  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.
