与训练以及模型评估等基本实施框架的基础上，综述了 机器学习在组织与缺陷控制、力学性能的预测以及服役行为的预测等方面的应用，探讨了球墨铸铁研发和应用过程中 基于机器学习的一些急需解决的问题，提出了基于机器学习的球墨铸铁研发方向及未来的发展趋势。

Data-driven machine learning methods, by establishing complex mapping relationships between material characteristic parameters and target properties, provide a novel paradigm for ductile iron research and development, overcoming the limitations of time and cost inherent in traditional research and development (R&D) approaches. Recent progress in the application of machine learning within the ductile iron R&D process is systematically reviewed. The fundamental implementation framework is elucidated, encompassing data collection, data preprocessing, model construction and training, and model evaluation. The applications of machine learning are summarized in various areas, including microstructure and defect control, prediction of mechanical properties, and prediction of service performance. Critical challenges demanding urgent solutions in machine learning-based ductile iron R&D and applications are discussed. Finally, research directions and future development trends for machine learning-driven ductile iron development are proposed.