Manufacturing Letters, Volume 43, 2025, Pages 60-63, ISSN 2213-8463, doi.org/10.1016/j.mfglet.2024.12.002.

Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing

Jay Lee¹, Dai-Yan Ji¹, and Yuan-Ming Hsu^2
1 Center for Industrial Artificial Intelligence, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
² Department of Mechanical & Materials Engineering, College Engineering and Applied Science, University of Cincinnati, OH 45221, USA

For more information about this article and related research, please contact Prof. Jay Lee.

Abstract:

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.

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