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.

该研究论文提出了一种基于拓扑分析的方法，用于智能制造中流质量评估的 5 级网络物理系统架构。该方法不仅可以实时监测和预测分析质量，还可以发现不同制造过程中质量特征与工艺参数之间的隐藏关系。通过应用于增材制造的案例研究，论文证明了该方法在保持高产品质量和适应产品质量变化方面的可行性。论文展示了如何有效地使用拓扑图可视化来通过流质量评估实时识别新的代表性数据。