为了解决汽车行业潮模砂铸造领域质量追溯困难的问题，通过采集铸件生产制造过程影响铸件质量的不同种类、不同格式、不同结构、不同质量的数据，基于 MES 系统通过程序设计将所采集数据运用多源异构融合技术进行处理，形成铸件制造过程质量相关规范数据，并与铸件实时绑定存入数据库或数据湖中进行共享，供生产计划排程、生产过程管控、全生产过程质量管理、工厂内物流管理系统使用，实现铸件产品单体质量追溯及批次质量追溯。 处理后的数据通过对照工艺质量标准进行追溯，在指导铸件生产过程质量参数优化方面起着重要作用。 系统运行后，经过大量数据积累，后期运用大数据分析和质量模型算法，实现了质量预测和反馈控制，并探索出一条铸造企业在生产制造领域以数据赋能产品质量提升的数字化系统建设之路。

 To address the difficulty of quality traceability in the field of automotive foundries using sand molds, data of different types, formats, structures, and qualities that affect the quality of castings during the production process were collected. Based on the manufacturing execution system (MES), the collected data were processed using multi-source heterogeneous fusion technology to generate standardized quality-related data of the casting manufacturing process, which was linked to the castings in real-time and stored in a database or data lake for sharing. It was then used for production planning and scheduling, production process control, overall quality management of the production process, and internal logistics management systems to achieve traceability of individual casting product quality and batch quality. The processed data play an important role in quality traceability and guiding the optimization of quality parameters in the casting production process by comparing them with process quality standards. After the system is implemented and a large amount of data is accumulated, big data analysis and quality model algorithms are applied to achieve quality prediction and feedback control. This study explores the path for a foundry enterprise to enhance product quality through data empowerment in the field of production manufacturing.