Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing
This addresses quality control challenges in smart manufacturing, but it appears incremental as it applies existing topological methods to a specific domain.
The paper tackles real-time quality monitoring in smart manufacturing by proposing a topological analytics approach, demonstrating its feasibility in additive manufacturing to maintain high product quality and adapt to variations.
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.