Towards a Comprehensive Virtual Metrology Framework: Integrating AutoML, Data Integration, Uncertainty Quantification & Model Maintenance

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.