Abstract
Despite the potential of Virtual Metrology (VM), integrated frameworks combining standardized data handling, automated model development, and uncertainty quantification remain rare. This paper presents a scalable VM architecture that leverages Asset Administration Shells (AAS) for data integration, AutoML for modeling, and a practical UQ approach. We propose a novel but practically applicable method that connects GUM principles with ML-based uncertainty, aiming to support informed architectural decisions and foster robust, interpretable, and scalable VM deployment in industrial environments.
| Original language | English |
|---|---|
| Journal | IFAC-PapersOnLine |
| Volume | 59 |
| Issue number | 30 |
| Pages (from-to) | 395-400 |
| Number of pages | 6 |
| ISSN | 2405-8971 |
| DOIs | |
| Publication status | Published - 01.10.2025 |
| Event | 5th Conference on Modeling, Estimation and Control - MECC 2025 - Sheraton Pittsburg Hotel, Pittsburgh, United States Duration: 05.10.2025 → 08.10.2025 Conference number: 5 https://mecc2025.a2c2.org/ |
Bibliographical note
Publisher Copyright:© 2025 The Authors.
Research areas and keywords
- AAS
- AutoML
- Uncertainty Quantification
- Virtual Metrology
- Engineering
ASJC Scopus Subject Areas
- Control and Systems Engineering
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