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

TL;DR

This paper introduces a topological analytics approach integrated into CPS architecture for real-time quality monitoring and predictive analytics in smart manufacturing, demonstrating its effectiveness through a case study in additive manufacturing.

Contribution

It presents a novel topological methodology for Stream-of-Quality assessment that uncovers hidden relationships and enables real-time quality control in smart manufacturing.

Findings

Effective real-time quality monitoring demonstrated in additive manufacturing case study

Topological graph visualization aids in identifying new representative data

Method improves adaptability to product quality variations

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.