Metal Additive Manufacturing Conference - MAMC 2026

Session

Digitalisation & Artificial Intelligence

12 Nov 2026, 11:00

Arcotel Wimberger

34. Data Driven Machine Learning Approach for 3D Flow Prediction in Hydrogen Burners

Luca Juris (Chair for Digital Additive Production DAP - RWTH Aachen)

12/11/2026, 11:00

Digitalisation, Artificial Intelligence & Simulation

Oral Presentation

Hydrogen burner design requires accurate prediction of three-dimensional flow and mixing fields, since local hydrogen distribution strongly influences flame stability, flashback risk, and emission behavior. Additive manufacturing enables the realization of complex burner concepts with tailored injection layouts, small-scale fuel inlets, and compact mixing geometries that would be difficult or...

30. Spatiotemporal Sensor Fusion for AI-Based Quality Assurance in Arc-Based Manufacturing

Georgij Safronov (Technische Universität München), Mr Heiko Theisinger (BMW Group)

12/11/2026, 11:20

Digitalisation, Artificial Intelligence & Simulation

Oral Presentation

Artificial intelligence (AI) can enable adaptive, in-process quality assurance in arc-based manufacturing and wire-arc directed energy deposition (wire-arc DED). Industrial deployment lags behind. Machine environments are heterogeneous, sensor systems are proprietary, and transferable concepts for feeding process data into AI-based monitoring are missing. AI already works for isolated welding...

17. Influence of Scan Vector Metadata Channel Encodings on Optical Tomography Image Prediction in PBF-LB/M

Sven Erb (Chair for Digital Additive Production DAP - RWTH Aachen)

12/11/2026, 11:40

Digitalisation, Artificial Intelligence & Simulation

Oral Presentation

Parts produced by Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) are susceptible to thermally induced defects during the manufacturing process. The thermal history of parts can be assessed through Optical Tomography (OT), an in-situ monitoring technique that captures layer-wise integrated near-infrared emission intensity during the build process. Acquiring reference OT images in...

24. Process Fundamentals for Potential AI Driven Geometry Adaptive Process Control in Laser Powder Bed Fusion

Luke Schüller (Fraunhofer-Institut für Lasertechnik ILT)

12/11/2026, 12:00

Digitalisation, Artificial Intelligence & Simulation

Oral Presentation

Laser Powder Bed Fusion (PBF-LB/M) enables the tool-free manufacturing of complex metallic components. However, the often-emphasized geometric freedom of PBF-LB/M is limited by the insufficient transferability of process parameters developed on simple reference geometries to real components with locally varying geometric conditions. In particular, thin-walled sections, overhangs and filigree...