Carlos Jordan’s research while affiliated with Ghent University and other places

In order to develop smart laser powder bed fusion (LPBF) devices that autonomously identify a defect and remove it during the process for first-time-right and zero-defect parts, it is important to develop reliable on-machine defect measuring capabilities. As defects in LPBF parts often occur below the layer that is being processed, capturing the information of a printing layer may not give information about physical phenomena that are occurring below this layer. Therefore, to predict volumetric features such as porosity only by looking at the layer being processed, the correlation between process signatures identified in-process and defects measured via post-process inspection methods (for example X-ray computed tomography) needs to be conducted. Hence, in situ monitoring and post-process metrology form a basis to better understand the fundamental physics involved in an LPBF process and ultimately to determine its stability. By utilizing high-speed imaging, various process signatures are produced during single-track formation of 316L stainless steel with various combinations of laser power and scan speed. In this study, we evaluate whether these signatures can be used to detect the onset of potential defects. To identify process signatures, image segmentation and feature detection are applied to the monitoring data along the line scans. The process signatures determined in the current study are mainly related to the features like the process zone length-to-width ratio, process zone area, process zone mean intensity, spatter speed and number of spatters. It is shown that the scan speed has a significant impact on the process stability and spatter formation during single-track fusion. Simulations with similar processing conditions were also performed to predict melt pool geometric features. Post-process characterization techniques such as X-ray computed tomography and 2.5-D surface topography measurement were carried out for a quality check of the line track. An attempt was made to correlate physics-based features with process-related defects and a correlation between the number of keyhole porosities, and the number of spatters was observed for the line tracks. Graphical abstract

In-situ monitoring and post-process metrology form a basis to better understand the fundamental physics involved in the Laser Powder Bed Fusion (LPBF) process and ultimately to determine its stability. By utilizing high-speed imaging, various process signatures are produced during single track formation of 316L stainless steel with various combinations of laser power and scan speed. In this study, we evaluate whether these signatures can be used to detect the onset of potential defects. To identify process signatures, image segmentation and feature detection are applied to the monitoring data along the line scans. The process signatures determined in the current study are mainly related to the features like the process zone length-to-width ratio, process zone area, process zone mean intensity, spatter speed and number of spatters. It is shown that the scan speed has a significant impact on the process stability and spatter formation during single track fusion. Simulations with similar processing conditions were also performed to predict melt pool geometric features. Post-process characterization techniques such as X-ray computed tomography and 2.5-D surface topography measurement were carried out for a quality check of the line track. An attempt was made to correlate physics-based features with process-related defects and a correlation between the number of keyhole porosities and the number of spatters was observed for the line tracks.