On A Semantic Model and Knowledge Graph based Approach to Enable Transparency, Explainability, and Auditability for High-Pressure Die-Casting

This paper addresses the critical challenge of fragmented data and knowledge in high-pressure die-casting environments, where the lack of integrated information hampers effective troubleshooting and compliance with emerging transparency requirements. We developed a comprehensive semantic model that integrates distributed data sources and expert knowledge into a unified knowledge graph framework, explicitly connecting manufacturing processes, failures, metrics, and countermeasures through formalized semantic relationships. Our implementation shows how the resulting architecture successfully transforms traditionally siloed industrial data into an interconnected knowledge representation that distinguishes between specified expert knowledge and actual operational data, enabling systematic reasoning about cause-effect relationships throughout the manufacturing process. The approach provides significant value by enhancing manufacturing transparency and decision support while aligning with Industry 5.0 principles and emerging regulatory frameworks for explainable industrial systems, ultimately supporting more sustainable and efficient manufacturing processes.