AI-powered knowledge graphs: Accelerating simultaneous engineering in the automotive digital era

This paper presents a case study at Mercedes-Benz Vans, proving the concept of a semantic-enhanced Digital Twin to solve data integration issues in product development across tools and teams. But in contrast to metadata-level interoperability methods, this approach enables a value-level solution. A multi-layered knowledge graph represents product structure, behavior, and geometry, making hidden values (e.g. wheel position within Unity scene) accessible and synchronizable across systems. The exploratory case study shall prove the feasibility of the semantic-enhanced Digital Twin. The concept was tested in a suspension system use case synchronizing two Unity scenes. Therefor an artifact based on a novel framework defining the underlying processes, mechanisms and system architecture was developed. The implemented system was then tested on several exemplary use cases for basic functionality. The study confirms that the semantic-enhanced Digital Twin approach is technically feasible. Value-level synchronization of structural and behavioral data was achieved, demonstrating the potential to replace traditional file-based export/import processes. The approach supports seamless integration across engineering systems, enabling simultaneous development workflows. Overall, the novel concept and the developed prototype shall provide initial insights into the use of Knowledge Graphs for the synchronization of deep product data in digital engineering. It was the aim to lay the groundwork for future research towards a holistic engineering environment where systems are not only fully interoperable, but also interconnected to enable dynamic feedback loops, such as automatically updating simulation outputs in response to geometry changes in another system.