Scaling ML Analytics with Knowledge Graphs: A Bosch Welding Case

Automated welding is heavily used in automotive industry to produce car bodies by connecting metal parts with welding spots. Modern welding solutions and manufacturing environments produce high volume of heterogeneous data. Analytics of these data with machine learning (ML) can help to ensure high quality of welding operations. However, due to heterogeneity of data and application scenarios, scaling such ML-based analytics is challenging. We address this challenge by relying on knowledge graphs (KG) that not only conveniently allow to integrate welding data, but also to serve as the bases for layering ML-based analytical applications, thus enabling quality monitoring of welding operations. In this work we focus on construction of a KG for welding that is tailored towards further use for ML applications. Furthermore, we demonstrate how selected ML analytical tasks are supported by this KG.