Quality management is a constant and significant concern in enterprises. Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs. This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical (HCP) knowledge graph, which is a systematic method that encompasses the following elements: data management and classification based on HCP ternary data, HCP ontology construction, knowledge extraction for constructing an HCP knowledge graph, and comprehensive application of quality control based on HCP knowledge. The proposed method implements case retrieval, automatic analysis, and assisted decision making based on an HCP knowledge graph, enabling quality monitoring, inspection, diagnosis, and maintenance strategies for quality control. In practical applications, the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge. Moreover, the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making. The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process, and the effectiveness of the method was verified by the application system deployed. Furthermore, the proposed method can be extended to other manufacturing process quality control tasks.

中文翻译：

---

一种基于人-信息-物理知识图谱的制造过程智能质量控制方法


质量管理是企业持续而重要的关注点。有效确定综合问题的正确解决方案有助于避免增加回溯测试成本。本研究提出了一种基于人-信息-物理 （HCP） 知识图谱的制造过程智能质量控制方法，该方法是一种系统的方法，包括以下要素：基于 HCP 三元数据的数据管理和分类、HCP 本体构建、构建 HCP 知识图谱的知识抽取以及基于 HCP 知识的质量控制的综合应用。所提方法基于 HCP 知识图谱实现了案例检索、自动分析和辅助决策，使能质量控制的质量监控、巡检、诊断和维护策略。在实际应用中，所提出的模块化和分层 HCP 本体在所获得知识的可共享性和可重用性方面表现出显着的优越性。此外，HCP 知识图谱将提供的 HCP 数据深度集成，有效支持综合决策。在涉及汽车生产线和齿轮制造过程的案例中实施了所提出的方法，并通过部署的应用系统验证了该方法的有效性。此外，所提出的方法可以扩展到其他制造过程质量控制任务。