Demonstrating Data-to-Knowledge Pipelines for Connecting Production Sites in the World Wide Lab
This addresses the need for scalable data integration and decision support in industrial production, though it appears incremental by building on existing concepts like Digital Shadows.
The paper tackles the problem of data integration and decision-making in digital production by proposing Data-to-Knowledge pipelines, demonstrating a proof of concept that captures and semantically annotates trajectory data from multiple robots across organizations and uses it to train an inverse dynamic foundation model for robotic control.
The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.