  Reliably manufacturing defect free products is still an open challenge for
Laser Powder Bed Fusion processes. Particularly, pores that occur frequently
have a negative impact on mechanical properties like fatigue performance.
Therefore, an accurate localisation of pores is mandatory for quality
assurance, but requires time-consuming post-processing steps like computer
tomography scans. Although existing solutions using in-situ monitoring data can
detect pore occurrence within a layer, they are limited in their localisation
precision. Therefore, we propose a pore localisation approach that estimates
their position within a single layer using a Gaussian kernel density
estimation. This allows segmentation models to learn the correlation between
in-situ monitoring data and the derived probability distribution of pore
occurrence. Within our experiments, we compare the prediction performance of
different segmentation models depending on machine parameter configuration and
geometry features. From our results, we conclude that our approach allows a
precise localisation of pores that requires minimal data preprocessing. Our
research extends the literature by providing a foundation for more precise pore
detection systems.
