Master's student (f/m/x) - Semantic Modeling of Robotic Additive Manufacturing Digital Process Chain

What to expect

The DLR Institute of Vehicle Concepts researches novel vehicle structures. Certain structural parts for these vehicles are manufactured with Robotic Screw Extrusion Additive Manufacturing (SEAM). Robotic SEAM is a novel 3D manufacturing process facilitated by Yizumi Space A hybrid manufacturing cell.
The digital process chain to produce components with Robotic SEAM spans from CAD geometry, through slicing, digital twin simulation, the generated production program, down to the production data captured during the build. Each stage produces heterogeneous information that is rarely connected in a machine-interpretable way. This thesis is positioned as a foundational building block toward a cognitive twin for robotic AM in order to enhance interoperability.

Your tasks

• Familiarizing with the Robotic SEAM setup, digital twin, existing ontology, and the digital process chain
• Leveraging ontologies for semantic representation of data sources, interoperability, data integration and knowledge sharing
• Review modeling approaches for temporal knowledge in knowledge graphs and select/adapt a suitable approach 
• Working with and integrating Digital Twin 
• Design and implement a knowledge-graph-grounded RAG interface
• Validate the framework and traceability

Your profile

• Masters Student majoring in Data Science, Informatics, Mechatronics, Computer Science or relevant
• Proficiency in Python programming language, data processing, RAG and interested in LLM
• Interest in robotics, semantic technology and AI
• Advantageous to have prior knowledge of Additive Manufacturing, and familiarity with RoboDK or willingness to learn
• Independent, structured and responsible way of working