This paper presents a topological analytics approach within the 5-level Cyber-Physical Systems (CPS) architecture for the Stream-of-Quality assessment in smart manufacturing. The proposed methodology not only enables real-time quality monitoring and predictive analytics but also discovers the hidden relationships between quality features and process parameters across different manufacturing processes. A case study in additive manufacturing was used to demonstrate the feasibility of the proposed methodology to maintain high product quality and adapt to product quality variations. This paper demonstrates how topological graph visualization can be effectively used for the real-time identification of new representative data through the Stream-of-Quality assessment.

翻译：本文提出了一种基于五级信息物理系统（CPS）架构的拓扑分析方法，用于智能制造中的流式质量评估。该方法不仅能够实现实时质量监测与预测分析，还能发现不同制造过程中质量特征与工艺参数之间的隐含关系。通过增材制造案例研究，验证了该方法在维持高产品质量及适应产品质量波动方面的可行性。本文展示了如何将拓扑图可视化技术有效用于流式质量评估中实时识别新增代表性数据。