To be able to monitor a wide range of process emissions, it is important to select the right sensor for the given process and the targeted emission type. This is possible with our broad and fundamental knowledge of available sensor types and data acquisition cards.

Moreover, as our aim is not only to monitor processes, but also to control them, our team conducts research into closed-control feedback loops. Depending on the requirements, the team uses standard solutions such as a control-/monitoring computer setup. If a high-speed response loop is required, the team employs FPGA-solutions with software developed by the team itself.

Figure 1: Multi-material LPBF monitored by optical and acoustic sensors [1]

[1] Pandiyan et al., Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process, Virtual and Physical Prototyping 19 (2024) e2356080. https://doi.org/10.1080/17452759.2024.2356080.

Our research employs cutting-edge in-situ characterization techniques at synchrotron and neutron facilities to explore melt pool dynamics, phase transformations, and microstructure evolution in materials processed through additive manufacturing (AM) methods such as LPBF and DED. We have developed specialized experimental setups that replicate laser-based AM conditions on a smaller scale, making them compatible with synchrotron and neutron beamlines. Additionally, these beamline experiments are frequently integrated with other in-situ monitoring techniques, including optical emission, acoustic emission, and/or high-speed imaging, to provide a comprehensive understanding of laser-based manufacturing processes.

Figure 2: Melt pool dynamics in LPBF revealed by in-situ synchrotron X-ray imaging with tracing particles [1]

[1] Yang et al., Experimental quantification of inward    Marangoni convection and its impact on keyhole threshold in laser powder bed fusion of stainless steel, Additive Manufacturing 84 (2024) 104092. https://doi.org/10.1016/j.addma.2024.104092.