Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913325229539328 |
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| author | Lee, Jay Ji, Dai-Yan Hsu, Yuan-Ming |
| author_facet | Lee, Jay Ji, Dai-Yan Hsu, Yuan-Ming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14728 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing Lee, Jay Ji, Dai-Yan Hsu, Yuan-Ming Machine Learning Computers and Society 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. |
| title | Novel Topological Machine Learning Methodology for Stream-of-Quality Modeling in Smart Manufacturing |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2404.14728 |